{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T09:01:32.717896Z", "iopub.status.busy": "2025-03-01T09:01:32.717574Z", "iopub.status.idle": "2025-03-01T09:01:32.723994Z", "shell.execute_reply": "2025-03-01T09:01:32.723092Z", "shell.execute_reply.started": "2025-03-01T09:01:32.717866Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import re\n", "import tensorflow as tf\n", "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Input, Concatenate, Attention\n", "from tensorflow.keras.models import Model, load_model\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "from tensorflow.keras.preprocessing.text import Tokenizer\n", "from sklearn.model_selection import train_test_split\n", "import nltk\n", "from nltk.translate.bleu_score import corpus_bleu\n", "import pickle\n", "\n", "nltk.download('punkt')\n", "\n", "# Configuration\n", "SEQUENCE_LENGTH = 50\n", "EMBEDDING_DIM = 128\n", "HIDDEN_UNITS = 128\n", "EPOCHS = 3\n", "BATCH_SIZE = 20\n", "BEAM_WIDTH = 3 # Beam width for decoding" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T08:52:01.367291Z", "iopub.status.busy": "2025-03-01T08:52:01.366855Z", "iopub.status.idle": "2025-03-01T08:52:02.941469Z", "shell.execute_reply": "2025-03-01T08:52:02.940734Z", "shell.execute_reply.started": "2025-03-01T08:52:01.367264Z" }, "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":3: DtypeWarning: Columns (2,3,4,5,6,7,8,9,10,11) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv('/kaggle/input/nlp-22/ensw.csv', encoding='ISO-8859-1')\n" ] } ], "source": [ "# Load and preprocess data with start/end tokens\n", "def load_data():\n", " df = pd.read_csv('/kaggle/input/nlp-22/ensw.csv', encoding='ISO-8859-1')\n", " \n", " def clean_text(text):\n", " if not isinstance(text, str): \n", " return \"\"\n", " # Allow letters, apostrophes, and keep case (remove lower())\n", " text = re.sub(r'[^a-zA-Zāēīōūĉĝĥĵŝŭŵŷẑñçàéèêëìíîïòóôùúûüÿśźąćęłńóśźżàâçéèêëîïôùûüÿœæß¿¡\\s\\']', '', text)\n", " return text.strip() # Keep case\n", " \n", " df = df.iloc[:, :2]\n", " df['Swahili'] = df['English sentence'].apply(clean_text)\n", " df['English'] = df['Swahili Translation'].apply(clean_text)\n", " # Add start/end tokens after cleaning\n", " df['English'] = ' ' + df['English'] + ' '\n", " df = df.dropna().drop_duplicates(subset=['Swahili', 'English'])\n", " return df\n", "\n", "# Load data\n", "df = load_data()\n", "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "trusted": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T08:52:04.067745Z", "iopub.status.busy": "2025-03-01T08:52:04.067427Z", "iopub.status.idle": "2025-03-01T08:52:10.266945Z", "shell.execute_reply": "2025-03-01T08:52:10.266238Z", "shell.execute_reply.started": "2025-03-01T08:52:04.067720Z" }, "trusted": true }, "outputs": [], "source": [ "# Tokenization with proper filters\n", "sw_tokenizer = Tokenizer(filters='')\n", "en_tokenizer = Tokenizer(filters='', lower=False) # Preserve case and special tokens\n", "sw_tokenizer.fit_on_texts(train_df['Swahili'])\n", "en_tokenizer.fit_on_texts(train_df['English'])\n", "\n", "sw_vocab_size = len(sw_tokenizer.word_index) + 1\n", "en_vocab_size = len(en_tokenizer.word_index) + 1\n", "\n", "# Convert texts to sequences with padding\n", "sw_train_seq = sw_tokenizer.texts_to_sequences(train_df['Swahili'])\n", "en_train_seq = en_tokenizer.texts_to_sequences(train_df['English'])\n", "\n", "sw_train_padded = pad_sequences(sw_train_seq, maxlen=SEQUENCE_LENGTH, padding='post')\n", "en_train_padded = pad_sequences(en_train_seq, maxlen=SEQUENCE_LENGTH + 1, padding='post')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T08:52:10.268250Z", "iopub.status.busy": "2025-03-01T08:52:10.268003Z", "iopub.status.idle": "2025-03-01T08:52:10.274368Z", "shell.execute_reply": "2025-03-01T08:52:10.273478Z", "shell.execute_reply.started": "2025-03-01T08:52:10.268229Z" }, "trusted": true }, "outputs": [], "source": [ "# Model with attention\n", "def build_model_with_attention():\n", " # Encoder\n", " encoder_inputs = Input(shape=(SEQUENCE_LENGTH,))\n", " encoder_emb = Embedding(sw_vocab_size, EMBEDDING_DIM)(encoder_inputs)\n", " encoder_lstm = Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True, return_state=True))\n", " encoder_outputs, fh, fc, bh, bc = encoder_lstm(encoder_emb)\n", " state_h = Concatenate()([fh, bh])\n", " state_c = Concatenate()([fc, bc])\n", " \n", " # Decoder\n", " decoder_inputs = Input(shape=(SEQUENCE_LENGTH,))\n", " decoder_emb = Embedding(en_vocab_size, EMBEDDING_DIM)(decoder_inputs)\n", " decoder_lstm = LSTM(HIDDEN_UNITS * 2, return_sequences=True, return_state=True)\n", " decoder_outputs, _, _ = decoder_lstm(decoder_emb, initial_state=[state_h, state_c])\n", " \n", " # Attention\n", " attention = Attention()([decoder_outputs, encoder_outputs])\n", " decoder_combined = Concatenate(axis=-1)([decoder_outputs, attention])\n", " decoder_dense = Dense(en_vocab_size, activation='softmax')\n", " output = decoder_dense(decoder_combined)\n", " \n", " return Model([encoder_inputs, decoder_inputs], output)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T09:01:35.788738Z", "iopub.status.busy": "2025-03-01T09:01:35.788454Z", "iopub.status.idle": "2025-03-01T10:59:07.638833Z", "shell.execute_reply": "2025-03-01T10:59:07.638123Z", "shell.execute_reply.started": "2025-03-01T09:01:35.788716Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/3\n", "\u001b[1m6428/6428\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2353s\u001b[0m 366ms/step - accuracy: 0.8602 - loss: 1.2808 - val_accuracy: 0.8952 - val_loss: 0.8033\n", "Epoch 2/3\n", "\u001b[1m6428/6428\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2349s\u001b[0m 366ms/step - accuracy: 0.9042 - loss: 0.6804 - val_accuracy: 0.9138 - val_loss: 0.6489\n", "Epoch 3/3\n", "\u001b[1m6428/6428\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2347s\u001b[0m 365ms/step - accuracy: 0.9261 - loss: 0.4528 - val_accuracy: 0.9187 - val_loss: 0.6090\n" ] } ], "source": [ "# Build and train model\n", "model = build_model_with_attention()\n", "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", "model.fit([sw_train_padded, en_train_padded[:, :-1]], en_train_padded[:, 1:], \n", " epochs=EPOCHS,validation_split=0.2, batch_size=BATCH_SIZE)\n", "\n", "# Save model and tokenizers\n", "model.save('/kaggle/working/translation_model200.h5')\n", "with open('/kaggle/working/tokenizers.pkl', 'wb') as f:\n", " pickle.dump((sw_tokenizer, en_tokenizer), f)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T10:59:07.640221Z", "iopub.status.busy": "2025-03-01T10:59:07.639896Z", "iopub.status.idle": "2025-03-01T10:59:07.668113Z", "shell.execute_reply": "2025-03-01T10:59:07.667327Z", "shell.execute_reply.started": "2025-03-01T10:59:07.640195Z" }, "trusted": true }, "outputs": [ { "data": { "text/html": [ "
Model: \"functional_2\"\n",
       "
\n" ], "text/plain": [ "\u001b[1mModel: \"functional_2\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)              ┃ Output Shape           ┃        Param # ┃ Connected to           ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_4             │ (None, 50)             │              0 │ -                      │\n",
       "│ (InputLayer)              │                        │                │                        │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ embedding_4 (Embedding)   │ (None, 50, 128)        │      6,317,568 │ input_layer_4[0][0]    │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ input_layer_5             │ (None, 50)             │              0 │ -                      │\n",
       "│ (InputLayer)              │                        │                │                        │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ bidirectional_2           │ [(None, 50, 256),      │        263,168 │ embedding_4[0][0]      │\n",
       "│ (Bidirectional)           │ (None, 128), (None,    │                │                        │\n",
       "│                           │ 128), (None, 128),     │                │                        │\n",
       "│                           │ (None, 128)]           │                │                        │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ embedding_5 (Embedding)   │ (None, 50, 128)        │     12,117,632 │ input_layer_5[0][0]    │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ concatenate_6             │ (None, 256)            │              0 │ bidirectional_2[0][1], │\n",
       "│ (Concatenate)             │                        │                │ bidirectional_2[0][3]  │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ concatenate_7             │ (None, 256)            │              0 │ bidirectional_2[0][2], │\n",
       "│ (Concatenate)             │                        │                │ bidirectional_2[0][4]  │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ lstm_5 (LSTM)             │ [(None, 50, 256),      │        394,240 │ embedding_5[0][0],     │\n",
       "│                           │ (None, 256), (None,    │                │ concatenate_6[0][0],   │\n",
       "│                           │ 256)]                  │                │ concatenate_7[0][0]    │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ attention_2 (Attention)   │ (None, 50, 256)        │              0 │ lstm_5[0][0],          │\n",
       "│                           │                        │                │ bidirectional_2[0][0]  │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ concatenate_8             │ (None, 50, 512)        │              0 │ lstm_5[0][0],          │\n",
       "│ (Concatenate)             │                        │                │ attention_2[0][0]      │\n",
       "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
       "│ dense_2 (Dense)           │ (None, 50, 94669)      │     48,565,197 │ concatenate_8[0][0]    │\n",
       "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
       "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,317,568\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ input_layer_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ bidirectional_2 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m263,168\u001b[0m │ embedding_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, │ │ │\n", "│ │ \u001b[38;5;34m128\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m), │ │ │\n", "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)] │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ embedding_5 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m12,117,632\u001b[0m │ input_layer_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ concatenate_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bidirectional_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m], │\n", "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ bidirectional_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m3\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ concatenate_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ bidirectional_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m2\u001b[0m], │\n", "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ bidirectional_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m4\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m394,240\u001b[0m │ embedding_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, │ │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ \u001b[38;5;34m256\u001b[0m)] │ │ concatenate_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ attention_2 (\u001b[38;5;33mAttention\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ lstm_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ bidirectional_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ concatenate_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ lstm_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ attention_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m94669\u001b[0m) │ \u001b[38;5;34m48,565,197\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m135,315,612\u001b[0m (516.19 MB)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.summary()\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T11:10:35.028564Z", "iopub.status.busy": "2025-03-01T11:10:35.028252Z", "iopub.status.idle": "2025-03-01T11:10:44.720245Z", "shell.execute_reply": "2025-03-01T11:10:44.719484Z", "shell.execute_reply.started": "2025-03-01T11:10:35.028540Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Translation: \n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "import pickle\n", "import re\n", "\n", "# Load the trained model\n", "model = load_model('/kaggle/working/translation_model200.h5')\n", "\n", "# Load the tokenizers (Swahili and English)\n", "with open('/kaggle/working/tokenizers.pkl', 'rb') as f:\n", " sw_tokenizer, en_tokenizer = pickle.load(f)\n", "\n", "# Define the text cleaning function\n", "def clean_text(text):\n", " \"\"\"\n", " Clean the input text by removing unwanted characters and retaining specific allowed characters.\n", " \n", " Args:\n", " text (str): Input text to clean.\n", " \n", " Returns:\n", " str: Cleaned text.\n", " \"\"\"\n", " if not isinstance(text, str):\n", " return \"\"\n", " # Keep letters, specific accented characters, spaces, and apostrophes\n", " text = re.sub(r'[^a-zA-Zāēīōūĉĝĥĵŝŭŵŷẑñçàéèêëìíîïòóôùúûüÿśźąćęłńóśźżàâçéèêëîïôùûüÿœæß¿¡\\s\\']', '', text)\n", " return text.strip()\n", "\n", "# Define the encoder inputs\n", "encoder_inputs = model.get_layer('input_layer_4').output # Corrected encoder input layer\n", "\n", "# Define the encoder outputs\n", "encoder_outputs = model.get_layer('bidirectional_2').output[0] # Corrected bidirectional layer\n", "state_h = model.get_layer('concatenate_6').output # Corrected state_h layer\n", "state_c = model.get_layer('concatenate_7').output # Corrected state_c layer\n", "\n", "# Create the encoder model\n", "encoder_model = tf.keras.Model(inputs=encoder_inputs, outputs=[encoder_outputs, state_h, state_c])\n", "\n", "# Define the decoder model\n", "decoder_input_token = tf.keras.layers.Input(shape=(1,), name='decoder_input_token')\n", "encoder_outputs_input = tf.keras.layers.Input(shape=(50, 256), name='encoder_outputs_input') # Shape matches bidirectional_2 output\n", "decoder_state_input_h = tf.keras.layers.Input(shape=(256,), name='decoder_state_input_h')\n", "decoder_state_input_c = tf.keras.layers.Input(shape=(256,), name='decoder_state_input_c')\n", "\n", "# Use the correct embedding layer\n", "embedding_5 = model.get_layer('embedding_5') # Corrected embedding layer\n", "decoder_emb = embedding_5(decoder_input_token) # Output: (None, 1, 128)\n", "\n", "# LSTM layer with initial states\n", "lstm_5 = model.get_layer('lstm_5') # Corrected LSTM layer\n", "decoder_outputs, state_h, state_c = lstm_5(decoder_emb, initial_state=[decoder_state_input_h, decoder_state_input_c])\n", "\n", "# Attention mechanism\n", "attention_2 = model.get_layer('attention_2') # Corrected attention layer\n", "attention_out = attention_2([decoder_outputs, encoder_outputs_input]) # Output: (None, 1, 256)\n", "\n", "# Concatenate LSTM output and attention output\n", "concatenate_8 = model.get_layer('concatenate_8') # Corrected concatenate layer\n", "decoder_combined = concatenate_8([decoder_outputs, attention_out]) # Output: (None, 1, 512)\n", "\n", "# Dense layer to produce logits\n", "dense_2 = model.get_layer('dense_2') # Corrected dense layer\n", "output = dense_2(decoder_combined) # Output: (None, 1, 94669)\n", "\n", "# Squeeze the sequence dimension since we're processing one token\n", "output = tf.keras.layers.Lambda(lambda x: tf.squeeze(x, axis=1))(output) # Output: (None, 94669)\n", "\n", "# Define the decoder model\n", "decoder_model = tf.keras.Model(\n", " inputs=[decoder_input_token, encoder_outputs_input, decoder_state_input_h, decoder_state_input_c],\n", " outputs=[output, state_h, state_c]\n", ")\n", "\n", "# Beam search decoding function\n", "def beam_search_decode(sw_sentence, beam_width=3, max_length=50):\n", " \"\"\"\n", " Perform beam search decoding to generate a translation from a Swahili sentence.\n", " \n", " Args:\n", " sw_sentence (str): Input Swahili sentence.\n", " beam_width (int): Number of candidate sequences to maintain during beam search.\n", " max_length (int): Maximum length of the generated sequence.\n", " \n", " Returns:\n", " str: Translated English sentence.\n", " \"\"\"\n", " # Preprocess and tokenize the input sentence\n", " cleaned_sentence = clean_text(sw_sentence)\n", " input_seq = sw_tokenizer.texts_to_sequences([cleaned_sentence])\n", " input_padded = pad_sequences(input_seq, maxlen=50, padding='post')\n", "\n", " # Run the encoder\n", " encoder_outputs, state_h, state_c = encoder_model.predict(input_padded, verbose=0)\n", "\n", " # Define start and end tokens\n", " start_token = en_tokenizer.word_index['']\n", " end_token = en_tokenizer.word_index['']\n", "\n", " # Initialize beam with the start token\n", " initial_candidate = {\n", " 'sequence': [start_token],\n", " 'score': 0.0,\n", " 'state_h': state_h,\n", " 'state_c': state_c\n", " }\n", " beam = [initial_candidate]\n", " completed_sequences = []\n", "\n", " # Beam search loop\n", " for _ in range(max_length):\n", " all_candidates = []\n", " for candidate in beam:\n", " if candidate['sequence'][-1] == end_token:\n", " completed_sequences.append(candidate)\n", " continue\n", "\n", " # Prepare the current token input\n", " current_token = np.array([[candidate['sequence'][-1]]])\n", "\n", " # Predict next token logits\n", " output, new_state_h, new_state_c = decoder_model.predict(\n", " [current_token, encoder_outputs, candidate['state_h'], candidate['state_c']],\n", " verbose=0\n", " )\n", "\n", " # Compute log probabilities from logits\n", " log_probs = tf.nn.log_softmax(output).numpy()[0] # Shape: (94669,)\n", "\n", " # Get top beam_width candidates\n", " top_indices = np.argsort(log_probs)[-beam_width:]\n", " for idx in top_indices:\n", " new_sequence = candidate['sequence'] + [idx]\n", " new_score = candidate['score'] + log_probs[idx]\n", " new_candidate = {\n", " 'sequence': new_sequence,\n", " 'score': new_score,\n", " 'state_h': new_state_h,\n", " 'state_c': new_state_c\n", " }\n", " all_candidates.append(new_candidate)\n", "\n", " # Sort and select top beam_width candidates\n", " all_candidates = sorted(all_candidates, key=lambda x: x['score'], reverse=True)\n", " beam = all_candidates[:beam_width]\n", "\n", " # Break if all candidates have ended\n", " if all(c['sequence'][-1] == end_token for c in beam):\n", " break\n", "\n", " # Include any remaining candidates in completed sequences\n", " completed_sequences.extend(beam)\n", "\n", " # Select the best sequence\n", " best_candidate = max(completed_sequences, key=lambda x: x['score'])\n", " sequence = best_candidate['sequence'][1:-1] # Exclude start and end tokens\n", "\n", " # Convert token indices to text\n", " translation = en_tokenizer.sequences_to_texts([sequence])[0]\n", " return translation\n", "\n", "# Example usage\n", "en_sentence = \"In 1983, two separate research groups led by Robert Gallo and Luc Montagnier declared that a novel retrovirus may have been infecting people with AIDS, and published their findings in the same issue of the.\"\n", "translation = beam_search_decode(sw_sentence, beam_width=3, max_length=50)\n", "print(\"Translation:\", translation)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T11:12:51.475931Z", "iopub.status.busy": "2025-03-01T11:12:51.475590Z", "iopub.status.idle": "2025-03-01T11:12:53.307575Z", "shell.execute_reply": "2025-03-01T11:12:53.306532Z", "shell.execute_reply.started": "2025-03-01T11:12:51.475903Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Translation: wombs a gift a wouldn a wouldn a alone\n" ] } ], "source": [ "def beam_search_decode(en_sentence, beam_width=3, max_length=50):\n", " \"\"\"\n", " Perform beam search decoding to generate a Swahili translation from an English sentence.\n", " \n", " Args:\n", " en_sentence (str): Input English sentence.\n", " beam_width (int): Number of candidate sequences to maintain during beam search.\n", " max_length (int): Maximum length of the generated sequence.\n", " \n", " Returns:\n", " str: Translated Swahili sentence.\n", " \"\"\"\n", " # Preprocess and tokenize the input English sentence\n", " cleaned_sentence = clean_text(en_sentence)\n", " input_seq = en_tokenizer.texts_to_sequences([cleaned_sentence]) \n", " input_padded = pad_sequences(input_seq, maxlen=50, padding='post')\n", "\n", " # Run the encoder\n", " encoder_outputs, state_h, state_c = encoder_model.predict(input_padded, verbose=0)\n", "\n", " # Define start and end tokens\n", " start_token = sw_tokenizer.word_index.get('', 1)\n", " end_token = sw_tokenizer.word_index.get('', 2)\n", "\n", " # Initialize beam\n", " initial_candidate = {\n", " 'sequence': [start_token],\n", " 'score': 0.0,\n", " 'state_h': state_h,\n", " 'state_c': state_c\n", " }\n", " beam = [initial_candidate]\n", " completed_sequences = []\n", "\n", " # Beam search loop\n", " for _ in range(max_length):\n", " all_candidates = []\n", " for candidate in beam:\n", " if candidate['sequence'][-1] == end_token:\n", " completed_sequences.append(candidate)\n", " continue\n", "\n", " # Stop if last 5 words are identical (avoid infinite loops)\n", " if len(candidate['sequence']) > 5 and len(set(candidate['sequence'][-5:])) == 1:\n", " completed_sequences.append(candidate)\n", " continue\n", "\n", " # Prepare the current token input\n", " current_token = np.array([[candidate['sequence'][-1]]])\n", "\n", " # Predict next token logits\n", " output, new_state_h, new_state_c = decoder_model.predict(\n", " [current_token, encoder_outputs, candidate['state_h'], candidate['state_c']],\n", " verbose=0\n", " )\n", "\n", " # Apply temperature scaling\n", " temperature = 0.7\n", " log_probs = tf.nn.log_softmax(output / temperature).numpy()[0] \n", "\n", " # Get top beam_width candidates\n", " top_indices = np.argsort(log_probs)[-beam_width:]\n", " for idx in top_indices:\n", " # Penalize repetition\n", " word_penalty = 0.1 * len(set(candidate['sequence']))\n", " new_score = candidate['score'] + log_probs[idx] - word_penalty\n", "\n", " new_candidate = {\n", " 'sequence': candidate['sequence'] + [idx],\n", " 'score': new_score,\n", " 'state_h': new_state_h,\n", " 'state_c': new_state_c\n", " }\n", " all_candidates.append(new_candidate)\n", "\n", " # Sort and select top beam_width candidates\n", " all_candidates = sorted(all_candidates, key=lambda x: x['score'], reverse=True)\n", " beam = all_candidates[:beam_width]\n", "\n", " # Stop early if too many candidates reached \n", " if sum(seq['sequence'].count(end_token) for seq in completed_sequences) > beam_width:\n", " break\n", "\n", " # Include any remaining candidates in completed sequences\n", " completed_sequences.extend(beam)\n", "\n", " # Select the best sequence\n", " best_candidate = max(completed_sequences, key=lambda x: x['score'])\n", " sequence = best_candidate['sequence'][1:-1] # Exclude start and end tokens\n", "\n", " # Convert token indices to text using Swahili tokenizer\n", " translation = sw_tokenizer.sequences_to_texts([sequence])[0]\n", " return translation\n", "\n", "# Example usage\n", "en_sentence = \"The government has announced new policies to improve education in rural areas.\"\n", "translation = beam_search_decode(en_sentence, beam_width=3, max_length=50)\n", "print(\"Translation:\", translation)\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T11:23:29.591785Z", "iopub.status.busy": "2025-03-01T11:23:29.591435Z", "iopub.status.idle": "2025-03-01T11:23:31.785787Z", "shell.execute_reply": "2025-03-01T11:23:31.785119Z", "shell.execute_reply.started": "2025-03-01T11:23:29.591756Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "Translation: Serikali ilipata sera mpya kuboresha elimu ya kuboresha elimu katika maeneo ya vijijini\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import load_model, Model\n", "from tensorflow.keras.layers import Input, Lambda\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "import pickle\n", "import re\n", "\n", "# -----------------------------\n", "# Configuration (must match training)\n", "SEQUENCE_LENGTH = 50\n", "EMBEDDING_DIM = 128\n", "HIDDEN_UNITS = 128 # Since states are concatenated from a bidirectional LSTM, state size = HIDDEN_UNITS*2 (256)\n", "# -----------------------------\n", "\n", "# Load the trained model and tokenizers\n", "model = load_model('/kaggle/working/translation_model200.h5')\n", "with open('/kaggle/working/tokenizers.pkl', 'rb') as f:\n", " sw_tokenizer, en_tokenizer = pickle.load(f)\n", "\n", "def clean_text(text):\n", " \"\"\"Clean input text by removing unwanted characters.\"\"\"\n", " if not isinstance(text, str):\n", " return \"\"\n", " text = re.sub(r\"[^a-zA-Zāēīōūĉĝĥĵŝŭŵŷẑñçàéèêëìíîïòóôùúûüÿśźąćęłńóśźżàâçéèêëîïôùûüÿœæß¿¡\\s']\", \"\", text)\n", " return text.strip()\n", "\n", "# -----------------------------\n", "# Build the Encoder Inference Model\n", "# Instead of using model.get_layer(\"input_layer_4\").input (which returns a list), we use model.input[0].\n", "encoder_input = model.input[0] # Use the first input tensor\n", "encoder_emb = model.get_layer(\"embedding_4\")(encoder_input)\n", "# Bidirectional LSTM layer \"bidirectional_2\" returns a tuple; its first element is the time-distributed output.\n", "encoder_lstm_outputs = model.get_layer(\"bidirectional_2\")(encoder_emb)\n", "encoder_outputs = encoder_lstm_outputs[0] # Time-distributed outputs\n", "state_h = model.get_layer(\"concatenate_6\").output # Concatenated hidden states\n", "state_c = model.get_layer(\"concatenate_7\").output # Concatenated cell states\n", "\n", "encoder_model = Model(inputs=encoder_input, outputs=[encoder_outputs, state_h, state_c])\n", "\n", "# -----------------------------\n", "# Build the Decoder Inference Model\n", "# Create new Input layers for one-token input and previous states.\n", "decoder_input_token = Input(shape=(1,), name=\"decoder_input_token\")\n", "state_h_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_h\") # Expected shape: (256,)\n", "state_c_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_c\")\n", "encoder_outputs_input = Input(shape=(SEQUENCE_LENGTH, HIDDEN_UNITS * 2), name=\"encoder_outputs_input\")\n", "\n", "# Use the decoder embedding layer (\"embedding_5\")\n", "decoder_emb = model.get_layer(\"embedding_5\")(decoder_input_token) # Output shape: (None, 1, EMBEDDING_DIM)\n", "\n", "# Decoder LSTM (\"lstm_5\"): run one time step with initial states\n", "decoder_lstm_outputs, dec_state_h, dec_state_c = model.get_layer(\"lstm_5\")(\n", " decoder_emb, initial_state=[state_h_input, state_c_input]\n", ")\n", "\n", "# Attention layer (\"attention_2\") over encoder outputs\n", "attention_out = model.get_layer(\"attention_2\")([decoder_lstm_outputs, encoder_outputs_input])\n", "\n", "# Concatenate LSTM output and attention output (\"concatenate_8\")\n", "decoder_combined = model.get_layer(\"concatenate_8\")([decoder_lstm_outputs, attention_out])\n", "\n", "# Dense softmax layer (\"dense_2\") to produce token probabilities\n", "decoder_dense_outputs = model.get_layer(\"dense_2\")(decoder_combined)\n", "# Squeeze the time dimension (since we predict one token at a time)\n", "decoder_outputs_final = Lambda(lambda x: tf.squeeze(x, axis=1))(decoder_dense_outputs)\n", "\n", "decoder_model = Model(\n", " inputs=[decoder_input_token, encoder_outputs_input, state_h_input, state_c_input],\n", " outputs=[decoder_outputs_final, dec_state_h, dec_state_c]\n", ")\n", "\n", "# -----------------------------\n", "# Greedy Decoding Function (English -> Swahili)\n", "def greedy_decode(input_sentence, max_length=50):\n", " \"\"\"\n", " Translate an English sentence to Swahili using greedy decoding.\n", " \n", " Args:\n", " input_sentence (str): Input English sentence.\n", " max_length (int): Maximum length of the generated translation.\n", " \n", " Returns:\n", " str: Translated Swahili sentence.\n", " \"\"\"\n", " # Preprocess and tokenize the input using the encoder's tokenizer (sw_tokenizer)\n", " # (Recall: during training, English source sentences were tokenized with sw_tokenizer)\n", " cleaned = clean_text(input_sentence)\n", " input_seq = sw_tokenizer.texts_to_sequences([cleaned])\n", " input_seq = pad_sequences(input_seq, maxlen=SEQUENCE_LENGTH, padding=\"post\")\n", " \n", " # Run the encoder to obtain encoder outputs and initial states\n", " encoder_out, state_h, state_c = encoder_model.predict(input_seq)\n", " \n", " # Get start and end token indices from the target tokenizer (en_tokenizer)\n", " # (Recall: during training, Swahili translations were tokenized with en_tokenizer and wrapped with and )\n", " start_token = en_tokenizer.word_index[\"\"]\n", " end_token = en_tokenizer.word_index[\"\"]\n", " \n", " # Initialize the target sequence with the token\n", " target_seq = np.array([[start_token]])\n", " output_tokens = []\n", " \n", " for _ in range(max_length):\n", " # Predict the next token using the decoder inference model\n", " output_probs, state_h, state_c = decoder_model.predict(\n", " [target_seq, encoder_out, state_h, state_c]\n", " )\n", " # Greedily choose the token with the highest probability\n", " predicted_token = np.argmax(output_probs, axis=-1)[0]\n", " if predicted_token == end_token:\n", " break\n", " output_tokens.append(predicted_token)\n", " # Update the target sequence to the predicted token for the next time step\n", " target_seq = np.array([[predicted_token]])\n", " \n", " # Convert token indices to a string using the target tokenizer (which was fit on Swahili translations)\n", " translated_sentence = en_tokenizer.sequences_to_texts([output_tokens])[0]\n", " return translated_sentence\n", "\n", "# -----------------------------\n", "# Example usage:\n", "input_sentence = \"The government has announced new policies to improve education in rural areas.\"\n", "translation = greedy_decode(input_sentence, max_length=50)\n", "print(\"Translation:\", translation)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T11:31:00.002467Z", "iopub.status.busy": "2025-03-01T11:31:00.001998Z", "iopub.status.idle": "2025-03-01T11:31:19.025632Z", "shell.execute_reply": "2025-03-01T11:31:19.024676Z", "shell.execute_reply.started": "2025-03-01T11:31:00.002332Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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yao katika suala hilo katika suala hilo Sayansi ya jarida la jarida la Knight\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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hakuwa na jina rasmi kwa ugonjwa wa ugonjwa wa wagonjwa ambao walipona kwa muda wa ugonjwa huo ambao ulisababisha ugonjwa huo baada ya kuambukizwa kwa sababu ya ugonjwa wa UKIMWI\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 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kiufundi katika kuvuta sigara na matumizi ya dawa kama vile matibabu ya matibabu kama vile matibabu au ya matibabu\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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hata kabla ya mapinduzi\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 20ms/step\n", "Source: Hermann Brehmer opened the first TB sanatorium in 1859 in Görbersdorf (now Sokołowsko) in Silesia.\n", "Translation: Nilifungua kitabu cha kwanza katika kila sasa katika wakati wa\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m 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Afya ya Ulimwengu na matibabu ya matibabu ya awali ni pamoja na virusi vya korona vya CoV ambavyo ni pamoja na virusi\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m 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kuthibitisha kuthibitisha kwamba na hasara ya asili ya RNA ya RNA\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 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\u001b[1m0s\u001b[0m 21ms/step\n", "Source: IGRAs may increase sensitivity when used in addition to the skin test, but may be less sensitive than the skin test when used alone.\n", "Translation: Inaweza kuongeza kuwa na kipimo cha ngozi ya ngozi lakini inaweza kuwa na mafanikio kuwa na mawazo ya ngozi ya ngozi ya ngozi\n", "\n", "Warning: Number of hypotheses and references do not match. Using only the first 10 references.\n", "BLEU score: 0.39752332339848573\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import load_model, Model\n", "from tensorflow.keras.layers import Input, Lambda\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "import pickle\n", "import re\n", "from nltk.translate.bleu_score import corpus_bleu\n", "\n", "# -----------------------------\n", "# Configuration (must match training)\n", "SEQUENCE_LENGTH = 50\n", "EMBEDDING_DIM = 128\n", "HIDDEN_UNITS = 128 # Decoder LSTM state size will be HIDDEN_UNITS*2 (256)\n", "# -----------------------------\n", "\n", "# Load the trained model and tokenizers\n", "model = load_model('/kaggle/working/translation_model200.h5')\n", "with open('/kaggle/working/tokenizers.pkl', 'rb') as f:\n", " sw_tokenizer, en_tokenizer = pickle.load(f)\n", "\n", "def clean_text(text):\n", " \"\"\"Clean input text by removing unwanted characters.\"\"\"\n", " if not isinstance(text, str):\n", " return \"\"\n", " text = re.sub(r\"[^a-zA-Zāēīōūĉĝĥĵŝŭŵŷẑñçàéèêëìíîïòóôùúûüÿśźąćęłńóśźżàâçéèêëîïôùûüÿœæß¿¡\\s']\", \"\", text)\n", " return text.strip()\n", "\n", "# -----------------------------\n", "# Build the Encoder Inference Model\n", "encoder_input = model.input[0] # Use the first input tensor\n", "encoder_emb = model.get_layer(\"embedding_4\")(encoder_input)\n", "encoder_lstm_outputs = model.get_layer(\"bidirectional_2\")(encoder_emb)\n", "encoder_outputs = encoder_lstm_outputs[0] # Time-distributed outputs\n", "state_h = model.get_layer(\"concatenate_6\").output # Concatenated hidden states\n", "state_c = model.get_layer(\"concatenate_7\").output # Concatenated cell states\n", "\n", "encoder_model = Model(inputs=encoder_input, outputs=[encoder_outputs, state_h, state_c])\n", "\n", "# -----------------------------\n", "# Build the Decoder Inference Model\n", "decoder_input_token = Input(shape=(1,), name=\"decoder_input_token\")\n", "state_h_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_h\")\n", "state_c_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_c\")\n", "encoder_outputs_input = Input(shape=(SEQUENCE_LENGTH, HIDDEN_UNITS * 2), name=\"encoder_outputs_input\")\n", "\n", "decoder_emb = model.get_layer(\"embedding_5\")(decoder_input_token)\n", "decoder_lstm_outputs, dec_state_h, dec_state_c = model.get_layer(\"lstm_5\")(\n", " decoder_emb, initial_state=[state_h_input, state_c_input]\n", ")\n", "attention_out = model.get_layer(\"attention_2\")([decoder_lstm_outputs, encoder_outputs_input])\n", "decoder_combined = model.get_layer(\"concatenate_8\")([decoder_lstm_outputs, attention_out])\n", "decoder_dense_outputs = model.get_layer(\"dense_2\")(decoder_combined)\n", "decoder_outputs_final = Lambda(lambda x: tf.squeeze(x, axis=1))(decoder_dense_outputs)\n", "\n", "decoder_model = Model(\n", " inputs=[decoder_input_token, encoder_outputs_input, state_h_input, state_c_input],\n", " outputs=[decoder_outputs_final, dec_state_h, dec_state_c]\n", ")\n", "\n", "# -----------------------------\n", "# Greedy Decoding Function (English -> Swahili)\n", "def greedy_decode(input_sentence, max_length=50):\n", " \"\"\"\n", " Translate an English sentence to Swahili using greedy decoding.\n", " \n", " Args:\n", " input_sentence (str): Input English sentence.\n", " max_length (int): Maximum length of the generated translation.\n", " \n", " Returns:\n", " str: Translated Swahili sentence.\n", " \"\"\"\n", " # Preprocess and tokenize the input using the encoder's tokenizer (sw_tokenizer)\n", " cleaned = clean_text(input_sentence)\n", " input_seq = sw_tokenizer.texts_to_sequences([cleaned])\n", " input_seq = pad_sequences(input_seq, maxlen=SEQUENCE_LENGTH, padding=\"post\")\n", " \n", " # Run the encoder\n", " encoder_out, state_h, state_c = encoder_model.predict(input_seq)\n", " \n", " # Get start and end tokens from the target tokenizer (en_tokenizer)\n", " start_token = en_tokenizer.word_index[\"\"]\n", " end_token = en_tokenizer.word_index[\"\"]\n", " \n", " target_seq = np.array([[start_token]])\n", " output_tokens = []\n", " \n", " for _ in range(max_length):\n", " output_probs, state_h, state_c = decoder_model.predict(\n", " [target_seq, encoder_out, state_h, state_c]\n", " )\n", " predicted_token = np.argmax(output_probs, axis=-1)[0]\n", " if predicted_token == end_token:\n", " break\n", " output_tokens.append(predicted_token)\n", " target_seq = np.array([[predicted_token]])\n", " \n", " translated_sentence = en_tokenizer.sequences_to_texts([output_tokens])[0]\n", " return translated_sentence\n", "\n", "# -----------------------------\n", "# Function to translate a document (first 10 sentences) and write to a file.\n", "def translate_document(source_path, output_path, num_sentences=10):\n", " with open(source_path, 'r', encoding='utf-8') as f:\n", " lines = [line.strip() for line in f.readlines() if line.strip()]\n", " \n", " selected_sentences = lines[:num_sentences]\n", " translations = []\n", " for sentence in selected_sentences:\n", " translation = greedy_decode(sentence, max_length=50)\n", " translations.append(translation)\n", " print(f\"Source: {sentence}\\nTranslation: {translation}\\n\")\n", " \n", " with open(output_path, 'w', encoding='utf-8') as f:\n", " for t in translations:\n", " f.write(t + \"\\n\")\n", " \n", " return translations\n", "\n", "# -----------------------------\n", "# Updated compute_metrics function\n", "def compute_metrics(generated_path, reference_path):\n", " with open(generated_path, 'r', encoding='utf-8') as f:\n", " generated_translations = [line.strip() for line in f.readlines() if line.strip()]\n", " with open(reference_path, 'r', encoding='utf-8') as f:\n", " reference_translations = [line.strip() for line in f.readlines() if line.strip()]\n", " \n", " # Ensure both lists have the same length by slicing the reference translations\n", " if len(generated_translations) != len(reference_translations):\n", " print(\"Warning: Number of hypotheses and references do not match. Using only the first {} references.\".format(len(generated_translations)))\n", " reference_translations = reference_translations[:len(generated_translations)]\n", " \n", " bleu_score = corpus_bleu([[ref] for ref in reference_translations], generated_translations)\n", " print(f\"BLEU score: {bleu_score}\")\n", "\n", "# -----------------------------\n", "# Example usage:\n", "source_path = '/kaggle/input/nlp-22/en-sw.txt/ELRC-wikipedia_health.en-sw.en' # English source sentences\n", "reference_path = '/kaggle/input/nlp-22/en-sw.txt/ELRC-wikipedia_health.en-sw.sw' # Reference Swahili translations\n", "output_path = '/kaggle/working/translated_document2.txt'\n", "\n", "translations = translate_document(source_path, output_path, num_sentences=10)\n", "compute_metrics(output_path, reference_path)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-01T11:32:54.244154Z", "iopub.status.busy": "2025-03-01T11:32:54.243767Z" }, 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la jarida la Knight\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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walipona kwa muda wa ugonjwa huo ambao ulisababisha ugonjwa huo baada ya kuambukizwa kwa sababu ya ugonjwa wa UKIMWI\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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vile matibabu ya matibabu kama vile matibabu au ya matibabu\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m 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opened the first TB sanatorium in 1859 in Görbersdorf (now Sokołowsko) in Silesia.\n", "Translation: Nilifungua kitabu cha kwanza katika kila sasa katika wakati wa\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m 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na virusi vya korona vya CoV ambavyo ni pamoja na virusi\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 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RNA\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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sensitivity when used in addition to the skin test, but may be less sensitive than the skin test when used alone.\n", "Translation: Inaweza kuongeza kuwa na kipimo cha ngozi ya ngozi lakini inaweza kuwa na mafanikio kuwa na mawazo ya ngozi ya ngozi ya ngozi\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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"Translation: Kwa kawaida hii ni Jahannamu na kwa sababu ya kuwa na dalili za kutisha\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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are underweight can improve their breathing muscle strength by increasing their calorie intake.\n", "Translation: Watu wenye uhitaji wa porini ni anayeweza kuunda nguvu yao ya misuli na kuongeza kazi zao za asili\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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kuzuia upinzani\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "Source: However, they are affected by M. szulgai, M. marinum, and M. kansasii.\n", "Translation: Hata hivyo kuna mifumo ya m m na m\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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screening test for the presence of pre-symptomatic tuberculosis.\n", "Translation: Ingawa haikuwa na ufanisi wa wazi baadaye kuwa ni kipimo cha uchunguzi wa uchunguzi wa kifua kikuu cha kupumua\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "Source: No benefit has been found in those with other health problems.\n", "Translation: Hakuna faida ya kuongezeka katika wale wengine wa afya\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "Source: It is estimated that 3% of all disability is related to COPD.\n", "Translation: Inakadiriwa kwamba hakuna wote wa wote ambao inahusiana na kupiga chafya\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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\u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "Source: This hematogenous transmission can also spread infection to more distant sites, such as peripheral lymph nodes, the kidneys, the brain, and the bones.\n", "Translation: Hii inaweza pia kusababisha maambukizi mengi ya kupumua kama vile vile vile Knight Knight Knight ubongo na vile mifupa na mifupa\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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Aeras Global TB Vaccine Foundation received a gift of more than $280 million (US) from the Bill and Melinda Gates Foundation to develop and license an improved vaccine against tuberculosis for use in high burden countries.\n", "Translation: Miongoni mwa Global Voices ni zawadi ya asili ya Global milioni zaidi kuliko milioni za kwanza kutoka kwenye mtandao wa intaneti na pia na leseni ya kuuza chanjo ya kimatibabu ili kuzuia kuenea kwa matumizi katika nchi zinazoendelea katika nchi zinazoendelea\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "Source: There is some evidence that vitamin A supplementation in children with an HIV infection reduces mortality and improves growth.\n", "Translation: Kuna ushahidi fulani kwamba majukwaa ya kuwa na vizuizi vya watoto kwa wagonjwa wenye ugonjwa mbaya na ukuaji wa kupumua\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: The disease also has large economic impacts.\n", "Translation: Ugonjwa pia una uwezo mkubwa wa kiuchumi\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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katika Afrika ya COVID katika unyanyasaji wa kimataifa nchini Afrika\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "Source: Slightly over half the infected population are women and 2.1 million are children.\n", "Translation: Kiwango cha mchana wa wastani wa idadi ya watu ni wanawake na milioni milioni\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: The World Health Organization estimates the risk of transmission as a result of a medical injection in Africa at 1.2%.\n", "Translation: Shirika la Afya Duniani Shirika la Afya Duniani lilitangaza hatari ya jumla ya COVID katika Afrika\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", 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Department of Health and Human Services.\n", "Translation: Taarifa kwenye matibabu ya matibabu ya matibabu ya matibabu na huduma za afya za binadamu na huduma za afya\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: The photo was displayed in Life, was the winner of the World Press Photo, and acquired worldwide notoriety after being used in a United Colors of Benetton advertising campaign in 1992.\n", "Translation: Picha hiyo ilithibitishwa kuwa mshindi wa waandishi wa habari wa waandishi wa habari na kupatikana duniani kote baada ya kuwa na katika nafasi ya utangazaji wa kampuni ya utangazaji wa utangazaji wa\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "Source: In 2011, there were approximately 730,000 hospitalizations in the United States for COPD.\n", "Translation: Mnamo huko kulikuwa na mamba katika miji ya Marekani\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m 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wanawake\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: The closest relative of HIV-2 is SIV(smm), a virus of the sooty mangabey (Cercocebus atys atys), an Old World monkey living in coastal West Africa (from southern Senegal to western Ivory Coast).\n", "Translation: Kiwango cha juu cha COVID cha COVID ni virusi vya zamani vya zamani vya zamani ya Dunia katika Afrika Kusini mwa Kenya na kusini mwa Kenya na pwani ya Kenya\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m 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infection.\n", "Translation: Katika ugonjwa wa kimsingi wa kawaida wa visa vya Ebola hutokea hivi baada ya maambukizi ya kwanza\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m 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\u001b[1m0s\u001b[0m 22ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "Source: It is thought that several transmissions of the virus from individual to individual in quick succession are necessary to allow it enough time to mutate into HIV.\n", "Translation: Ni suala hilo ni kwamba sehemu kadhaa za virusi vya virusi kutokana na mtu wa haraka wa kupata viini kwa muda wa kutosha ili kutibu COVID\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: AIDS stigma has been further divided into the following three categories:\n", "Translation: Katuni ya UKIMWI imetoa zaidi katika ziara ya tatu za tatu\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", "Source: Many resource-poor places as of 2011 have access to only sputum microscopy.\n", "Translation: Maeneo mengi kama ya kutumia ugonjwa pekee\n", "\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import load_model, Model\n", "from tensorflow.keras.layers import Input, Lambda\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "import pickle\n", "import re\n", "from nltk.translate.bleu_score import corpus_bleu\n", "\n", "# -----------------------------\n", "# Configuration (must match training)\n", "SEQUENCE_LENGTH = 50\n", "EMBEDDING_DIM = 128\n", "HIDDEN_UNITS = 128 # Decoder LSTM state size will be HIDDEN_UNITS*2 (256)\n", "# -----------------------------\n", "\n", "# Load the trained model and tokenizers\n", "model = load_model('/kaggle/working/translation_model200.h5')\n", "with open('/kaggle/working/tokenizers.pkl', 'rb') as f:\n", " sw_tokenizer, en_tokenizer = pickle.load(f)\n", "\n", "def clean_text(text):\n", " \"\"\"Clean input text by removing unwanted characters.\"\"\"\n", " if not isinstance(text, str):\n", " return \"\"\n", " text = re.sub(r\"[^a-zA-Zāēīōūĉĝĥĵŝŭŵŷẑñçàéèêëìíîïòóôùúûüÿśźąćęłńóśźżàâçéèêëîïôùûüÿœæß¿¡\\s']\", \"\", text)\n", " return text.strip()\n", "\n", "# -----------------------------\n", "# Build the Encoder Inference Model\n", "encoder_input = model.input[0] # Use the first input tensor\n", "encoder_emb = model.get_layer(\"embedding_4\")(encoder_input)\n", "encoder_lstm_outputs = model.get_layer(\"bidirectional_2\")(encoder_emb)\n", "encoder_outputs = encoder_lstm_outputs[0] # Time-distributed outputs\n", "state_h = model.get_layer(\"concatenate_6\").output # Concatenated hidden states\n", "state_c = model.get_layer(\"concatenate_7\").output # Concatenated cell states\n", "\n", "encoder_model = Model(inputs=encoder_input, outputs=[encoder_outputs, state_h, state_c])\n", "\n", "# -----------------------------\n", "# Build the Decoder Inference Model\n", "decoder_input_token = Input(shape=(1,), name=\"decoder_input_token\")\n", "state_h_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_h\")\n", "state_c_input = Input(shape=(HIDDEN_UNITS * 2,), name=\"decoder_state_input_c\")\n", "encoder_outputs_input = Input(shape=(SEQUENCE_LENGTH, HIDDEN_UNITS * 2), name=\"encoder_outputs_input\")\n", "\n", "decoder_emb = model.get_layer(\"embedding_5\")(decoder_input_token)\n", "decoder_lstm_outputs, dec_state_h, dec_state_c = model.get_layer(\"lstm_5\")(\n", " decoder_emb, initial_state=[state_h_input, state_c_input]\n", ")\n", "attention_out = model.get_layer(\"attention_2\")([decoder_lstm_outputs, encoder_outputs_input])\n", "decoder_combined = model.get_layer(\"concatenate_8\")([decoder_lstm_outputs, attention_out])\n", "decoder_dense_outputs = model.get_layer(\"dense_2\")(decoder_combined)\n", "decoder_outputs_final = Lambda(lambda x: tf.squeeze(x, axis=1))(decoder_dense_outputs)\n", "\n", "decoder_model = Model(\n", " inputs=[decoder_input_token, encoder_outputs_input, state_h_input, state_c_input],\n", " outputs=[decoder_outputs_final, dec_state_h, dec_state_c]\n", ")\n", "\n", "# -----------------------------\n", "# Greedy Decoding Function (English -> Swahili)\n", "def greedy_decode(input_sentence, max_length=50):\n", " \"\"\"\n", " Translate an English sentence to Swahili using greedy decoding.\n", " \n", " Args:\n", " input_sentence (str): Input English sentence.\n", " max_length (int): Maximum length of the generated translation.\n", " \n", " Returns:\n", " str: Translated Swahili sentence.\n", " \"\"\"\n", " # Preprocess and tokenize the input using the encoder's tokenizer (sw_tokenizer)\n", " cleaned = clean_text(input_sentence)\n", " input_seq = sw_tokenizer.texts_to_sequences([cleaned])\n", " input_seq = pad_sequences(input_seq, maxlen=SEQUENCE_LENGTH, padding=\"post\")\n", " \n", " # Run the encoder\n", " encoder_out, state_h, state_c = encoder_model.predict(input_seq)\n", " \n", " # Get start and end tokens from the target tokenizer (en_tokenizer)\n", " start_token = en_tokenizer.word_index[\"\"]\n", " end_token = en_tokenizer.word_index[\"\"]\n", " \n", " target_seq = np.array([[start_token]])\n", " output_tokens = []\n", " \n", " for _ in range(max_length):\n", " output_probs, state_h, state_c = decoder_model.predict(\n", " [target_seq, encoder_out, state_h, state_c]\n", " )\n", " predicted_token = np.argmax(output_probs, axis=-1)[0]\n", " if predicted_token == end_token:\n", " break\n", " output_tokens.append(predicted_token)\n", " target_seq = np.array([[predicted_token]])\n", " \n", " translated_sentence = en_tokenizer.sequences_to_texts([output_tokens])[0]\n", " return translated_sentence\n", "\n", "# -----------------------------\n", "# Function to translate a document (first 10 sentences) and write to a file.\n", "def translate_document(source_path, output_path, num_sentences=10):\n", " with open(source_path, 'r', encoding='utf-8') as f:\n", " lines = [line.strip() for line in f.readlines() if line.strip()]\n", " \n", " selected_sentences = lines[:num_sentences]\n", " translations = []\n", " for sentence in selected_sentences:\n", " translation = greedy_decode(sentence, max_length=50)\n", " translations.append(translation)\n", " print(f\"Source: {sentence}\\nTranslation: {translation}\\n\")\n", " \n", " with open(output_path, 'w', encoding='utf-8') as f:\n", " for t in translations:\n", " f.write(t + \"\\n\")\n", " \n", " return translations\n", "\n", "# -----------------------------\n", "# Updated compute_metrics function\n", "def compute_metrics(generated_path, reference_path):\n", " with open(generated_path, 'r', encoding='utf-8') as f:\n", " generated_translations = [line.strip() for line in f.readlines() if line.strip()]\n", " with open(reference_path, 'r', encoding='utf-8') as f:\n", " reference_translations = [line.strip() for line in f.readlines() if line.strip()]\n", " \n", " # Ensure both lists have the same length by slicing the reference translations\n", " if len(generated_translations) != len(reference_translations):\n", " print(\"Warning: Number of hypotheses and references do not match. Using only the first {} references.\".format(len(generated_translations)))\n", " reference_translations = reference_translations[:len(generated_translations)]\n", " \n", " bleu_score = corpus_bleu([[ref] for ref in reference_translations], generated_translations)\n", " print(f\"BLEU score: {bleu_score}\")\n", "\n", "# -----------------------------\n", "# Example usage:\n", "source_path = '/kaggle/input/nlp-22/en-sw.txt/ELRC-wikipedia_health.en-sw.en' # English source sentences\n", "reference_path = '/kaggle/input/nlp-22/en-sw.txt/ELRC-wikipedia_health.en-sw.sw' # Reference Swahili translations\n", "output_path = '/kaggle/working/translated_document2.txt'\n", "\n", "translations = translate_document(source_path, output_path, num_sentences=500)\n", "compute_metrics(output_path, reference_path)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "trusted": true }, "outputs": [], "source": [] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [ { "datasetId": 6662595, "sourceId": 10823292, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 30918, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 4 }