---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results_trainer
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# results_trainer

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4417
- Accuracy: 0.799
- F1: 0.7767
- Precision: 0.7296
- Recall: 0.8302

## Model description

Test model created as part of an online course on adapters for working with text data.

## Intended uses & limitations

This is just a test case for learning.

## Training and evaluation data

[HateEval 2019 - Task 5](https://aclanthology.org/S19-2007/) data set

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 108
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4928        | 1.0   | 250  | 0.4792          | 0.768    | 0.7089 | 0.7513    | 0.6710 |
| 0.3599        | 2.0   | 500  | 0.4417          | 0.799    | 0.7767 | 0.7296    | 0.8302 |
| 0.346         | 3.0   | 750  | 0.4399          | 0.8065   | 0.7730 | 0.7636    | 0.7827 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1