---
library_name: transformers
language:
- en
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
- matthews_correlation
model-index:
- name: wordnet-network-predictor
  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. -->

# wordnet-network-predictor

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
- Precision: 0.9808
- Recall: 0.9944
- F1: 0.9875
- Accuracy: 0.9874
- Matthews Correlation: 0.9749

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 320
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------------:|
| No log        | 0     | 0     | 0.7276          | 0.4368    | 0.7188 | 0.5434 | 0.3935   | -0.2846              |
| 0.3924        | 1.0   | 2144  | 0.0697          | 0.9622    | 0.9893 | 0.9756 | 0.9751   | 0.9506               |
| 0.2978        | 2.0   | 4288  | 0.0522          | 0.9770    | 0.9883 | 0.9826 | 0.9825   | 0.9650               |
| 0.2273        | 3.0   | 6432  | 0.0534          | 0.9739    | 0.9932 | 0.9834 | 0.9832   | 0.9666               |
| 0.1735        | 4.0   | 8576  | 0.0483          | 0.9786    | 0.9925 | 0.9855 | 0.9853   | 0.9707               |
| 0.1457        | 5.0   | 10720 | 0.0463          | 0.9790    | 0.9935 | 0.9862 | 0.9860   | 0.9722               |
| 0.1212        | 6.0   | 12864 | 0.0456          | 0.9798    | 0.9940 | 0.9869 | 0.9867   | 0.9735               |
| 0.0956        | 7.0   | 15008 | 0.0483          | 0.9800    | 0.9945 | 0.9872 | 0.9870   | 0.9742               |
| 0.1035        | 8.0   | 17152 | 0.0462          | 0.9820    | 0.9937 | 0.9878 | 0.9877   | 0.9755               |
| 0.0810        | 9.0   | 19296 | 0.0482          | 0.9807    | 0.9942 | 0.9874 | 0.9873   | 0.9746               |
| 0.0831        | 10.0  | 21440 | 0.0483          | 0.9808    | 0.9944 | 0.9875 | 0.9874   | 0.9749               |


### Framework versions

- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2