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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
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