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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: disi-unibo-nlp |
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results: [] |
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datasets: |
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- disi-unibo-nlp/foodex2-clean |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# DeBERTa FoodEx2 Coder |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the **train_task1** split of the dataset [foodex2-clean](). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0548 |
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- Accuracy: 0.9822 |
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- F1: 0.8507 |
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- Precision: 0.9301 |
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- Recall: 0.7838 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.124 | 0.2899 | 1000 | 0.1032 | 0.9671 | 0.7090 | 0.8324 | 0.6174 | |
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| 0.1004 | 0.5799 | 2000 | 0.0855 | 0.9721 | 0.7551 | 0.8769 | 0.6631 | |
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| 0.0858 | 0.8698 | 3000 | 0.0737 | 0.9757 | 0.7873 | 0.9102 | 0.6937 | |
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| 0.0736 | 1.1598 | 4000 | 0.0696 | 0.9786 | 0.8196 | 0.9031 | 0.7502 | |
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| 0.0696 | 1.4497 | 5000 | 0.0639 | 0.9795 | 0.8294 | 0.8996 | 0.7694 | |
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| 0.068 | 1.7396 | 6000 | 0.0606 | 0.9812 | 0.8401 | 0.9385 | 0.7604 | |
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| 0.0634 | 2.0296 | 7000 | 0.0593 | 0.9809 | 0.8414 | 0.9123 | 0.7808 | |
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| 0.0565 | 2.3195 | 8000 | 0.0568 | 0.9820 | 0.8485 | 0.9318 | 0.7790 | |
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| 0.0584 | 2.6095 | 9000 | 0.0553 | 0.9822 | 0.8512 | 0.9296 | 0.7850 | |
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| 0.0568 | 2.8994 | 10000 | 0.0548 | 0.9822 | 0.8507 | 0.9301 | 0.7838 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
