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--- |
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library_name: transformers |
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base_model: IVN-RIN/bioBIT |
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tags: |
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- token-classification |
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- generated_from_trainer |
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datasets: |
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- Rodrigo1771/drugtemist-it-fasttext-85-ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: output |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: Rodrigo1771/drugtemist-it-fasttext-85-ner |
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type: Rodrigo1771/drugtemist-it-fasttext-85-ner |
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config: DrugTEMIST Italian NER |
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split: validation |
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args: DrugTEMIST Italian NER |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9211538461538461 |
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- name: Recall |
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type: recall |
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value: 0.9273959341723137 |
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- name: F1 |
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type: f1 |
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value: 0.9242643511818619 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9986302259153467 |
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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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# output |
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This model is a fine-tuned version of [IVN-RIN/bioBIT](https://huggingface.co/IVN-RIN/bioBIT) on the Rodrigo1771/drugtemist-it-fasttext-85-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0080 |
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- Precision: 0.9212 |
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- Recall: 0.9274 |
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- F1: 0.9243 |
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- Accuracy: 0.9986 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 0.9989 | 451 | 0.0051 | 0.9326 | 0.8703 | 0.9004 | 0.9984 | |
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| 0.0116 | 2.0 | 903 | 0.0049 | 0.9066 | 0.9206 | 0.9135 | 0.9985 | |
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| 0.0034 | 2.9989 | 1354 | 0.0056 | 0.8990 | 0.9216 | 0.9101 | 0.9984 | |
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| 0.0018 | 4.0 | 1806 | 0.0066 | 0.9094 | 0.9235 | 0.9164 | 0.9985 | |
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| 0.0011 | 4.9989 | 2257 | 0.0056 | 0.9082 | 0.9293 | 0.9187 | 0.9986 | |
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| 0.0007 | 6.0 | 2709 | 0.0068 | 0.9145 | 0.9109 | 0.9127 | 0.9985 | |
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| 0.0005 | 6.9989 | 3160 | 0.0076 | 0.8880 | 0.9284 | 0.9077 | 0.9984 | |
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| 0.0003 | 8.0 | 3612 | 0.0080 | 0.9094 | 0.9235 | 0.9164 | 0.9986 | |
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| 0.0002 | 8.9989 | 4063 | 0.0078 | 0.9162 | 0.9206 | 0.9184 | 0.9986 | |
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| 0.0001 | 9.9889 | 4510 | 0.0080 | 0.9212 | 0.9274 | 0.9243 | 0.9986 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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