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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: michiyasunaga/BioLinkBERT-base |
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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-en-fasttext-75-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-en-fasttext-75-ner |
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type: Rodrigo1771/drugtemist-en-fasttext-75-ner |
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config: DrugTEMIST English NER |
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split: validation |
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args: DrugTEMIST English NER |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9249771271729186 |
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- name: Recall |
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type: recall |
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value: 0.9422180801491147 |
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- name: F1 |
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type: f1 |
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value: 0.9335180055401663 |
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- name: Accuracy |
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type: accuracy |
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value: 0.998772081600759 |
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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 [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-fasttext-75-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0076 |
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- Precision: 0.9250 |
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- Recall: 0.9422 |
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- F1: 0.9335 |
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- Accuracy: 0.9988 |
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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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| 0.0183 | 1.0 | 507 | 0.0055 | 0.8974 | 0.9376 | 0.9170 | 0.9985 | |
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| 0.0043 | 2.0 | 1014 | 0.0059 | 0.9099 | 0.9320 | 0.9208 | 0.9986 | |
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| 0.0022 | 3.0 | 1521 | 0.0057 | 0.9015 | 0.9301 | 0.9156 | 0.9985 | |
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| 0.0018 | 4.0 | 2028 | 0.0072 | 0.9275 | 0.9180 | 0.9227 | 0.9986 | |
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| 0.0009 | 5.0 | 2535 | 0.0064 | 0.9078 | 0.9357 | 0.9215 | 0.9987 | |
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| 0.0007 | 6.0 | 3042 | 0.0064 | 0.9194 | 0.9357 | 0.9275 | 0.9987 | |
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| 0.0004 | 7.0 | 3549 | 0.0072 | 0.9289 | 0.9376 | 0.9332 | 0.9988 | |
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| 0.0004 | 8.0 | 4056 | 0.0076 | 0.9250 | 0.9422 | 0.9335 | 0.9988 | |
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| 0.0003 | 9.0 | 4563 | 0.0077 | 0.9161 | 0.9366 | 0.9263 | 0.9987 | |
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| 0.0002 | 10.0 | 5070 | 0.0077 | 0.9195 | 0.9366 | 0.9280 | 0.9988 | |
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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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