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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-8-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-8-ner |
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type: Rodrigo1771/drugtemist-en-8-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: 0.9318394024276377 |
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- name: Recall |
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type: recall |
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value: 0.9301025163094129 |
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- name: F1 |
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type: f1 |
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value: 0.9309701492537313 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9986953367008066 |
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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-8-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0057 |
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- Precision: 0.9318 |
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- Recall: 0.9301 |
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- F1: 0.9310 |
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- Accuracy: 0.9987 |
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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 | 1.0 | 493 | 0.0050 | 0.9288 | 0.9245 | 0.9267 | 0.9987 | |
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| 0.018 | 2.0 | 986 | 0.0057 | 0.9104 | 0.9189 | 0.9147 | 0.9984 | |
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| 0.0044 | 3.0 | 1479 | 0.0079 | 0.9362 | 0.9161 | 0.9260 | 0.9985 | |
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| 0.0023 | 4.0 | 1972 | 0.0057 | 0.9318 | 0.9301 | 0.9310 | 0.9987 | |
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| 0.0014 | 5.0 | 2465 | 0.0070 | 0.9201 | 0.9226 | 0.9214 | 0.9986 | |
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| 0.0008 | 6.0 | 2958 | 0.0082 | 0.9118 | 0.9254 | 0.9186 | 0.9985 | |
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| 0.0006 | 7.0 | 3451 | 0.0074 | 0.9172 | 0.9394 | 0.9282 | 0.9986 | |
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| 0.0003 | 8.0 | 3944 | 0.0085 | 0.9219 | 0.9245 | 0.9232 | 0.9985 | |
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| 0.0003 | 9.0 | 4437 | 0.0086 | 0.9149 | 0.9320 | 0.9234 | 0.9985 | |
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| 0.0002 | 10.0 | 4930 | 0.0089 | 0.9172 | 0.9292 | 0.9231 | 0.9985 | |
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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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