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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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- generated_from_trainer |
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datasets: |
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- source_data |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: SourceData_SmallmolRoles_v1_0_0_BioLinkBERT_base |
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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: source_data |
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type: source_data |
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config: ROLES_SM |
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split: validation |
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args: ROLES_SM |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9716285227917534 |
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- name: Recall |
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type: recall |
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value: 0.9729166666666667 |
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- name: F1 |
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type: f1 |
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value: 0.9722721680704078 |
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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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# SourceData_SmallmolRoles_v1_0_0_BioLinkBERT_base |
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the source_data dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0016 |
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- Accuracy Score: 0.9996 |
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- Precision: 0.9716 |
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- Recall: 0.9729 |
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- F1: 0.9723 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use adafactor and the args are: |
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No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| |
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| 0.0011 | 1.0 | 864 | 0.0016 | 0.9996 | 0.9716 | 0.9729 | 0.9723 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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