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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- EMBO/SourceData |
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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_GP-CHEM-ROLES_v_2-0-3_BioLinkBERT_large |
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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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args: ROLES_MULTI |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9656922807631717 |
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- name: Recall |
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type: recall |
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value: 0.9742186120430867 |
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- name: F1 |
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type: f1 |
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value: 0.9699367088607594 |
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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_GP-CHEM-ROLES_v_2-0-3_BioLinkBERT_large |
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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: 0.0061 |
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- Accuracy Score: 0.9982 |
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- Precision: 0.9657 |
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- Recall: 0.9742 |
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- F1: 0.9699 |
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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: 1.5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 256 |
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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: Adafactor |
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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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| 0.0099 | 1.0 | 942 | 0.0061 | 0.9982 | 0.9657 | 0.9742 | 0.9699 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0a0+bfe5ad2 |
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- Datasets 2.10.1 |
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- Tokenizers 0.12.1 |
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