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--- |
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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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- sem_eval_2024_task_2 |
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metrics: |
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- accuracy |
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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: run1 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: sem_eval_2024_task_2 |
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type: sem_eval_2024_task_2 |
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config: sem_eval_2024_task_2_source |
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split: validation |
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args: sem_eval_2024_task_2_source |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.62 |
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- name: Precision |
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type: precision |
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value: 0.6273344651952462 |
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- name: Recall |
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type: recall |
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value: 0.62 |
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- name: F1 |
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type: f1 |
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value: 0.614448051948052 |
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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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# run1 |
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the sem_eval_2024_task_2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6923 |
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- Accuracy: 0.62 |
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- Precision: 0.6273 |
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- Recall: 0.62 |
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- F1: 0.6144 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.99 | 53 | 0.6893 | 0.55 | 0.5565 | 0.55 | 0.5366 | |
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| 0.7034 | 2.0 | 107 | 0.6771 | 0.595 | 0.5986 | 0.595 | 0.5913 | |
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| 0.7034 | 2.99 | 160 | 0.6680 | 0.585 | 0.5882 | 0.585 | 0.5812 | |
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| 0.6769 | 4.0 | 214 | 0.6448 | 0.625 | 0.6271 | 0.625 | 0.6234 | |
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| 0.6769 | 4.99 | 267 | 0.6465 | 0.625 | 0.6503 | 0.625 | 0.6085 | |
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| 0.5962 | 6.0 | 321 | 0.6457 | 0.635 | 0.6456 | 0.635 | 0.6282 | |
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| 0.5962 | 6.99 | 374 | 0.6595 | 0.63 | 0.6366 | 0.63 | 0.6255 | |
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| 0.4977 | 8.0 | 428 | 0.6763 | 0.62 | 0.6273 | 0.62 | 0.6144 | |
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| 0.4977 | 8.99 | 481 | 0.6831 | 0.63 | 0.6379 | 0.63 | 0.6246 | |
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| 0.4268 | 9.91 | 530 | 0.6923 | 0.62 | 0.6273 | 0.62 | 0.6144 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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