update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioLinkBERT-Large-LitCovid-1.4
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# BioLinkBERT-Large-LitCovid-1.4
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5976
+- Hamming loss: 0.0604
+- F1 micro: 0.6804
+- F1 macro: 0.5425
+- F1 weighted: 0.7357
+- F1 samples: 0.6807
+- Precision micro: 0.5509
+- Precision macro: 0.4271
+- Precision weighted: 0.6552
+- Precision samples: 0.5921
+- Recall micro: 0.8895
+- Recall macro: 0.8221
+- Recall weighted: 0.8895
+- Recall samples: 0.9063
+- Roc Auc: 0.9165
+- Accuracy: 0.1370
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Hamming loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.5869        | 1.0   | 1151 | 0.5737          | 0.0978       | 0.5682   | 0.4375   | 0.6759      | 0.5754     | 0.4172          | 0.3269          | 0.5906             | 0.4591            | 0.8901       | 0.8593       | 0.8901          | 0.9076         | 0.8966  | 0.0421   |
+| 0.4636        | 2.0   | 2302 | 0.5316          | 0.0805       | 0.6179   | 0.4702   | 0.7052      | 0.6237     | 0.4704          | 0.3554          | 0.6181             | 0.5153            | 0.9005       | 0.8611       | 0.9005          | 0.9160         | 0.9107  | 0.0812   |
+| 0.3782        | 3.0   | 3453 | 0.5382          | 0.0760       | 0.6321   | 0.4929   | 0.7146      | 0.6327     | 0.4864          | 0.3757          | 0.6293             | 0.5230            | 0.9027       | 0.8556       | 0.9027          | 0.9183         | 0.9142  | 0.0797   |
+| 0.3031        | 4.0   | 4605 | 0.5807          | 0.0619       | 0.6754   | 0.5346   | 0.7343      | 0.6744     | 0.5437          | 0.4189          | 0.6531             | 0.5820            | 0.8915       | 0.8274       | 0.8915          | 0.9089         | 0.9166  | 0.1235   |
+| 0.2625        | 5.0   | 5755 | 0.5976          | 0.0604       | 0.6804   | 0.5425   | 0.7357      | 0.6807     | 0.5509          | 0.4271          | 0.6552             | 0.5921            | 0.8895       | 0.8221       | 0.8895          | 0.9063         | 0.9165  | 0.1370   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.13.3