update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioBERT-LitCovid-v1.3.1
+  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. -->
+
+# BioBERT-LitCovid-v1.3.1
+
+This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6731
+- Hamming loss: 0.0186
+- F1 micro: 0.8434
+- F1 macro: 0.3657
+- F1 weighted: 0.8790
+- F1 samples: 0.8763
+- Precision micro: 0.7702
+- Precision macro: 0.2942
+- Precision weighted: 0.8399
+- Precision samples: 0.8618
+- Recall micro: 0.9320
+- Recall macro: 0.7288
+- Recall weighted: 0.9320
+- Recall samples: 0.9432
+- Roc Auc: 0.9581
+- Accuracy: 0.6841
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 1.1065        | 1.0   | 2272  | 0.4899          | 0.0357       | 0.7347   | 0.2684   | 0.8280      | 0.8274     | 0.6112          | 0.2128          | 0.7700             | 0.7976            | 0.9207       | 0.7424       | 0.9207          | 0.9370         | 0.9437  | 0.5688   |
+| 0.8552        | 2.0   | 4544  | 0.4641          | 0.0246       | 0.8018   | 0.3270   | 0.8588      | 0.8548     | 0.7057          | 0.2595          | 0.8123             | 0.8327            | 0.9282       | 0.7833       | 0.9282          | 0.9424         | 0.9531  | 0.6325   |
+| 0.7061        | 3.0   | 6816  | 0.5058          | 0.0227       | 0.8166   | 0.3320   | 0.8679      | 0.8652     | 0.7201          | 0.2640          | 0.8146             | 0.8402            | 0.9429       | 0.7601       | 0.9429          | 0.9522         | 0.9611  | 0.6548   |
+| 0.5914        | 4.0   | 9088  | 0.6116          | 0.0196       | 0.8368   | 0.3588   | 0.8758      | 0.8719     | 0.7572          | 0.2869          | 0.8321             | 0.8533            | 0.9353       | 0.7398       | 0.9353          | 0.9456         | 0.9591  | 0.6706   |
+| 0.294         | 5.0   | 11360 | 0.6731          | 0.0186       | 0.8434   | 0.3657   | 0.8790      | 0.8763     | 0.7702          | 0.2942          | 0.8399             | 0.8618            | 0.9320       | 0.7288       | 0.9320          | 0.9432         | 0.9581  | 0.6841   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.13.3