update model card README.md

README.md

@@ -0,0 +1,80 @@
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioLinkBERT-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-LitCovid-1.4
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5613
+- Hamming loss: 0.0775
+- F1 micro: 0.6253
+- F1 macro: 0.4797
+- F1 weighted: 0.7043
+- F1 samples: 0.6321
+- Precision micro: 0.4806
+- Precision macro: 0.3631
+- Precision weighted: 0.6169
+- Precision samples: 0.5276
+- Recall micro: 0.8947
+- Recall macro: 0.8442
+- Recall weighted: 0.8947
+- Recall samples: 0.9099
+- Roc Auc: 0.9097
+- Accuracy: 0.0849
+
+## 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
+- gradient_accumulation_steps: 2
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.6654        | 1.0   | 1151 | 0.6313          | 0.1143       | 0.5259   | 0.3963   | 0.6460      | 0.5359     | 0.3756          | 0.2909          | 0.5586             | 0.4182            | 0.8764       | 0.8497       | 0.8764          | 0.8940         | 0.8814  | 0.0227   |
+| 0.5313        | 2.0   | 2303 | 0.5682          | 0.0997       | 0.5655   | 0.4266   | 0.6717      | 0.5784     | 0.4128          | 0.3161          | 0.5789             | 0.4624            | 0.8972       | 0.8620       | 0.8972          | 0.9120         | 0.8988  | 0.0492   |
+| 0.4594        | 3.0   | 3454 | 0.5529          | 0.0884       | 0.5938   | 0.4517   | 0.6907      | 0.6012     | 0.4446          | 0.3394          | 0.6041             | 0.4883            | 0.8939       | 0.8549       | 0.8939          | 0.9094         | 0.9034  | 0.0586   |
+| 0.3966        | 4.0   | 4606 | 0.5580          | 0.0797       | 0.6193   | 0.4739   | 0.7014      | 0.6245     | 0.4731          | 0.3579          | 0.6129             | 0.5166            | 0.8965       | 0.8476       | 0.8965          | 0.9109         | 0.9093  | 0.0751   |
+| 0.3693        | 5.0   | 5755 | 0.5613          | 0.0775       | 0.6253   | 0.4797   | 0.7043      | 0.6321     | 0.4806          | 0.3631          | 0.6169             | 0.5276            | 0.8947       | 0.8442       | 0.8947          | 0.9099         | 0.9097  | 0.0849   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.13.3