update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Bioformer-LitCovid-v1.2.2
+  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. -->
+
+# Bioformer-LitCovid-v1.2.2
+
+This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2230
+- F1 micro: 0.9107
+- F1 macro: 0.8633
+- F1 weighted: 0.9127
+- F1 samples: 0.9132
+- Precision micro: 0.8780
+- Precision macro: 0.8105
+- Precision weighted: 0.8840
+- Precision samples: 0.9034
+- Recall micro: 0.9460
+- Recall macro: 0.9339
+- Recall weighted: 0.9460
+- Recall samples: 0.9534
+- Roc Auc: 0.9577
+- Accuracy: 0.7542
+
+## 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 | 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.2625        | 1.0   | 2183  | 0.2415          | 0.8961   | 0.8499   | 0.8980      | 0.8996     | 0.8443          | 0.7844          | 0.8501             | 0.8775            | 0.9545       | 0.9373       | 0.9545          | 0.9590         | 0.9568  | 0.7083   |
+| 0.2099        | 2.0   | 4366  | 0.2230          | 0.9107   | 0.8633   | 0.9127      | 0.9132     | 0.8780          | 0.8105          | 0.8840             | 0.9034            | 0.9460       | 0.9339       | 0.9460          | 0.9534         | 0.9577  | 0.7542   |
+| 0.1735        | 3.0   | 6549  | 0.2661          | 0.9141   | 0.8732   | 0.9153      | 0.9155     | 0.8821          | 0.8361          | 0.8857             | 0.9057            | 0.9486       | 0.9203       | 0.9486          | 0.9543         | 0.9596  | 0.7653   |
+| 0.1336        | 4.0   | 8732  | 0.2682          | 0.9187   | 0.8769   | 0.9197      | 0.9207     | 0.8953          | 0.8408          | 0.8979             | 0.9169            | 0.9435       | 0.9199       | 0.9435          | 0.9511         | 0.9589  | 0.7804   |
+| 0.1102        | 5.0   | 10915 | 0.2825          | 0.9183   | 0.8778   | 0.9191      | 0.9199     | 0.8913          | 0.8413          | 0.8936             | 0.9134            | 0.9470       | 0.9202       | 0.9470          | 0.9536         | 0.9601  | 0.7792   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3