update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioElectra-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. -->
+
+# BioElectra-LitCovid-1.4
+
+This model is a fine-tuned version of [kamalkraj/bioelectra-base-discriminator-pubmed](https://huggingface.co/kamalkraj/bioelectra-base-discriminator-pubmed) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6551
+- Hamming loss: 0.1096
+- F1 micro: 0.5375
+- F1 macro: 0.4017
+- F1 weighted: 0.6519
+- F1 samples: 0.5520
+- Precision micro: 0.3867
+- Precision macro: 0.2948
+- Precision weighted: 0.5638
+- Precision samples: 0.4347
+- Recall micro: 0.8813
+- Recall macro: 0.8425
+- Recall weighted: 0.8813
+- Recall samples: 0.8977
+- Roc Auc: 0.8862
+- Accuracy: 0.0375
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 0.8117        | 1.0   | 1151 | 0.7562          | 0.1732       | 0.4140   | 0.3137   | 0.5784      | 0.4179     | 0.2740          | 0.2255          | 0.4949             | 0.2947            | 0.8462       | 0.8285       | 0.8462          | 0.8675         | 0.8357  | 0.0005   |
+| 0.639         | 2.0   | 2303 | 0.6690          | 0.1346       | 0.4836   | 0.3618   | 0.6199      | 0.4952     | 0.3347          | 0.2629          | 0.5289             | 0.3716            | 0.8714       | 0.8448       | 0.8714          | 0.8906         | 0.8682  | 0.0095   |
+| 0.556         | 3.0   | 3454 | 0.6453          | 0.1253       | 0.5012   | 0.3747   | 0.6358      | 0.5147     | 0.3519          | 0.2750          | 0.5539             | 0.3944            | 0.8706       | 0.8536       | 0.8706          | 0.8895         | 0.8728  | 0.0220   |
+| 0.4906        | 4.0   | 4606 | 0.6567          | 0.1111       | 0.5339   | 0.4013   | 0.6494      | 0.5469     | 0.3832          | 0.2946          | 0.5608             | 0.4282            | 0.8800       | 0.8428       | 0.8800          | 0.8976         | 0.8848  | 0.0312   |
+| 0.4594        | 5.0   | 5755 | 0.6551          | 0.1096       | 0.5375   | 0.4017   | 0.6519      | 0.5520     | 0.3867          | 0.2948          | 0.5638             | 0.4347            | 0.8813       | 0.8425       | 0.8813          | 0.8977         | 0.8862  | 0.0375   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3