update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioELECTRA-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. -->
+
+# BioELECTRA-LitCovid-v1.3.1
+
+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: 0.6749
+- Hamming loss: 0.0257
+- F1 micro: 0.7955
+- F1 macro: 0.3005
+- F1 weighted: 0.8714
+- F1 samples: 0.8642
+- Precision micro: 0.6936
+- Precision macro: 0.2470
+- Precision weighted: 0.8294
+- Precision samples: 0.8463
+- Recall micro: 0.9326
+- Recall macro: 0.7358
+- Recall weighted: 0.9326
+- Recall samples: 0.9427
+- Roc Auc: 0.9546
+- Accuracy: 0.6664
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 1.385         | 1.0   | 2272  | 0.6961          | 0.0592       | 0.6188   | 0.2176   | 0.7539      | 0.7422     | 0.4725          | 0.1706          | 0.6672             | 0.6890            | 0.8965       | 0.6896       | 0.8965          | 0.9065         | 0.9199  | 0.3887   |
+| 1.2034        | 2.0   | 4544  | 0.6242          | 0.0342       | 0.7421   | 0.2668   | 0.8404      | 0.8354     | 0.6231          | 0.2180          | 0.7922             | 0.8120            | 0.9172       | 0.6872       | 0.9172          | 0.9319         | 0.9429  | 0.5906   |
+| 1.0857        | 3.0   | 6816  | 0.6185          | 0.0270       | 0.7869   | 0.2949   | 0.8615      | 0.8587     | 0.6815          | 0.2402          | 0.8153             | 0.8382            | 0.9308       | 0.7164       | 0.9308          | 0.9437         | 0.9531  | 0.6444   |
+| 0.8846        | 4.0   | 9088  | 0.6143          | 0.0260       | 0.7936   | 0.2994   | 0.8677      | 0.8626     | 0.6916          | 0.2460          | 0.8237             | 0.8444            | 0.9309       | 0.7254       | 0.9309          | 0.9421         | 0.9537  | 0.6594   |
+| 0.6753        | 5.0   | 11360 | 0.6749          | 0.0257       | 0.7955   | 0.3005   | 0.8714      | 0.8642     | 0.6936          | 0.2470          | 0.8294             | 0.8463            | 0.9326       | 0.7358       | 0.9326          | 0.9427         | 0.9546  | 0.6664   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.13.3