update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: PubMedBERT-Large-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. -->
+
+# PubMedBERT-Large-LitCovid-v1.3.1
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2334
+- Hamming loss: 0.0114
+- F1 micro: 0.8967
+- F1 macro: 0.5317
+- F1 weighted: 0.9010
+- F1 samples: 0.9021
+- Precision micro: 0.8733
+- Precision macro: 0.4705
+- Precision weighted: 0.8829
+- Precision samples: 0.9034
+- Recall micro: 0.9214
+- Recall macro: 0.6606
+- Recall weighted: 0.9214
+- Recall samples: 0.9340
+- Roc Auc: 0.9569
+- Accuracy: 0.7445
+
+## 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: 4
+- eval_batch_size: 4
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
+| 2.4299        | 1.0   | 9086  | 0.8402          | 0.0183       | 0.8440   | 0.3442   | 0.8744      | 0.8786     | 0.7770          | 0.2832          | 0.8384             | 0.8690            | 0.9236       | 0.6237       | 0.9236          | 0.9363         | 0.9543  | 0.6930   |
+| 1.388         | 2.0   | 18172 | 0.7233          | 0.0161       | 0.8613   | 0.3836   | 0.8886      | 0.8913     | 0.8016          | 0.3164          | 0.8565             | 0.8827            | 0.9306       | 0.6955       | 0.9306          | 0.9426         | 0.9588  | 0.7163   |
+| 0.9723        | 3.0   | 27258 | 0.8506          | 0.0132       | 0.8829   | 0.4584   | 0.8970      | 0.8963     | 0.8434          | 0.3870          | 0.8735             | 0.8924            | 0.9264       | 0.6941       | 0.9264          | 0.9380         | 0.9583  | 0.7313   |
+| 0.7713        | 4.0   | 36344 | 1.0207          | 0.0119       | 0.8923   | 0.5060   | 0.8994      | 0.8990     | 0.8635          | 0.4387          | 0.8795             | 0.8976            | 0.9230       | 0.6758       | 0.9230          | 0.9349         | 0.9574  | 0.7389   |
+| 0.3859        | 5.0   | 45430 | 1.2334          | 0.0114       | 0.8967   | 0.5317   | 0.9010      | 0.9021     | 0.8733          | 0.4705          | 0.8829             | 0.9034            | 0.9214       | 0.6606       | 0.9214          | 0.9340         | 0.9569  | 0.7445   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3