update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: tmvar_2e-05_0404_ES6_strict_tok1
+  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. -->
+
+# tmvar_2e-05_0404_ES6_strict_tok1
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0330
+- Precision: 0.8213
+- Recall: 0.8629
+- F1: 0.8416
+- Accuracy: 0.9916
+
+## 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
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.7315        | 0.49  | 25   | 0.2102          | 0.0       | 0.0    | 0.0    | 0.9555   |
+| 0.1371        | 0.98  | 50   | 0.1021          | 0.0       | 0.0    | 0.0    | 0.9698   |
+| 0.0836        | 1.47  | 75   | 0.0960          | 0.0       | 0.0    | 0.0    | 0.9725   |
+| 0.0666        | 1.96  | 100  | 0.0526          | 0.0       | 0.0    | 0.0    | 0.9804   |
+| 0.0391        | 2.45  | 125  | 0.0521          | 0.7294    | 0.3147 | 0.4397 | 0.9843   |
+| 0.0252        | 2.94  | 150  | 0.0382          | 0.8630    | 0.6396 | 0.7347 | 0.9899   |
+| 0.016         | 3.43  | 175  | 0.0452          | 0.6496    | 0.7716 | 0.7053 | 0.9872   |
+| 0.0145        | 3.92  | 200  | 0.0272          | 0.8730    | 0.8376 | 0.8549 | 0.9923   |
+| 0.0082        | 4.41  | 225  | 0.0301          | 0.8804    | 0.8223 | 0.8504 | 0.9920   |
+| 0.0058        | 4.9   | 250  | 0.0330          | 0.8213    | 0.8629 | 0.8416 | 0.9916   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3