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: MultiCorp_all_label_5e-05_0404_ES2_strict_tok
+  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. -->
+
+# MultiCorp_all_label_5e-05_0404_ES2_strict_tok
+
+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: 0.1346
+- Precision: 0.3090
+- Recall: 0.1750
+- F1: 0.2235
+- Accuracy: 0.9674
+
+## 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: 5e-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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.9281        | 0.08  | 25   | 0.2509          | 0.0       | 0.0    | 0.0    | 0.9637   |
+| 0.2583        | 0.15  | 50   | 0.2399          | 0.0       | 0.0    | 0.0    | 0.9637   |
+| 0.2319        | 0.23  | 75   | 0.2011          | 0.0       | 0.0    | 0.0    | 0.9637   |
+| 0.1901        | 0.31  | 100  | 0.1717          | 0.3333    | 0.0014 | 0.0028 | 0.9639   |
+| 0.1894        | 0.39  | 125  | 0.1740          | 0.0       | 0.0    | 0.0    | 0.9637   |
+| 0.1492        | 0.46  | 150  | 0.1454          | 0.1955    | 0.0320 | 0.0550 | 0.9635   |
+| 0.1504        | 0.54  | 175  | 0.1437          | 0.1288    | 0.0139 | 0.0251 | 0.9643   |
+| 0.1559        | 0.62  | 200  | 0.1326          | 0.1795    | 0.1123 | 0.1382 | 0.9665   |
+| 0.1571        | 0.7   | 225  | 0.1406          | 0.3095    | 0.0604 | 0.1010 | 0.9613   |
+| 0.1353        | 0.77  | 250  | 0.1346          | 0.3090    | 0.1750 | 0.2235 | 0.9674   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3