update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- token-classification
+- generated_from_trainer
+datasets:
+- ncbi_disease
+model-index:
+- name: bert-base-cased-finetuned-ner-NCBI_Disease
+  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. -->
+
+# bert-base-cased-finetuned-ner-NCBI_Disease
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0614
+- Disease: {'precision': 0.8063891577928364, 'recall': 0.8677083333333333, 'f1': 0.8359257400903161, 'number': 960}
+- Overall Precision: 0.8064
+- Overall Recall: 0.8677
+- Overall F1: 0.8359
+- Overall Accuracy: 0.9825
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Disease                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0525        | 1.0   | 340  | 0.0617          | {'precision': 0.7813471502590673, 'recall': 0.7854166666666667, 'f1': 0.7833766233766233, 'number': 960} | 0.7813            | 0.7854         | 0.7834     | 0.9796           |
+| 0.022         | 2.0   | 680  | 0.0551          | {'precision': 0.7897240723120837, 'recall': 0.8645833333333334, 'f1': 0.8254599701640976, 'number': 960} | 0.7897            | 0.8646         | 0.8255     | 0.9819           |
+| 0.0154        | 3.0   | 1020 | 0.0614          | {'precision': 0.8063891577928364, 'recall': 0.8677083333333333, 'f1': 0.8359257400903161, 'number': 960} | 0.8064            | 0.8677         | 0.8359     | 0.9825           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3