update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- jnlpba
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: pubmedbert-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: jnlpba
+      type: jnlpba
+      config: jnlpba
+      split: train
+      args: jnlpba
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6877153861747415
+    - name: Recall
+      type: recall
+      value: 0.7833063957515586
+    - name: F1
+      type: f1
+      value: 0.7324050086355786
+    - name: Accuracy
+      type: accuracy
+      value: 0.926729986431479
+---
+
+<!-- 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-finetuned-ner
+
+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 jnlpba dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3766
+- Precision: 0.6877
+- Recall: 0.7833
+- F1: 0.7324
+- Accuracy: 0.9267
+
+## 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 | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1607        | 1.0   | 2319  | 0.2241          | 0.6853    | 0.7835 | 0.7311 | 0.9302   |
+| 0.112         | 2.0   | 4638  | 0.2620          | 0.6753    | 0.7929 | 0.7294 | 0.9276   |
+| 0.0785        | 3.0   | 6957  | 0.3014          | 0.6948    | 0.7731 | 0.7319 | 0.9268   |
+| 0.055         | 4.0   | 9276  | 0.3526          | 0.6898    | 0.7801 | 0.7322 | 0.9268   |
+| 0.0418        | 5.0   | 11595 | 0.3766          | 0.6877    | 0.7833 | 0.7324 | 0.9267   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1