update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- jnlpba
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: scibert-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: 0.6737190414118119
+    - name: Recall
+      type: recall
+      value: 0.7756869083352574
+    - name: F1
+      type: f1
+      value: 0.7211161792326267
+    - name: Accuracy
+      type: accuracy
+      value: 0.9226268866380928
+---
+
+<!-- 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. -->
+
+# scibert-finetuned-ner
+
+This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the jnlpba dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4717
+- Precision: 0.6737
+- Recall: 0.7757
+- F1: 0.7211
+- Accuracy: 0.9226
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1608        | 1.0   | 2319  | 0.2431          | 0.6641    | 0.7581 | 0.7080 | 0.9250   |
+| 0.103         | 2.0   | 4638  | 0.2916          | 0.6739    | 0.7803 | 0.7232 | 0.9228   |
+| 0.0659        | 3.0   | 6957  | 0.3662          | 0.6796    | 0.7624 | 0.7186 | 0.9233   |
+| 0.0393        | 4.0   | 9276  | 0.4222          | 0.6737    | 0.7771 | 0.7217 | 0.9225   |
+| 0.025         | 5.0   | 11595 | 0.4717          | 0.6737    | 0.7757 | 0.7211 | 0.9226   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1