update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-cased-finetuned-conll2003
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9409771754636234
+    - name: Recall
+      type: recall
+      value: 0.946886775524852
+    - name: F1
+      type: f1
+      value: 0.9439227260531259
+    - name: Accuracy
+      type: accuracy
+      value: 0.9859745687878966
+---
+
+<!-- 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-conll2003
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0643
+- Precision: 0.9410
+- Recall: 0.9469
+- F1: 0.9439
+- Accuracy: 0.9860
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2349        | 0.57  | 500  | 0.0885          | 0.8957    | 0.8980 | 0.8968 | 0.9747   |
+| 0.0822        | 1.14  | 1000 | 0.0774          | 0.9184    | 0.9219 | 0.9202 | 0.9802   |
+| 0.0476        | 1.71  | 1500 | 0.0683          | 0.9345    | 0.9325 | 0.9335 | 0.9833   |
+| 0.0368        | 2.28  | 2000 | 0.0653          | 0.9333    | 0.9430 | 0.9381 | 0.9847   |
+| 0.028         | 2.85  | 2500 | 0.0670          | 0.9279    | 0.9342 | 0.9311 | 0.9835   |
+| 0.0171        | 3.42  | 3000 | 0.0643          | 0.9410    | 0.9469 | 0.9439 | 0.9860   |
+| 0.0149        | 3.99  | 3500 | 0.0667          | 0.9369    | 0.9477 | 0.9422 | 0.9856   |
+| 0.0088        | 4.56  | 4000 | 0.0698          | 0.9360    | 0.9473 | 0.9416 | 0.9855   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1