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: distilbert-base-cased-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9254922831293241
+    - name: Recall
+      type: recall
+      value: 0.9361205813744842
+    - name: F1
+      type: f1
+      value: 0.9307760927743086
+    - name: Accuracy
+      type: accuracy
+      value: 0.9831488785541885
+---
+
+<!-- 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. -->
+
+# distilbert-base-cased-ner
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0966
+- Precision: 0.9255
+- Recall: 0.9361
+- F1: 0.9308
+- Accuracy: 0.9831
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 2147483647
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1045        | 1.0   | 1756 | 0.0891          | 0.8908    | 0.9032 | 0.8970 | 0.9747   |
+| 0.044         | 2.0   | 3512 | 0.0809          | 0.9209    | 0.9175 | 0.9192 | 0.9793   |
+| 0.0253        | 3.0   | 5268 | 0.0806          | 0.9268    | 0.9280 | 0.9274 | 0.9821   |
+| 0.0129        | 4.0   | 7024 | 0.0909          | 0.9301    | 0.9341 | 0.9321 | 0.9829   |
+| 0.0042        | 5.0   | 8780 | 0.0966          | 0.9255    | 0.9361 | 0.9308 | 0.9831   |
+
+
+### Framework versions
+
+- Transformers 4.28.0.dev0
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3