update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: test
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.5527638190954773
+    - name: Recall
+      type: recall
+      value: 0.4077849860982391
+    - name: F1
+      type: f1
+      value: 0.46933333333333327
+    - name: Accuracy
+      type: accuracy
+      value: 0.9475439271514685
+---
+
+<!-- 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. -->
+
+# ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3030
+- Precision: 0.5528
+- Recall: 0.4078
+- F1: 0.4693
+- Accuracy: 0.9475
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2577          | 0.5099    | 0.3568 | 0.4198 | 0.9437   |
+| No log        | 2.0   | 426  | 0.2675          | 0.5406    | 0.3948 | 0.4563 | 0.9463   |
+| 0.0737        | 3.0   | 639  | 0.3040          | 0.5737    | 0.3716 | 0.4511 | 0.9465   |
+| 0.0737        | 4.0   | 852  | 0.3042          | 0.5514    | 0.3976 | 0.4620 | 0.9474   |
+| 0.0307        | 5.0   | 1065 | 0.3030          | 0.5528    | 0.4078 | 0.4693 | 0.9475   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3