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-classification
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.5421686746987951
+    - name: Recall
+      type: recall
+      value: 0.3336422613531047
+    - name: F1
+      type: f1
+      value: 0.41308089500860584
+    - name: Accuracy
+      type: accuracy
+      value: 0.9439100508742679
+---
+
+<!-- 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-classification
+
+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: 0.2699
+- Precision: 0.5422
+- Recall: 0.3336
+- F1: 0.4131
+- Accuracy: 0.9439
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2797          | 0.5105    | 0.2484 | 0.3342 | 0.9386   |
+| No log        | 2.0   | 426  | 0.2636          | 0.5493    | 0.3151 | 0.4005 | 0.9430   |
+| 0.1938        | 3.0   | 639  | 0.2699          | 0.5422    | 0.3336 | 0.4131 | 0.9439   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu102
+- Datasets 2.4.0
+- Tokenizers 0.12.1