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: bert-small-finetuned-xglue-ner-longer20
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: train
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.5782747603833865
+    - name: Recall
+      type: recall
+      value: 0.43301435406698563
+    - name: F1
+      type: f1
+      value: 0.4952120383036936
+    - name: Accuracy
+      type: accuracy
+      value: 0.92613831861452
+---
+
+<!-- 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-small-finetuned-xglue-ner-longer20
+
+This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer10](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner-longer10) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5839
+- Precision: 0.5783
+- Recall: 0.4330
+- F1: 0.4952
+- Accuracy: 0.9261
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 425  | 0.4882          | 0.5       | 0.4510 | 0.4742 | 0.9228   |
+| 0.013         | 2.0   | 850  | 0.5039          | 0.5130    | 0.4246 | 0.4647 | 0.9242   |
+| 0.0124        | 3.0   | 1275 | 0.5331          | 0.5506    | 0.4426 | 0.4907 | 0.9256   |
+| 0.0159        | 4.0   | 1700 | 0.5403          | 0.5488    | 0.4234 | 0.4781 | 0.9249   |
+| 0.0132        | 5.0   | 2125 | 0.5459          | 0.5714    | 0.4211 | 0.4848 | 0.9255   |
+| 0.0117        | 6.0   | 2550 | 0.5522          | 0.5637    | 0.4342 | 0.4905 | 0.9253   |
+| 0.0117        | 7.0   | 2975 | 0.5712          | 0.5778    | 0.4354 | 0.4966 | 0.9257   |
+| 0.007         | 8.0   | 3400 | 0.5860          | 0.5828    | 0.4378 | 0.5    | 0.9259   |
+| 0.0066        | 9.0   | 3825 | 0.5745          | 0.5703    | 0.4462 | 0.5007 | 0.9259   |
+| 0.0049        | 10.0  | 4250 | 0.5839          | 0.5783    | 0.4330 | 0.4952 | 0.9261   |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1