update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- xglue
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-tiny-finetuned-xglue-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: xglue
+      type: xglue
+      config: ner
+      split: train
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.630759453447728
+    - name: Recall
+      type: recall
+      value: 0.6681252103668799
+    - name: F1
+      type: f1
+      value: 0.6489048708728343
+    - name: Accuracy
+      type: accuracy
+      value: 0.9274310133922189
+---
+
+<!-- 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-tiny-finetuned-xglue-ner
+
+This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2489
+- Precision: 0.6308
+- Recall: 0.6681
+- F1: 0.6489
+- Accuracy: 0.9274
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4082        | 1.0   | 1756 | 0.3326          | 0.5600    | 0.5798 | 0.5697 | 0.9118   |
+| 0.2974        | 2.0   | 3512 | 0.2635          | 0.6143    | 0.6562 | 0.6346 | 0.9248   |
+| 0.2741        | 3.0   | 5268 | 0.2489          | 0.6308    | 0.6681 | 0.6489 | 0.9274   |
+
+
+### Framework versions
+
+- Transformers 4.21.0
+- Pytorch 1.12.0+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1