update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: deberta-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9577488309953239
+    - name: Recall
+      type: recall
+      value: 0.9651632446987546
+    - name: F1
+      type: f1
+      value: 0.961441743503772
+    - name: Accuracy
+      type: accuracy
+      value: 0.9907182964622135
+---
+
+<!-- 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. -->
+
+# deberta-finetuned-ner
+
+This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0515
+- Precision: 0.9577
+- Recall: 0.9652
+- F1: 0.9614
+- Accuracy: 0.9907
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0742        | 1.0   | 1756 | 0.0526          | 0.9390    | 0.9510 | 0.9450 | 0.9868   |
+| 0.0374        | 2.0   | 3512 | 0.0528          | 0.9421    | 0.9554 | 0.9487 | 0.9879   |
+| 0.0205        | 3.0   | 5268 | 0.0505          | 0.9505    | 0.9636 | 0.9570 | 0.9900   |
+| 0.0089        | 4.0   | 7024 | 0.0528          | 0.9531    | 0.9636 | 0.9583 | 0.9898   |
+| 0.0076        | 5.0   | 8780 | 0.0515          | 0.9577    | 0.9652 | 0.9614 | 0.9907   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1