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-base-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9563020492186769
+    - name: Recall
+      type: recall
+      value: 0.9652436720816018
+    - name: F1
+      type: f1
+      value: 0.9607520564042303
+    - name: Accuracy
+      type: accuracy
+      value: 0.9899205302077261
+---
+
+<!-- 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-base-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: 0.0501
+- Precision: 0.9563
+- Recall: 0.9652
+- F1: 0.9608
+- Accuracy: 0.9899
+
+## 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: 5e-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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1419        | 1.0   | 878  | 0.0628          | 0.9290    | 0.9288 | 0.9289 | 0.9835   |
+| 0.0379        | 2.0   | 1756 | 0.0466          | 0.9456    | 0.9567 | 0.9511 | 0.9878   |
+| 0.0176        | 3.0   | 2634 | 0.0473          | 0.9539    | 0.9575 | 0.9557 | 0.9890   |
+| 0.0098        | 4.0   | 3512 | 0.0468          | 0.9570    | 0.9635 | 0.9603 | 0.9896   |
+| 0.0043        | 5.0   | 4390 | 0.0501          | 0.9563    | 0.9652 | 0.9608 | 0.9899   |
+
+
+### Framework versions
+
+- Transformers 4.11.3
+- Pytorch 1.9.0+cu111
+- Datasets 1.12.1
+- Tokenizers 0.10.3