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: ner-deBERTa-v2-x-large
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: test
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7384370015948963
+    - name: Recall
+      type: recall
+      value: 0.7377832861189801
+    - name: F1
+      type: f1
+      value: 0.7381099991143388
+    - name: Accuracy
+      type: accuracy
+      value: 0.9460966943038657
+---
+
+<!-- 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-deBERTa-v2-x-large
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3963
+- Precision: 0.7384
+- Recall: 0.7378
+- F1: 0.7381
+- Accuracy: 0.9461
+
+## 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: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 219  | 0.4082          | 0.6932    | 0.7087 | 0.7009 | 0.9386   |
+| No log        | 2.0   | 439  | 0.4299          | 0.7467    | 0.6948 | 0.7198 | 0.9426   |
+| 0.0094        | 3.0   | 658  | 0.4086          | 0.7435    | 0.7072 | 0.7249 | 0.9441   |
+| 0.0094        | 4.0   | 878  | 0.3873          | 0.7426    | 0.7420 | 0.7423 | 0.9461   |
+| 0.0054        | 4.99  | 1095 | 0.3963          | 0.7384    | 0.7378 | 0.7381 | 0.9461   |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3