update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: deberta-finetuned-ner-connll-late-stop
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: en
+      split: train
+      args: en
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.830192600803658
+    - name: Recall
+      type: recall
+      value: 0.8470945850417079
+    - name: F1
+      type: f1
+      value: 0.8385584324702589
+    - name: Accuracy
+      type: accuracy
+      value: 0.9228861596598961
+---
+
+<!-- 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-connll-late-stop
+
+This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5259
+- Precision: 0.8302
+- Recall: 0.8471
+- F1: 0.8386
+- Accuracy: 0.9229
+
+## 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: 7
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3408        | 1.0   | 1875  | 0.3639          | 0.7462    | 0.7887 | 0.7669 | 0.8966   |
+| 0.2435        | 2.0   | 3750  | 0.2933          | 0.8104    | 0.8332 | 0.8217 | 0.9178   |
+| 0.1822        | 3.0   | 5625  | 0.3034          | 0.8147    | 0.8388 | 0.8266 | 0.9221   |
+| 0.1402        | 4.0   | 7500  | 0.3667          | 0.8275    | 0.8474 | 0.8374 | 0.9235   |
+| 0.1013        | 5.0   | 9375  | 0.4290          | 0.8285    | 0.8448 | 0.8366 | 0.9227   |
+| 0.0677        | 6.0   | 11250 | 0.4914          | 0.8259    | 0.8473 | 0.8365 | 0.9231   |
+| 0.0439        | 7.0   | 13125 | 0.5259          | 0.8302    | 0.8471 | 0.8386 | 0.9229   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1