update model card README.md

README.md

@@ -0,0 +1,93 @@
+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: microsoft-deberta-v3-large_ner_wikiann
+  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: 0.8557286258220838
+    - name: Recall
+      type: recall
+      value: 0.8738159196946134
+    - name: F1
+      type: f1
+      value: 0.8646776957783918
+    - name: Accuracy
+      type: accuracy
+      value: 0.9406352438660972
+---
+
+<!-- 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. -->
+
+# microsoft-deberta-v3-large_ner_wikiann
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3108
+- Precision: 0.8557
+- Recall: 0.8738
+- F1: 0.8647
+- Accuracy: 0.9406
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3005        | 1.0   | 1250 | 0.2462          | 0.8205    | 0.8400 | 0.8301 | 0.9294   |
+| 0.1931        | 2.0   | 2500 | 0.2247          | 0.8448    | 0.8630 | 0.8538 | 0.9386   |
+| 0.1203        | 3.0   | 3750 | 0.2341          | 0.8468    | 0.8693 | 0.8579 | 0.9403   |
+| 0.0635        | 4.0   | 5000 | 0.2948          | 0.8596    | 0.8745 | 0.8670 | 0.9411   |
+| 0.0451        | 5.0   | 6250 | 0.3108          | 0.8557    | 0.8738 | 0.8647 | 0.9406   |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.1
+- Tokenizers 0.13.2