update model card README.md

README.md

@@ -0,0 +1,98 @@
+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-base-WNUT-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: test
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6251511487303507
+    - name: Recall
+      type: recall
+      value: 0.47914735866543096
+    - name: F1
+      type: f1
+      value: 0.5424973767051418
+    - name: Accuracy
+      type: accuracy
+      value: 0.952295460374455
+---
+
+<!-- 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. -->
+
+# xlm-roberta-base-WNUT-ner
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3376
+- Precision: 0.6252
+- Recall: 0.4791
+- F1: 0.5425
+- Accuracy: 0.9523
+
+## 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: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2787          | 0.5650    | 0.3383 | 0.4232 | 0.9418   |
+| No log        | 2.0   | 426  | 0.2535          | 0.6225    | 0.4004 | 0.4873 | 0.9485   |
+| 0.177         | 3.0   | 639  | 0.2773          | 0.6594    | 0.3911 | 0.4910 | 0.9497   |
+| 0.177         | 4.0   | 852  | 0.2651          | 0.6098    | 0.4708 | 0.5314 | 0.9526   |
+| 0.0551        | 5.0   | 1065 | 0.3076          | 0.6026    | 0.4652 | 0.5251 | 0.9514   |
+| 0.0551        | 6.0   | 1278 | 0.3031          | 0.6343    | 0.4662 | 0.5374 | 0.9531   |
+| 0.0551        | 7.0   | 1491 | 0.3319          | 0.6336    | 0.4680 | 0.5384 | 0.9523   |
+| 0.0276        | 8.0   | 1704 | 0.3430          | 0.6508    | 0.4560 | 0.5362 | 0.9526   |
+| 0.0276        | 9.0   | 1917 | 0.3342          | 0.6138    | 0.4773 | 0.5370 | 0.9521   |
+| 0.0157        | 10.0  | 2130 | 0.3376          | 0.6252    | 0.4791 | 0.5425 | 0.9523   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2