update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-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: 0.7013977128335451
+    - name: Recall
+      type: recall
+      value: 0.5115848007414272
+    - name: F1
+      type: f1
+      value: 0.5916398713826366
+    - name: Accuracy
+      type: accuracy
+      value: 0.9570402667350603
+---
+
+<!-- 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-large-WNUT-ner
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3570
+- Precision: 0.7014
+- Recall: 0.5116
+- F1: 0.5916
+- Accuracy: 0.9570
+
+## 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  | 0.2223          | 0.5588    | 0.4495 | 0.4982 | 0.9504   |
+| No log        | 2.0   | 426  | 0.2326          | 0.6602    | 0.4430 | 0.5302 | 0.9514   |
+| 0.1516        | 3.0   | 639  | 0.2792          | 0.6846    | 0.4124 | 0.5147 | 0.9520   |
+| 0.1516        | 4.0   | 852  | 0.2417          | 0.6510    | 0.5134 | 0.5741 | 0.9574   |
+| 0.0427        | 5.0   | 1065 | 0.2954          | 0.6850    | 0.4856 | 0.5683 | 0.9544   |
+| 0.0427        | 6.0   | 1278 | 0.3033          | 0.6761    | 0.4893 | 0.5677 | 0.9557   |
+| 0.0427        | 7.0   | 1491 | 0.3502          | 0.7007    | 0.4838 | 0.5724 | 0.9563   |
+| 0.0178        | 8.0   | 1704 | 0.3712          | 0.6995    | 0.4875 | 0.5745 | 0.9563   |
+| 0.0178        | 9.0   | 1917 | 0.3541          | 0.6951    | 0.4986 | 0.5807 | 0.9569   |
+| 0.0068        | 10.0  | 2130 | 0.3570          | 0.7014    | 0.5116 | 0.5916 | 0.9570   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2