update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: twitter-roberta-base-dec2021-WNUT
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.70703125
+    - name: Recall
+      type: recall
+      value: 0.6495215311004785
+    - name: F1
+      type: f1
+      value: 0.6770573566084789
+    - name: Accuracy
+      type: accuracy
+      value: 0.9649780715693129
+---
+
+<!-- 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. -->
+
+# twitter-roberta-base-dec2021-WNUT
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2086
+- Precision: 0.7070
+- Recall: 0.6495
+- F1: 0.6771
+- Accuracy: 0.9650
+
+## 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: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 1024
+- 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        | 0.46  | 25   | 0.2818          | 0.0982    | 0.0383 | 0.0551 | 0.9241   |
+| No log        | 0.93  | 50   | 0.2158          | 0.6181    | 0.4569 | 0.5254 | 0.9480   |
+| No log        | 1.39  | 75   | 0.1930          | 0.6682    | 0.5347 | 0.5940 | 0.9555   |
+| No log        | 1.85  | 100  | 0.1728          | 0.6583    | 0.5646 | 0.6079 | 0.9594   |
+| No log        | 2.31  | 125  | 0.1787          | 0.7050    | 0.5718 | 0.6314 | 0.9619   |
+| No log        | 2.78  | 150  | 0.2051          | 0.6979    | 0.5251 | 0.5993 | 0.9587   |
+| No log        | 3.24  | 175  | 0.1755          | 0.7172    | 0.5945 | 0.6501 | 0.9621   |
+| No log        | 3.7   | 200  | 0.1720          | 0.6943    | 0.6304 | 0.6608 | 0.9645   |
+| No log        | 4.17  | 225  | 0.1873          | 0.7203    | 0.6316 | 0.6730 | 0.9646   |
+| No log        | 4.63  | 250  | 0.1781          | 0.6934    | 0.6196 | 0.6545 | 0.9638   |
+| No log        | 5.09  | 275  | 0.1953          | 0.7040    | 0.6172 | 0.6577 | 0.9631   |
+| No log        | 5.56  | 300  | 0.1953          | 0.7223    | 0.6316 | 0.6739 | 0.9642   |
+| No log        | 6.02  | 325  | 0.1839          | 0.7008    | 0.6471 | 0.6729 | 0.9648   |
+| No log        | 6.48  | 350  | 0.1995          | 0.716     | 0.6423 | 0.6772 | 0.9650   |
+| No log        | 6.94  | 375  | 0.2056          | 0.7251    | 0.6184 | 0.6675 | 0.9640   |
+| No log        | 7.41  | 400  | 0.2044          | 0.7065    | 0.6220 | 0.6616 | 0.9640   |
+| No log        | 7.87  | 425  | 0.2042          | 0.7201    | 0.6400 | 0.6776 | 0.9650   |
+| No log        | 8.33  | 450  | 0.2247          | 0.7280    | 0.6244 | 0.6722 | 0.9638   |
+| No log        | 8.8   | 475  | 0.2060          | 0.7064    | 0.6447 | 0.6742 | 0.9649   |
+| 0.0675        | 9.26  | 500  | 0.2152          | 0.7112    | 0.6244 | 0.6650 | 0.9643   |
+| 0.0675        | 9.72  | 525  | 0.2086          | 0.7070    | 0.6495 | 0.6771 | 0.9650   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0
+- Datasets 2.3.2
+- Tokenizers 0.12.1