---
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: twitter-roberta-base-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: 0.7045454545454546
    - name: Recall
      type: recall
      value: 0.6303827751196173
    - name: F1
      type: f1
      value: 0.6654040404040403
    - name: Accuracy
      type: accuracy
      value: 0.9639611008707811
---

<!-- 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-WNUT

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1938
- Precision: 0.7045
- Recall: 0.6304
- F1: 0.6654
- Accuracy: 0.9640

## 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: 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   | 0.3912          | 0.0       | 0.0    | 0.0    | 0.9205   |
| No log        | 0.93  | 50   | 0.2847          | 0.25      | 0.0024 | 0.0047 | 0.9209   |
| No log        | 1.39  | 75   | 0.2449          | 0.5451    | 0.3469 | 0.4240 | 0.9426   |
| No log        | 1.85  | 100  | 0.1946          | 0.6517    | 0.4856 | 0.5565 | 0.9492   |
| No log        | 2.31  | 125  | 0.1851          | 0.6921    | 0.5646 | 0.6219 | 0.9581   |
| No log        | 2.78  | 150  | 0.1672          | 0.6867    | 0.5873 | 0.6331 | 0.9594   |
| No log        | 3.24  | 175  | 0.1675          | 0.6787    | 0.5837 | 0.6277 | 0.9615   |
| No log        | 3.7   | 200  | 0.1644          | 0.6765    | 0.6328 | 0.6539 | 0.9638   |
| No log        | 4.17  | 225  | 0.1672          | 0.6997    | 0.6495 | 0.6737 | 0.9640   |
| No log        | 4.63  | 250  | 0.1652          | 0.6915    | 0.6435 | 0.6667 | 0.9649   |
| No log        | 5.09  | 275  | 0.1882          | 0.7067    | 0.6053 | 0.6521 | 0.9629   |
| No log        | 5.56  | 300  | 0.1783          | 0.7128    | 0.6352 | 0.6717 | 0.9645   |
| No log        | 6.02  | 325  | 0.1813          | 0.7011    | 0.6172 | 0.6565 | 0.9639   |
| No log        | 6.48  | 350  | 0.1804          | 0.7139    | 0.6447 | 0.6776 | 0.9647   |
| No log        | 6.94  | 375  | 0.1902          | 0.7218    | 0.6268 | 0.6709 | 0.9641   |
| No log        | 7.41  | 400  | 0.1883          | 0.7106    | 0.6316 | 0.6688 | 0.9641   |
| No log        | 7.87  | 425  | 0.1862          | 0.7067    | 0.6340 | 0.6683 | 0.9643   |
| No log        | 8.33  | 450  | 0.1882          | 0.7053    | 0.6328 | 0.6671 | 0.9639   |
| No log        | 8.8   | 475  | 0.1919          | 0.7055    | 0.6304 | 0.6658 | 0.9638   |
| 0.1175        | 9.26  | 500  | 0.1938          | 0.7045    | 0.6304 | 0.6654 | 0.9640   |
| 0.1175        | 9.72  | 525  | 0.1880          | 0.7025    | 0.6411 | 0.6704 | 0.9646   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1