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---
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
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