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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: 0.7111716621253406
- name: Recall
type: recall
value: 0.6244019138755981
- name: F1
type: f1
value: 0.664968152866242
- name: Accuracy
type: accuracy
value: 0.9642789042140724
---
<!-- 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: 0.2152
- Precision: 0.7112
- Recall: 0.6244
- F1: 0.6650
- Accuracy: 0.9643
## 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
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