Instructions to use oscarv1114/tweeteval-emotion-bertweet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oscarv1114/tweeteval-emotion-bertweet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oscarv1114/tweeteval-emotion-bertweet")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oscarv1114/tweeteval-emotion-bertweet") model = AutoModelForSequenceClassification.from_pretrained("oscarv1114/tweeteval-emotion-bertweet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tweeteval-emotion-bertweet
This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5079
- F1 Macro: 0.7954
- Accuracy: 0.8396
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
|---|---|---|---|---|---|
| 0.8462 | 1.0 | 102 | 0.7151 | 0.7188 | 0.7888 |
| 0.5798 | 2.0 | 204 | 0.6028 | 0.7452 | 0.7995 |
| 0.4173 | 3.0 | 306 | 0.5082 | 0.7857 | 0.8396 |
| 0.3657 | 4.0 | 408 | 0.4952 | 0.7987 | 0.8476 |
| 0.2633 | 5.0 | 510 | 0.5079 | 0.7954 | 0.8396 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for oscarv1114/tweeteval-emotion-bertweet
Base model
vinai/bertweet-base