Instructions to use Franco9891/bertweet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Franco9891/bertweet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Franco9891/bertweet")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Franco9891/bertweet") model = AutoModelForSequenceClassification.from_pretrained("Franco9891/bertweet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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: 0.5516
- F1 Macro: 0.7736
- Accuracy: 0.8235
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
|---|---|---|---|---|---|
| 0.8817 | 1.0 | 102 | 0.7454 | 0.7008 | 0.7834 |
| 0.5917 | 2.0 | 204 | 0.5798 | 0.7592 | 0.8235 |
| 0.4386 | 3.0 | 306 | 0.5614 | 0.7565 | 0.8075 |
| 0.3474 | 4.0 | 408 | 0.5406 | 0.7696 | 0.8209 |
| 0.2714 | 5.0 | 510 | 0.5516 | 0.7736 | 0.8235 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.8.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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Model tree for Franco9891/bertweet
Base model
vinai/bertweet-base