Instructions to use Esteban-Ospina/bertweet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Esteban-Ospina/bertweet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Esteban-Ospina/bertweet")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Esteban-Ospina/bertweet") model = AutoModelForSequenceClassification.from_pretrained("Esteban-Ospina/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: 0.5312
- F1 Macro: 0.7885
- Accuracy: 0.8342
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 |
|---|---|---|---|---|---|
| 0.8654 | 1.0 | 102 | 0.7416 | 0.6915 | 0.7727 |
| 0.5960 | 2.0 | 204 | 0.5872 | 0.7534 | 0.8102 |
| 0.4344 | 3.0 | 306 | 0.5206 | 0.7702 | 0.8209 |
| 0.3408 | 4.0 | 408 | 0.5314 | 0.7691 | 0.8209 |
| 0.2563 | 5.0 | 510 | 0.5312 | 0.7885 | 0.8342 |
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 Esteban-Ospina/bertweet
Base model
vinai/bertweet-base