Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DuongTrongChi/facebook-commet-classification-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DuongTrongChi/facebook-commet-classification-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DuongTrongChi/facebook-commet-classification-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DuongTrongChi/facebook-commet-classification-base") model = AutoModelForSequenceClassification.from_pretrained("DuongTrongChi/facebook-commet-classification-base") - Notebooks
- Google Colab
- Kaggle
facebook-commet-classification-base
This model is a fine-tuned version of uitnlp/visobert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0642
- Accuracy: 0.9830
- F1: 0.9568
- Precision: 0.9441
- Recall: 0.9698
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0616 | 1.0 | 2376 | 0.0642 | 0.9830 | 0.9568 | 0.9441 | 0.9698 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
- Downloads last month
- 4
Model tree for DuongTrongChi/facebook-commet-classification-base
Base model
uitnlp/visobert