Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mpreda/test_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpreda/test_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpreda/test_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpreda/test_dir") model = AutoModelForSequenceClassification.from_pretrained("mpreda/test_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: cardiffnlp/twitter-roberta-base-hate | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: test_dir | |
| results: [] | |
| <!-- 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. --> | |
| # test_dir | |
| This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-hate](https://huggingface.co/cardiffnlp/twitter-roberta-base-hate) on the None dataset. | |
| ## 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: 64 | |
| - eval_batch_size: 64 | |
| - 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: cosine | |
| - num_epochs: 1 | |
| - label_smoothing_factor: 0.1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | No log | 1.0 | 35 | 0.7781 | 0.7913 | 0.2529 | 0.3241 | 0.2679 | | |
| ### Framework versions | |
| - Transformers 4.57.3 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 | |