Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use duynhatran/roberta-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duynhatran/roberta-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="duynhatran/roberta-train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("duynhatran/roberta-train") model = AutoModelForSequenceClassification.from_pretrained("duynhatran/roberta-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from duynhatran/roberta-train: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/duynhatran/roberta-train/resolve/main/README.md
- Command line
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hf download hf://duynhatran/roberta-train/README.md
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curl -L -o README.md https://huggingface.co/duynhatran/roberta-train/resolve/main/README.md
1.66 kB
| license: mit | |
| base_model: roberta-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: roberta-train | |
| 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. --> | |
| # roberta-train | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2541 | |
| - Accuracy: 0.9062 | |
| - F1: 0.9372 | |
| ## 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-06 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 1.0 | 190 | 0.4252 | 0.7312 | 0.8448 | | |
| | No log | 2.0 | 380 | 0.2966 | 0.8688 | 0.9106 | | |
| | 0.4762 | 3.0 | 570 | 0.2884 | 0.8875 | 0.9224 | | |
| | 0.4762 | 4.0 | 760 | 0.2458 | 0.9125 | 0.9421 | | |
| | 0.4762 | 5.0 | 950 | 0.2541 | 0.9062 | 0.9372 | | |
| ### Framework versions | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |