Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use GhylB/Sentiment_Analysis_RoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhylB/Sentiment_Analysis_RoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GhylB/Sentiment_Analysis_RoBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GhylB/Sentiment_Analysis_RoBERTa") model = AutoModelForSequenceClassification.from_pretrained("GhylB/Sentiment_Analysis_RoBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from GhylB/Sentiment_Analysis_RoBERTa: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
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https://huggingface.co/GhylB/Sentiment_Analysis_RoBERTa/resolve/main/README.md
- Command line
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hf download hf://GhylB/Sentiment_Analysis_RoBERTa/README.md
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curl -L -o README.md https://huggingface.co/GhylB/Sentiment_Analysis_RoBERTa/resolve/main/README.md
1.67 kB
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Sentiment_Analysis_RoBERTa | |
| 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. --> | |
| # Sentiment_Analysis_RoBERTa | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5934 | |
| - Rmse: 0.6311 | |
| ## 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: 3e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rmse | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.7173 | 2.0 | 500 | 0.5934 | 0.6311 | | |
| | 0.4139 | 4.0 | 1000 | 0.6405 | 0.6015 | | |
| | 0.1956 | 6.0 | 1500 | 0.8526 | 0.6122 | | |
| | 0.0997 | 8.0 | 2000 | 1.1684 | 0.6089 | | |
| | 0.0569 | 10.0 | 2500 | 1.2575 | 0.5986 | | |
| ### Framework versions | |
| - Transformers 4.29.0 | |
| - Pytorch 2.0.0+cu118 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |