Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Hemg/twitter-sentiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/twitter-sentiments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hemg/twitter-sentiments")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hemg/twitter-sentiments") model = AutoModelForSequenceClassification.from_pretrained("Hemg/twitter-sentiments", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hemg/twitter-sentiments: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/Hemg/twitter-sentiments/resolve/main/README.md
- Command line
-
hf download hf://Hemg/twitter-sentiments/README.md
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curl -L -o README.md https://huggingface.co/Hemg/twitter-sentiments/resolve/main/README.md
1.38 kB
| base_model: cardiffnlp/twitter-roberta-base-sentiment-latest | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: twitter-sentiments | |
| 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. --> | |
| # twitter-sentiments | |
| This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown 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: 3e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 285 | 0.7545 | 0.7658 | | |
| ### Framework versions | |
| - Transformers 4.39.3 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |