Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Jedida/tweet_sentiments_analysis_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/tweet_sentiments_analysis_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/tweet_sentiments_analysis_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/tweet_sentiments_analysis_roberta") model = AutoModelForSequenceClassification.from_pretrained("Jedida/tweet_sentiments_analysis_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jedida/tweet_sentiments_analysis_roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Jedida/tweet_sentiments_analysis_roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jedida/tweet_sentiments_analysis_roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jedida/tweet_sentiments_analysis_roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 20bd5c243256fe01557b2a2a777ed818d2ad72548aba448188448cdf5f09856c
- Size of remote file:
- 499 MB
- SHA256:
- ea5a945f35212a058310fe5f7d859460351df3b9c1618c5d39d1849217ec75b2
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