Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Text Classification
Pytorch
Sentiment_Analysis
Deberta
text-embeddings-inference
Instructions to use RashidNLP/Amazon-Deberta-Base-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RashidNLP/Amazon-Deberta-Base-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RashidNLP/Amazon-Deberta-Base-Sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RashidNLP/Amazon-Deberta-Base-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("RashidNLP/Amazon-Deberta-Base-Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RashidNLP/Amazon-Deberta-Base-Sentiment: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/RashidNLP/Amazon-Deberta-Base-Sentiment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RashidNLP/Amazon-Deberta-Base-Sentiment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RashidNLP/Amazon-Deberta-Base-Sentiment/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- 3b5394683d259c20af0e38c9a79fc24800879957a13893ee37aac0d0a95bc012
- Size of remote file:
- 738 MB
- SHA256:
- 76adad59822a43b4f6d20b309d7aa53278742e19fcb39b46536eefaae70ab9e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.