Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use will702/stockbit-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use will702/stockbit-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="will702/stockbit-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("will702/stockbit-sentiment") model = AutoModelForSequenceClassification.from_pretrained("will702/stockbit-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d027b421e24edcf2eefe7062c0197430ba3d8eb0bad8853c2a406aea3eaa174
- Size of remote file:
- 178 kB
- SHA256:
- 7b2f2f57464d4c52cc005cc10bef901c9a12de7962a77396de5b03ede5b7dd14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.