Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use racro/sentiment-browser-extension with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use racro/sentiment-browser-extension with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="racro/sentiment-browser-extension")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("racro/sentiment-browser-extension") model = AutoModelForSequenceClassification.from_pretrained("racro/sentiment-browser-extension", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4a8592fa0cc15b4f1953da8d24ac16032c8d95b0ac6d29f4f1184675162be2d
- Size of remote file:
- 3.44 kB
- SHA256:
- 4c46be85231fe06d3b85fe7a9e736318c61f92981689f5bc2367dd028f15d4a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.