Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use manjinder/sentiment_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manjinder/sentiment_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="manjinder/sentiment_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("manjinder/sentiment_test") model = AutoModelForSequenceClassification.from_pretrained("manjinder/sentiment_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6798a63da4874d992a302a07052ef3b016d157d3efbf96f562c15d857d43f4f
- Size of remote file:
- 3.5 kB
- SHA256:
- 1032e82c9c79485fdd0c910d2a8daeeb81e3616282236e1fd085aac2bee80d7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.