Text Classification
Transformers
PyTorch
TensorFlow
Rust
Safetensors
English
distilbert
Eval Results (legacy)
Instructions to use HARSHU550/Sentiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HARSHU550/Sentiments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HARSHU550/Sentiments")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HARSHU550/Sentiments") model = AutoModelForSequenceClassification.from_pretrained("HARSHU550/Sentiments") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f5bd24518cc18de2a591e24027100367709bfcb9829ba16749752f1b21cf6da
- Size of remote file:
- 268 MB
- SHA256:
- 7c3919835e442510166d267fe7cbe847e0c51cd26d9ba07b89a57b952b49b8aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.