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:
- 141e2e89dc26acc2db3eac67e9675d6c1bf394b233208c314763a7ce64a2f779
- Size of remote file:
- 268 MB
- SHA256:
- 9db97da21b97a5e6db1212ce6a810a0c5e22c99daefe3355bae2117f78a0abb9
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