Instructions to use harshith20/Emotion_predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshith20/Emotion_predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harshith20/Emotion_predictor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harshith20/Emotion_predictor") model = AutoModelForSequenceClassification.from_pretrained("harshith20/Emotion_predictor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harshith20/Emotion_predictor: direct link, hf CLI and curl.
- Browser
- Download file 99.8 MB
-
https://huggingface.co/harshith20/Emotion_predictor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harshith20/Emotion_predictor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harshith20/Emotion_predictor/resolve/main/pytorch_model.bin
99.8 MB
- Xet hash:
- 8db8ac957b5c116bfee67a80e7721b706df8d8ed8043e556e630d1bd2c96d8b2
- Size of remote file:
- 99.8 MB
- SHA256:
- d7a58ca43876c242dd9c203cce1df928f8bb985cfff59d8751ecc06f7808841f
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