Instructions to use vicd/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vicd/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vicd/sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vicd/sentiment") model = AutoModelForSequenceClassification.from_pretrained("vicd/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vicd/sentiment: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/vicd/sentiment/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://vicd/sentiment@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vicd/sentiment/resolve/refs%2Fpr%2F1/pytorch_model.bin
501 MB
- Xet hash:
- d06fe6ff71e799f041992b45d5abdb0973fd450980619cbfd88596e656f48a4e
- Size of remote file:
- 501 MB
- SHA256:
- 40c00febd0605745753648ca71a80e9029304056cf0c33840755f1b81e389252
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