Instructions to use textattack/distilbert-base-uncased-rotten-tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-uncased-rotten-tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-rotten-tomatoes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-rotten-tomatoes") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-rotten-tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/distilbert-base-uncased-rotten-tomatoes: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/textattack/distilbert-base-uncased-rotten-tomatoes/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/distilbert-base-uncased-rotten-tomatoes/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/distilbert-base-uncased-rotten-tomatoes/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- f6de8feec4398633837fbf1c8e5da064397f600247f09edb162883a2e7da4c81
- Size of remote file:
- 268 MB
- SHA256:
- 451c65e1a0fc3e30626ec729b07df57d7352946c658fe1400261eb20701fc0f2
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