Instructions to use MarkKisker/RoBERTa-base-RottenTomatoes_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarkKisker/RoBERTa-base-RottenTomatoes_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarkKisker/RoBERTa-base-RottenTomatoes_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MarkKisker/RoBERTa-base-RottenTomatoes_v2") model = AutoModelForSequenceClassification.from_pretrained("MarkKisker/RoBERTa-base-RottenTomatoes_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tmp-checkpoint-1067/optimizer.pt from MarkKisker/RoBERTa-base-RottenTomatoes_v2: direct link, hf CLI and curl.
- Browser
- Download file 997 MB
-
https://huggingface.co/MarkKisker/RoBERTa-base-RottenTomatoes_v2/resolve/main/tmp-checkpoint-1067/optimizer.pt
- Command line
-
hf download hf://MarkKisker/RoBERTa-base-RottenTomatoes_v2/tmp-checkpoint-1067/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/MarkKisker/RoBERTa-base-RottenTomatoes_v2/resolve/main/tmp-checkpoint-1067/optimizer.pt
997 MB
- Xet hash:
- df422a95d4f7a9c09c7ed7515875e463050f99493dcf223c1accb1b6dc9a2375
- Size of remote file:
- 997 MB
- SHA256:
- fbac00785a0ee4936130c3d53f6930d52380220f3003afa61236cbb22e39fa55
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