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-5335/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-5335/optimizer.pt
- Command line
-
hf download hf://MarkKisker/RoBERTa-base-RottenTomatoes_v2/tmp-checkpoint-5335/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/MarkKisker/RoBERTa-base-RottenTomatoes_v2/resolve/main/tmp-checkpoint-5335/optimizer.pt
997 MB
- Xet hash:
- c08426fba43c298f9cd5a163b6a437f2d577d628549de731519b8f39e1bef3d8
- Size of remote file:
- 997 MB
- SHA256:
- a00b3430444af28b01ff1697208947f64ba5572af2b75c6bedea11307a2affae
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