Instructions to use xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector") model = AutoModelForSequenceClassification.from_pretrained("xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector/resolve/main/pytorch_model.bin
- Command line
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hf download hf://xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/xiazeng/sciverbinary-model_train_dev_data-robertal-label-neutral_detector/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- bc6a6f864c07af55c81e7428273830e01cdb479ea3207754c86aaa26a3d118bd
- Size of remote file:
- 1.42 GB
- SHA256:
- f3941944c6d464908af3ebecbc7c77acc2477cec00c8e28daa520f8638a6bd16
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