Instructions to use prajjwal1/albert-base-v2-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/albert-base-v2-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/albert-base-v2-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/albert-base-v2-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/albert-base-v2-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 479 Bytes
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https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/8e938e505595a833a4e26af669462dbe8d1dab59/README.md
- Command line
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hf download hf://prajjwal1/albert-base-v2-mnli@8e938e505595a833a4e26af669462dbe8d1dab59/README.md
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curl -L -o README.md https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/8e938e505595a833a4e26af669462dbe8d1dab59/README.md
479 Bytes
If you use the model, please consider citing the paper
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Original Implementation and more info can be found in this Github repository.