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
File size: 479 Bytes
eabf43d | 1 2 3 4 5 6 7 8 9 10 11 12 | 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](https://github.com/prajjwal1/generalize_lm_nli). |