Transformers
PyTorch
bart
text2text-generation
question-answering, multi-step-reasoning, multi-hop-reasoning
Instructions to use StonyBrookNLP/bart-large-drop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StonyBrookNLP/bart-large-drop with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("StonyBrookNLP/bart-large-drop") model = AutoModelForSeq2SeqLM.from_pretrained("StonyBrookNLP/bart-large-drop", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Harsh Trivedi commited on
Commit ·
353dbfc
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Parent(s): 79a29e7
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README.md
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Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
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Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
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```python
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# NOTE: This model is only pretrained on TeaBReaC, and not on any real QA dataset.
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
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