First draft of model card
Browse files
README.md
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---
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license: apache-2.0
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tags:
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datasets:
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- drop
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---
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# NT5, a T5 model trained to perform numerical reasoning
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T5-small model pre-trained on and fine-tuned on DROP. It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model.
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Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.
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## Model description
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The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on Discrete Reasoning over Text (DROP) dataset.
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## Intended uses & limitations
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You can use the model for numerical reasoning over text.
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### How to use
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Here is how to use this model:
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```python
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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context = """Saint Jean de Brébeuf was a French Jesuit missionary who
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travelled to New France in 1625. There he worked primarily with the Huron
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for the rest of his life, except for a few years in France from 1629 to
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1633. He learned their language and culture, writing extensively about
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each to aid other missionaries. In 1649, Br´ebeuf and another missionary
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were captured when an Iroquois raid took over a Huron village . Together
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with Huron captives, the missionaries were ritually tortured and killed
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on March 16, 1649. Br´ebeuf was beatified in 1925 and among eight Jesuit
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missionaries canonized as saints in the Roman Catholic Church in 1930."""
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question = "How many years did Saint Jean de Brébeuf stay in New France
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before he went back to France for a few years?"
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tokenizer = T5Tokenizer.from_pretrained("nielsr/nt5-small-finetuned-drop")
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model = T5ForConditionalGeneration.from_pretrained("nielsr/nt5-small-finetuned-drop")
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# encode context & question
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input_text = f"answer_me: {question} context: {context}"
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encoded_query = tokenizer(
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input_text,
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return_tensors='pt',
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padding='max_length',
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truncation=True,
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max_length=512)
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# generate answer
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generated_answer = model.generate(input_ids=encoded_query["input_ids"],
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attention_mask=encoded_query["attention_mask"],
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max_length=54)
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decoded_answer = tokenizer.decode(generated_answer.numpy()[0])
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print("T5 Answer: ", decoded_answer)
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T5 Answer: 4
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```
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## Evaluation results
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This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP.
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### BibTeX entry and citation info
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```bibtex
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@misc{yang2021nt5,
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title={NT5?! Training T5 to Perform Numerical Reasoning},
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author={Peng-Jian Yang and Ying Ting Chen and Yuechan Chen and Daniel Cer},
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year={2021},
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eprint={2104.07307},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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```bibtex
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@article{DBLP:journals/corr/abs-1903-00161,
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author = {Dheeru Dua and
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Yizhong Wang and
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Pradeep Dasigi and
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Gabriel Stanovsky and
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Sameer Singh and
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Matt Gardner},
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title = {{DROP:} {A} Reading Comprehension Benchmark Requiring Discrete Reasoning
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Over Paragraphs},
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journal = {CoRR},
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volume = {abs/1903.00161},
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year = {2019},
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url = {http://arxiv.org/abs/1903.00161},
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archivePrefix = {arXiv},
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eprint = {1903.00161},
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timestamp = {Wed, 03 Jul 2019 07:17:04 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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a service of Schloss Dagstuhl - Leibniz Center for Informatics homebrowsesearchabout
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```
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