
# NT5, a T5 model trained to perform numerical reasoning

T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). 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.

Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.

## Model description

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 the Discrete Reasoning over Text (DROP) dataset.

## Intended uses & limitations

You can use the model for numerical reasoning over text. 

### How to use

Here is how to use this model:

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

context = """Saint Jean de Brébeuf was a French Jesuit missionary who
travelled to New France in 1625. There he worked primarily with the Huron
for the rest of his life, except for a few years in France from 1629 to
1633. He learned their language and culture, writing extensively about
each to aid other missionaries. In 1649, Br´ebeuf and another missionary
were captured when an Iroquois raid took over a Huron village . Together
with Huron captives, the missionaries were ritually tortured and killed
on March 16, 1649. Br´ebeuf was beatified in 1925 and among eight Jesuit
missionaries canonized as saints in the Roman Catholic Church in 1930."""

question = "How many years did Saint Jean de Brébeuf stay in New France 
before he went back to France for a few years?"

tokenizer = T5Tokenizer.from_pretrained("nielsr/nt5-small-rc1")
model = T5ForConditionalGeneration.from_pretrained("nielsr/nt5-small-rc1")

# encode context & question
input_text = f"answer_me: {question} context: {context}"
encoded_query = tokenizer(
                    input_text, 
                    return_tensors='pt', 
                    padding='max_length', 
                    truncation=True, 
                    max_length=512)

# generate answer
generated_answer = model.generate(input_ids=encoded_query["input_ids"], 
                                  attention_mask=encoded_query["attention_mask"], 
                                  max_length=54)

decoded_answer = tokenizer.decode(generated_answer.numpy()[0])
print("T5 Answer: ", decoded_answer)
T5 Answer: 4
```

## Evaluation results

This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP. 


### BibTeX entry and citation info

```bibtex
@misc{yang2021nt5,
      title={NT5?! Training T5 to Perform Numerical Reasoning}, 
      author={Peng-Jian Yang and Ying Ting Chen and Yuechan Chen and Daniel Cer},
      year={2021},
      eprint={2104.07307},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

```bibtex
@article{DBLP:journals/corr/abs-1903-00161,
  author    = {Dheeru Dua and
               Yizhong Wang and
               Pradeep Dasigi and
               Gabriel Stanovsky and
               Sameer Singh and
               Matt Gardner},
  title     = {{DROP:} {A} Reading Comprehension Benchmark Requiring Discrete Reasoning
               Over Paragraphs},
  journal   = {CoRR},
  volume    = {abs/1903.00161},
  year      = {2019},
  url       = {http://arxiv.org/abs/1903.00161},
  archivePrefix = {arXiv},
  eprint    = {1903.00161},
  timestamp = {Wed, 03 Jul 2019 07:17:04 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
a service of Schloss Dagstuhl - Leibniz Center for Informatics\\\\thomebrowsesearchabout

```
{"license": "apache-2.0", "datasets": ["drop"]}
transformers pytorch jax t5 text2text-generation dataset:drop arxiv:2104.07307 arxiv:1903.00161 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-drop #arxiv-2104.07307 #arxiv-1903.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# NT5, a T5 model trained to perform numerical reasoning

T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. 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.

Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.

## Model description

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 the Discrete Reasoning over Text (DROP) dataset.

## Intended uses & limitations

You can use the model for numerical reasoning over text. 

### How to use

Here is how to use this model:



## Evaluation results

This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP. 


### BibTeX entry and citation info




# NT5, a T5 model trained to perform numerical reasoning

T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. 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.

Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me. ## Model description

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 the Discrete Reasoning over Text (DROP) dataset. ## Intended uses & limitations

You can use the model for numerical reasoning over text. ### How to use

Here is how to use this model: ## Evaluation results

This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-drop #arxiv-2104.07307 #arxiv-1903.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # NT5, a T5 model trained to perform numerical reasoning

T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. 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.

Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me. ## Model description

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 the Discrete Reasoning over Text (DROP) dataset. ## Intended uses & limitations

You can use the model for numerical reasoning over text. ### How to use

Here is how to use this model: ## Evaluation results

This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP. ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-drop #arxiv-2104.07307 #arxiv-1903.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# NT5, a T5 model trained to perform numerical reasoning

T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. 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.

Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.## Model description

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 the Discrete Reasoning over Text (DROP) dataset.## Intended uses & limitations

You can use the model for numerical reasoning over text.### How to use

Here is how to use this model:## Evaluation results

This model achieves an F1 score of 0.7031 and exact match of 0.6687 on the development set of DROP.### BibTeX entry and citation info