
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.

The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Web Questions (WQ)](https://huggingface.co/datasets/web_questions).

**Note**: The model was fine-tuned on 100% of the train splits of [Web Questions (WQ)](https://huggingface.co/datasets/web_questions) for 10k steps.

Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)

Paper: [How Much Knowledge Can You Pack
Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)

Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* 


## Results on Web Questions - Test Set

|Id | link | Exact Match  |
|---|---|---|
|T5-11b|https://huggingface.co/google/t5-11b-ssm-wq|44.7|
|**T5-xxl**|**https://huggingface.co/google/t5-xxl-ssm-wq**|**43.5**|

## Usage

The model can be used as follows for **closed book question answering**:

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-xxl-ssm-wq")
t5_tok = AutoTokenizer.from_pretrained("google/t5-xxl-ssm-wq")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))
```

## Abstract

It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at https://goo.gle/t5-cbqa.

![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/how_much_know_ledge_image.png)
{"language": "en", "license": "apache-2.0", "datasets": ["c4", "wikipedia", "web_questions"]}
transformers pytorch tf t5 text2text-generation en dataset:c4 dataset:wikipedia dataset:web_questions arxiv:2002.08909 arxiv:1910.10683 license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

Google's T5 for Closed Book Question Answering.


The model was pre-trained using T5's denoising objective on C4, subsequently additionally pre-trained using REALM's salient span masking objective on Wikipedia, and finally fine-tuned on Web Questions (WQ).


Note: The model was fine-tuned on 100% of the train splits of Web Questions (WQ) for 10k steps.


Other community Checkpoints: here


Paper: How Much Knowledge Can You Pack
Into the Parameters of a Language Model?


Authors: *Adam Roberts, Colin Raffel, Noam Shazeer*


Results on Web Questions - Test Set
-----------------------------------


Id: T5-11b, link: URL, Exact Match: 
Id: T5-xxl, link: URL, Exact Match: 


Usage
-----


The model can be used as follows for closed book question answering:


Abstract
--------


It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models at URL


!model image



TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-c4 #dataset-wikipedia #dataset-web_questions #arxiv-2002.08909 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
