# doc2query/all-with_prefix-t5-base-v1


## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

model_name = 'doc2query/all-with_prefix-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

prefix = "answer2question: "
text = prefix+"Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."

input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt')
outputs = model.generate(
    input_ids=input_ids,
    max_length=64,
    do_sample=True,
    top_p=0.95,
    num_return_sequences=5)

print("Text:")
print(text)

print("\nGenerated Queries:")
for i in range(len(outputs)):
    query = tokenizer.decode(outputs[i], skip_special_tokens=True)
    print(f'{i + 1}: {query}')
```

## Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 575k training steps. For the  training script, see the `train_script.py` in this repository.

The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. 

This model was trained on a large collection of datasets. For the exact datasets names and weights see the `data_config.json` in this repository. Most of the datasets are available at [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers).

The datasets include besides others:
- (title, body) pairs from [Reddit](https://huggingface.co/datasets/sentence-transformers/reddit-title-body)
- (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers!
- (title, review) pairs from Amazon reviews
- (query, paragraph) pairs from MS MARCO, NQ, and GooAQ 
- (question, duplicate_question) from Quora and WikiAnswers
- (title, abstract) pairs from S2ORC

## Prefix

This model was trained **with prefixed**: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different.

E.g. the above text about Python produces the following output:
| Prefix | Output |
| --- | --- |
| answer2question | Why should I use python in my business? ; What is the difference between Python and.NET? ;  what is the python design philosophy? |
| review2title | Python a powerful and useful language ; A new and improved programming language ; Object-oriented, practical and accessibl |
| abstract2title | Python: A Software Development Platform ; A Research Guide for Python X: Conceptual Approach to Programming ; Python : Language and Approach |
| text2query |  is python a low level language? ; what is the primary idea of python? ; is python a programming language? |

These are all available pre-fixes:
- text2reddit
- question2title
- answer2question
- abstract2title
- review2title
- news2title
- text2query
- question2question

For the datasets and weights for the different pre-fixes see `data_config.json` in this repository.