license: mit
datasets:
- sberquad
- adversarial_qa
language:
- en
- ru
metrics:
- rouge
pipeline_tag: text2text-generation
Model Card for mTk-QA_SQuAD-en_SQAD_ru
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.
You can find detailed information on Project Github & the referenced paper.
Model Details
Model Description
- Developed by: Michal Stefanik & Marek Kadlcik, Masaryk University
- Model type: [mt5]
- Language(s) (NLP): en,ru
- License: MIT
- Finetuned from model [optional]: google/mt5-large
Model Sources
- Repository: [https://github.com/fewshot-goes-multilingual/slavic-incontext-learning]
- Paper: [To be filled]
Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below). It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("{this model path}")
tokenizer = AutoTokenizer.from_pretrained("{this model path}")
# Instead, use keywords "Вопрос", "Контекст" and "Отвечать" for Russian few-shot prompts
input_text = """
Question: What is the customer's name?
Context: Origin: Barrack Obama, Customer id: Bill Moe.
Answer: Bill Moe,
Question: What is the customer's name?
Context: Customer id: Barrack Obama, if not deliverable, return to Bill Clinton.
Answer:
"""
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print("Answer:")
print(tokenizer.decode(outputs))
Training Details
Training this model can be reproduced by running pip install -r requirements.txt && python train_mt5_qa_en_AQA+ru_info.py .
See the referenced script for hyperparameters and other training configurations.
Citation
BibTeX: [Will be filled soon]