license: apache-2.0
language:
- en
tags:
- question_generation
- qg
- qgen
widget:
- text: >-
The International Monetary Fund warned on Tuesday that colliding pressures
from inflation, war-driven energy and food crises and sharply higher
interest rates were pushing the world to the brink of recession and
threatening financial market stability.
example_title: Example 1
Model card for the Question Generation component of the Discord Questions paper (EMNLP 2022 - Findings). The model is a finetuned BART-large, and can be used with the following command:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
dqg_tokenizer = AutoTokenizer.from_pretrained("Salesforce/discord_qg")
discord_qg = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/discord_qg")
paragraph = "The International Monetary Fund warned on Tuesday that colliding pressures from inflation, war-driven energy and food crises and sharply higher interest rates were pushing the world to the brink of recession and threatening financial market stability."
encoder_ids = dqg_tokenizer.batch_encode_plus([paragraph], add_special_tokens=True, return_tensors="pt")
model_output = discord_qg.generate(**encoder_ids)
generated_texts = dqg_tokenizer.batch_decode(model_output, skip_special_tokens=True)
print(generated_texts) # ['When was the last time the IMF warned of a global recession?']
The model has a tendency to generate "When " questions. If you would rather generate other questions you can do the following:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
qg_tokenizer = AutoTokenizer.from_pretrained("Salesforce/discord_qg")
qg_model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/discord_qg")
paragraph = "The International Monetary Fund warned on Tuesday that colliding pressures from inflation, war-driven energy and food crises and sharply higher interest rates were pushing the world to the brink of recession and threatening financial market stability."
for start_word in ["How", "Why"]:
encoder_ids = qg_tokenizer.batch_encode_plus([paragraph], add_special_tokens=True, padding=True, truncation=True, return_tensors="pt")
decoder_input_ids = qg_tokenizer.batch_encode_plus([start_word], add_special_tokens=True, return_tensors="pt")["input_ids"][:, :-1]
model_output = qg_model.generate(**encoder_ids, decoder_input_ids=decoder_input_ids, max_length=20)
generated_questions = qg_tokenizer.batch_decode(model_output, skip_special_tokens=True)
print(generated_questions)
Prints:
['How will the global economy be affected?']
['Why was the IMF warning?']
Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.