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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Question decomposer Based t5 and Seq2seq
Example: What is the capital of France and when it entablish ?
- What is the capital of France ?
- When was the capital of France entablish ?
Checkout my demo here 👉🏻 demo
How to Usage
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
# Set device
device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"
# Load model and tokenizer
model_path = "thenHung/question_decomposer_t5"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to(device)
model.eval()
# Decompose question
question = "Who is taller between John and Mary?"
input_text = f"decompose question: {question}"
input_ids = tokenizer(
input_text,
max_length=128,
padding="max_length",
truncation=True,
return_tensors="pt"
).input_ids.to(device)
with torch.no_grad():
outputs = model.generate(
input_ids,
max_length=128,
num_beams=4,
early_stopping=True
)
# Decode output
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
sub_questions = decoded_output.split(" [SEP] ")
# Print sub-questions
print(sub_questions)
# ['What is the height of John?', 'What is the height of Mary?']
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Base model
google-t5/t5-base