YAML Metadata Warning: 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, image-text-to-image, image-text-to-video, 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
Book Buddy - Question Generator
This fine-tuned model was generated to help students study. By submitting their texts, the model will generate a question to help them study.
Model Details
- Model Architecture: T5
- Tokenizer Used:
- Language: English
- Task: Question Generation
Model Usage
How to Use
Provide instructions on how to use your model in a clear and concise manner.
# Example code for using the model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("t5_tokenizer")
# Load the model
model = AutoModelForSeq2SeqLM.from_pretrained("t5_trained_model")
# Generate a question
input_text = "Provide a sample input text."
input_ids = tokenizer.encode(input_text, return_tensors="pt", padding=True, max_length=512, truncation=True)
# Generate question
question_ids = model.generate(input_ids, max_length=32, num_return_sequences=1, num_beams=4)
# Decode the generated question
generated_question = tokenizer.decode(question_ids[0], skip_special_tokens=True)
print(f"Generated Question: {generated_question}")
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