Instructions to use alinet/bart-base-balanced-qg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alinet/bart-base-balanced-qg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alinet/bart-base-balanced-qg") model = AutoModelForSeq2SeqLM.from_pretrained("alinet/bart-base-balanced-qg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update metadata with huggingface_hub
Browse files
README.md
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- type: bertscore
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value: 0.6576008430831539
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name: BERTScore Recall
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---
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A question generation model trained on `alinet/balanced_qg` dataset.
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- type: bertscore
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value: 0.6576008430831539
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name: BERTScore Recall
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- task:
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type: text2text-generation
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name: Question Generation
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dataset:
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name: Spoken-SQuAD
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type: alinet/spoken_squad
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metrics:
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- type: bertscore
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value: 0.6005104740534271
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name: BERTScore F1
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---
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A question generation model trained on `alinet/balanced_qg` dataset.
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