Instructions to use kaejo98/bart-base-question-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaejo98/bart-base-question-generation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kaejo98/bart-base-question-generation") model = AutoModelForSeq2SeqLM.from_pretrained("kaejo98/bart-base-question-generation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kaejo98/bart-base-question-generation: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
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https://huggingface.co/kaejo98/bart-base-question-generation/resolve/main/README.md
- Command line
-
hf download hf://kaejo98/bart-base-question-generation/README.md
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curl -L -o README.md https://huggingface.co/kaejo98/bart-base-question-generation/resolve/main/README.md
1.16 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-question-generation
results: []
bart-base-question-generation
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 5
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1