Transformers
PyTorch
Safetensors
English
t5
text2text-generation
qa
askscience
lfqa
information retrieval
text-generation-inference
Instructions to use pszemraj/t5-base-askscience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/t5-base-askscience with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/t5-base-askscience") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/t5-base-askscience", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# t5 - base- askscience
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- the entirety of askscience sub-section of eli5 dataset for
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- compare to bart on eli5 [here](https://huggingface.co/yjernite/bart_eli5)
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# t5 - base- askscience
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- the entirety of askscience sub-section of eli5 dataset for one epoch.
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- compare to bart on eli5 [here](https://huggingface.co/yjernite/bart_eli5)
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- note that for the inference API here, the model is restricted to outputting 128 tokens - using the model in python with the transformers library, you can get longer outputs.
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## training
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- for inputs, the model was presented with the post title and the post selftext encoded as: `question: <post title> context: <post selftext>`. You may see better results if queries are posed in this fashion.
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- The top two replies were aggregated and presented to the model as the output text.
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