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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/t5-base-askscience") model = AutoModelForMultimodalLM.from_pretrained("pszemraj/t5-base-askscience") - Notebooks
- Google Colab
- Kaggle
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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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## 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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- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs this should be fairly viable.
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