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--- |
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language: |
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- en |
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tags: |
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- t5 |
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- qa |
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- askscience |
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- lfqa |
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- information retrieval |
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datasets: |
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- eli5 |
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metrics: |
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- rouge |
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widget: |
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- text: "why aren't there more planets in our solar system?" |
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example_title: "solar system" |
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- text: "question: what is a probability distribution? context: I am just learning about statistics." |
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example_title: "probability distribution" |
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- text: "question: how does exercise help us lose weight? context: I started working out two weeks ago and already feel a lot better, and started to think about it and became deeply confused." |
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example_title: "pumpen" |
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inference: |
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parameters: |
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max_length: 64 |
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no_repeat_ngram_size: 2 |
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encoder_no_repeat_ngram_size: 3 |
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repetition_penalty: 2.4 |
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length_penalty: 0.5 |
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num_beams: 4 |
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early_stopping: True |
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--- |
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# t5 - base- askscience |
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- [t5-v1_1](https://huggingface.co/google/t5-v1_1-base) trained on the entirety of the _askscience_ sub-section of the 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, the model is restricted to outputting 64 tokens - by 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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- 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. |
