language: | |
- en | |
tags: | |
- t5 | |
- qa | |
- askscience | |
- lfqa | |
- information retrieval | |
datasets: | |
- eli5 | |
metrics: | |
- rouge | |
widget: | |
- text: "why aren't there more planets in our solar system?" | |
example_title: "solar system" | |
- text: "question: what is a probability distribution? context: I am just learning about statistics." | |
example_title: "probability distribution" | |
- text: "question: What are the underlying physical processes by which exercise helps 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." | |
example_title: "pumpen" | |
- text: "what is a neural network?" | |
example_title: "deep learning" | |
- text: "What are the primary mechanisms that computers use to understand human language?" | |
example_title: "NLP" | |
inference: | |
parameters: | |
max_length: 96 | |
no_repeat_ngram_size: 2 | |
encoder_no_repeat_ngram_size: 4 | |
repetition_penalty: 3.51 | |
length_penalty: 0.8 | |
num_beams: 4 | |
early_stopping: True | |
# t5 - base- askscience | |
- [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. | |
- compare to bart on eli5 [here](https://huggingface.co/yjernite/bart_eli5) | |
- 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. | |
## training | |
- 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. | |
- The top two replies were aggregated and presented to the model as the output text. | |
- Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable. |