---
license: apache-2.0
language:
- en
tags:
- t5
- qa
- askscience
- lfqa
- information retrieval
datasets:
- vblagoje/lfqa
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
    
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# checkpoints

This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the `vblagoje/lfqa` dataset, with training duration of 2 epochs. For a (_somewhat_) apples-to-apples comparison with [t5-base](https://huggingface.co/pszemraj/t5-base-askscience) on the standard eli5 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0