readme: ---
  license: apache-2.0
  language:
  - en
  tags:
  - t5
  - qa
  - askscience
  - lfqa
  - information retrieval
  datasets:
  - vblagoje/lfqa
  metrics:
  - rouge
  widget:
  - text: "why hasn't humanity expanded to live on other 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 is the process that computers use to understand human language in deep learning models?"
    example_title: "NLP"
    
  inference:
    parameters:
      max_length: 64
      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
      
  ---
  
  
  # 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.
    - This checkpoint does seem to be more coherent than t5-base on the original dataset.
  - Compared to [bart on lfqa](https://huggingface.co/vblagoje/bart_lfqa), it seems to be able to respond to some questions independently of retrieval.
  
  > NOTE: the inference API is limited to generating approx. 64 chars for runtime reasons, for longer outputs try using it in python as a transformers pipeline object.
  
  ## Intended uses & limitations
  
  - Q&A, information retrieval
  - it is probably better to use it with a [retrieval pipeline](https://github.com/deepset-ai/haystack) than alone
  
  ## Training and evaluation data
  
  - see linked dataset. the dataset was filtered to only included the `askscience` subreddit in an attempt to focus on academic/technical queries.
  
  ## 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
  