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A more recent version can be found here. Training smaller and/or comparably sized models is a WIP.


GOAL: a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e. it does not change things that do not need to be changed).

  • this model (at least from preliminary testing) can handle large amounts of errors in the source text (i.e. from audio transcription) and still produce cohesive results.
  • a fine-tuned version of google/t5-v1_1-base on an expanded version of the JFLEG dataset.

Model description

  • this is a WIP. This fine-tuned model is v1.
  • long term: a generalized grammar and spelling correction model that can handle lots of things at the same time.
  • currently, it seems to be more of a "gibberish to mostly correct English" translator

Intended uses & limitations

  • try some tests with the examples here
  • thus far, some limitations are: sentence fragments are not autocorrected (at least, if entered individually), some more complicated pronoun/they/he/her etc. agreement is not always fixed.

Training and evaluation data

  • trained as text-to-text
  • JFLEG dataset + additional selected and/or generated grammar corrections

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 5

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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Dataset used to train pszemraj/t5-v1_1-base-ft-jflAUG

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