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pszemraj
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t5-v1_1-base-ft-jflAUG

Text2Text Generation
Transformers
PyTorch
Safetensors
t5
grammar
spelling
punctuation
error-correction
Inference Endpoints
text-generation-inference
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t5-v1_1-base-ft-jflAUG / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: t5-v1_1-base-ft-jflAUG
widget:
  - text: Anna and Mike is going skiing
    example_title: skiing
  - text: >-
      so em if we have an now so with fito ringina know how to estimate the tren
      given the ereafte mylite trend we can also em an estimate is nod s i again
      tort watfettering an we have estimated the trend an called wot to be
      called sthat of exty right now we can and look at wy this should not hare
      a trend i becan we just remove the trend an and we can we now estimate
      tesees ona effect of them exty
    example_title: Transcribed Audio Example 2
  - text: I would like a peice of pie.
    example_title: miss-spelling
  - text: >-
      My coworker said he used a financial planner to help choose his stocks so
      he wouldn't loose money.
    example_title: incorrect word choice (context)
  - text: >-
      good so hve on an tadley i'm not able to make it to the exla session on
      monday this week e which is why i am e recording pre recording an this
      excelleision and so to day i want e to talk about two things and first of
      all em i wont em wene give a summary er about ta ohow to remove trents in
      these nalitives from time series
    example_title: lowercased audio transcription output
  - text: Frustrated, the chairs took me forever to set up.
    example_title: dangling modifier
  - text: There car broke down so their hitching a ride to they're class.
    example_title: compound-1
inference:
  parameters:
    no_repeat_ngram_size: 2
    max_length: 64
    min_length: 4
    num_beams: 4
    repetition_penalty: 3.51
    length_penalty: 0.8
    early_stopping: true

t5-v1_1-base-ft-jflAUG

  • GOAL: build a more robust and generalized grammar and spelling correction model that has minimal impact on the semantics of correct sentences (I.e. it does not change things that do not need to be changed.
  • this grammar correction 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.
  • This model is a fine-tuned version of google/t5-v1_1-base on an expanded version of the JFLEG dataset.

Model description

More information needed

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

More information needed

Training procedure

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

Training results

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6