license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: i can has cheezburger
example_title: cheezburger
- text: There car broke down so their hitching a ride to they're class.
example_title: compound-1
- 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: >-
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
parameters:
max_length: 128
min_length: 2
num_beams: 8
repetition_penalty: 1.3
length_penalty: 1
early_stopping: true
grammar-synthesis-small (beta)
This model is a fine-tuned version of google/t5-small-lm-adapt for grammar correction on an expanded version of the JFLEG dataset.
usage in Python (after pip install transformers):
from transformers import pipeline
corrector = pipeline(
'text2text-generation',
'pszemraj/grammar-synthesis-small',
)
raw_text = 'i can has cheezburger'
results = corrector(raw_text)
print(results)
Check out a simple demo in Google Colab here.
Model description
The intent is to create a text2text language model that successfully completes "single-shot grammar correction" on a potentially grammatically incorrect text that could have a lot of mistakes with the important qualifier of it does not semantically change text/information that IS grammatically correct.
Compare some of the heavier-error examples on other grammar correction models to see the difference :)
Limitations
- dataset:
cc-by-nc-sa-4.0 - model:
apache-2.0 - this is still a work-in-progress and while probably useful for "single-shot grammar correction" in a lot of cases, give the outputs a glance for correctness ok?
Use Cases
Obviously, this section is quite general as there are many things one can use "general single-shot grammar correction" for. Some ideas or use cases:
- Correcting highly error-prone LM outputs. Some examples would be audio transcription (ASR) (this is literally some of the examples) or something like handwriting OCR.
- To be investigated further, depending on what model/system is used it might be worth it to apply this after OCR on typed characters.
- Correcting/infilling text generated by text generation models to be cohesive/remove obvious errors that break the conversation immersion. I use this on the outputs of this OPT 2.7B chatbot-esque model of myself.
An example of this model running on CPU with beam search:
original response:
ive heard it attributed to a bunch of different philosophical schools, including stoicism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to
synthesizing took 306.12 seconds
Final response in 1294.857 s:
I've heard it attributed to a bunch of different philosophical schools, including solipsism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to speak)
Note: that I have some other logic that removes any periods at the end of the final sentence in this chatbot setting to avoid coming off as passive aggressive
- Somewhat related to #2 above, fixing/correcting so-called tortured-phrases that are dead giveaways text was generated by a language model. Note that SOME of these are not fixed, especially as they venture into domain-specific terminology (i.e. irregular timberland instead of Random Forest).
Training and evaluation data
More information needed 😉
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
Training results
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1