| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - grammar |
| - spelling |
| - punctuation |
| - error-correction |
| 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" |
| - text: "Frustrated, the chairs took me forever to set up." |
| example_title: "dangling modifier" |
| - text: "I would like a peice of pie." |
| example_title: "miss-spelling" |
| - text: "Which part of Zurich was you going to go hiking in when we were there for the first time together? ! ?" |
| example_title: "chatbot on Zurich" |
|
|
| parameters: |
| max_length: 128 |
| min_length: 4 |
| num_beams: 4 |
| repetition_penalty: 1.21 |
| length_penalty: 1 |
| early_stopping: True |
| --- |
| |
| > A more recent version can be found [here](https://huggingface.co/pszemraj/grammar-synthesis-large). Training smaller and/or comparably sized models is a WIP. |
|
|
| # t5-v1_1-base-ft-jflAUG |
| |
| **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](https://huggingface.co/google/t5-v1_1-base) on an expanded version of the [JFLEG dataset](https://aclanthology.org/E17-2037/). |
|
|
| ## 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](https://www.engvid.com/english-resource/50-common-grammar-mistakes-in-english/) |
| - 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 |
| |
