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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- grammar |
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- spelling |
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- punctuation |
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- error-correction |
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datasets: |
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- jfleg |
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widget: |
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- text: "i can has cheezburger" |
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example_title: "cheezburger" |
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- text: "There car broke down so their hitching a ride to they're class." |
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example_title: "compound-1" |
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- 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 |
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i again tort watfettering an we have estimated the trend an |
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called wot to be called sthat of exty right now we can and look at |
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wy this should not hare a trend i becan we just remove the trend an and we can we now estimate |
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tesees ona effect of them exty" |
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example_title: "Transcribed Audio Example 2" |
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- text: "My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money." |
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example_title: "incorrect word choice (context)" |
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- 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 |
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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 |
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ta ohow to remove trents in these nalitives from time series" |
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example_title: "lowercased audio transcription output" |
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- text: "Frustrated, the chairs took me forever to set up." |
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example_title: "dangling modifier" |
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- text: "I would like a peice of pie." |
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example_title: "miss-spelling" |
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- text: "Which part of Zurich was you going to go hiking in when we were there for the first time together? ! ?" |
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example_title: "chatbot on Zurich" |
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parameters: |
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max_length: 128 |
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min_length: 4 |
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num_beams: 4 |
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repetition_penalty: 1.21 |
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length_penalty: 1 |
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early_stopping: True |
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--- |
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> 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. |
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# t5-v1_1-base-ft-jflAUG |
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**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). |
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- 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. |
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- 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/). |
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## Model description |
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- this is a WIP. This fine-tuned model is v1. |
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- long term: a generalized grammar and spelling correction model that can handle lots of things at the same time. |
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- currently, it seems to be more of a "gibberish to mostly correct English" translator |
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## Intended uses & limitations |
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- try some tests with the [examples here](https://www.engvid.com/english-resource/50-common-grammar-mistakes-in-english/) |
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- 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. |
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## Training and evaluation data |
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- trained as text-to-text |
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- JFLEG dataset + additional selected and/or generated grammar corrections |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 5 |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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