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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: "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: "I would like a peice of pie." |
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example_title: "miss-spelling" |
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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: "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: "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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# grammar-synthesis-large - beta |
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A fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset. |
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usage in Python (after `pip install transformers`): |
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``` |
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from transformers import pipeline |
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corrector = pipeline( |
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'text2text-generation', |
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'pszemraj/grammar-synthesis-large', |
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) |
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raw_text = 'i can has cheezburger' |
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results = corrector(raw_text) |
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print(results) |
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``` |
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give it a spin in Colab at [this notebook](https://colab.research.google.com/gist/pszemraj/9b810e38a4d3bc766834df921818d782/scratchpad.ipynb) |
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## Model description |
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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.** |
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Compare some of the heavier-error examples on [other grammar correction models](https://huggingface.co/models?dataset=dataset:jfleg) to see the difference :) |
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## Intended uses & limitations |
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- dataset: `cc-by-nc-sa-4.0` |
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- model: `apache-2.0` |
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- 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?** |
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## Training and evaluation data |
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More information needed 😉 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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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: 0.02 |
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- num_epochs: 1 |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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