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--- |
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languages: |
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- en |
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license: |
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- cc-by-nc-sa-4.0 |
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- apache-2.0 |
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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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- grammar synthesis |
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datasets: |
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- jfleg |
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widget: |
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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: i can has cheezburger |
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example_title: cheezburger |
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- text: >- |
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so em if we have an now so with fito ringina know how to estimate the tren |
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given the ereafte mylite trend we can also em an estimate is nod s i again |
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tort watfettering an we have estimated the trend an called wot to be called |
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sthat of exty right now we can and look at wy this should not hare a trend i |
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becan we just remove the trend an and we can we now estimate tesees ona |
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effect of them exty |
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example_title: Transcribed Audio Example 2 |
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- text: >- |
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My coworker said he used a financial planner to help choose his stocks so he |
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wouldn't loose money. |
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example_title: incorrect word choice (context) |
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- text: >- |
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good so hve on an tadley i'm not able to make it to the exla session on |
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monday this week e which is why i am e recording pre recording an this |
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excelleision and so to day i want e to talk about two things and first of |
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all em i wont em wene give a summary er about ta ohow to remove trents in |
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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: >- |
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Which part of Zurich was you going to go hiking in when we were there for |
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the first time together? ! ? |
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example_title: chatbot on Zurich |
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- text: >- |
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Most of the course is about semantic or content of language but there are |
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also interesting topics to be learned from the servicefeatures except |
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statistics in characters in documents. At this point, Elvthos introduces |
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himself as his native English speaker and goes on to say that if you |
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continue to work on social scnce, |
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example_title: social science ASR summary output |
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- text: >- |
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they are somewhat nearby right yes please i'm not sure how the innish is |
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tepen thut mayyouselect one that istatte lo variants in their property e ere |
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interested and anyone basical e may be applyind reaching the browing |
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approach were |
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- medical course audio transcription |
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parameters: |
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max_new_tokens: 128 |
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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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language: |
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- en |
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pipeline_tag: text2text-generation |
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--- |
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# bart-base-grammar-synthesis |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an expanded version of the JFLEG dataset. |
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You can find other grammar-synthesis models by [searching for the grammar synthesis tag](https://huggingface.co/models?other=grammar%20synthesis) |
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## Intended uses & limitations |
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- robust grammar correction |
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- the model has a license of `cc-by-nc-sa-4.0` as it uses the JFLEG dataset + augments it for training |
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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: 0.0001 |
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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: 16 |
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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: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
