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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-v1_1-base-ft-jflAUG
widget:
- text: "Anna and Mike is going skiing"
example_title: "skiing"
- 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: "I would like a peice of pie."
example_title: "miss-spelling"
- 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: "There car broke down so their hitching a ride to they're class."
example_title: "compound-1"
inference:
parameters:
no_repeat_ngram_size: 2
max_length: 64
min_length: 4
num_beams: 4
repetition_penalty: 3.51
length_penalty: 0.8
early_stopping: True
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-v1_1-base-ft-jflAUG
- **GOAL:** build a more robust and generalized grammar and spelling correction model that has minimal impact on the semantics of correct sentences (I.e. it does not change things that do not need to be changed.
- this grammar correction 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.
- This model is 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.
## Model description
More information needed
## 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
More information needed
## Training procedure
### 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
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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