---
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: "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

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

More information needed

## 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