---
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: "When the girls on the team got to the hotel, they dropped off her luggage"
  example_title: "Pronoun Errors"
- text: "I don't want no pudding."
  example_title: "double negatives"
- text: "We drove all day in the van, we drove 206 miles to get to our cabin."
  example_title: "run-on sentence"
- 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 the None 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