|
--- |
|
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 don't want no pudding." |
|
example_title: "double negatives" |
|
- 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 |
|
|
