

# T5-base-finetuned-cola

<!-- Provide a quick summary of what the model is/does. -->

This model is T5 fine-tuned on GLUE CoLA dataset. It acheives the following results on the validation set
- Matthews Correlation Coefficient: 0.5869


## Model Details
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. 

## Training procedure

### Tokenization
Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
For each example, a sentence as been formed as **"cola sentence: " + cola_sent** and fed to the tokenizer to get the **input_ids** and **attention_mask**.
For each label, label is choosen as **"acceptable"** if label is 1, else label is **"unacceptable"** and tokenized to get **input_ids** and **attention_mask** . 
  During training, these inputs_ids having **pad** token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention_mask of labels
  is given as decoder attention mask.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-4
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: epsilon=1e-08
- num_epochs: 3.0

### Training results


|Epoch | Training Loss | Validation Matthews Correlation Coefficient |
|:----:|:-------------:|:-------------------:|
|   1  |    0.2471     | 0.4577          |
|   2  |    0.1633     | 0.5869              |
|   3  |    0.0933     | 0.5855              |
{"language": ["en"], "license": "apache-2.0", "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "t5-base-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5869, "name": "Matthews Correlation"}]}]}]}
transformers pytorch t5 text2text-generation en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

T5-base-finetuned-cola
======================


This model is T5 fine-tuned on GLUE CoLA dataset. It acheives the following results on the validation set


* Matthews Correlation Coefficient: 0.5869


Model Details
-------------


T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.


Training procedure
------------------


### Tokenization


Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
For each example, a sentence as been formed as "cola sentence: " + cola\_sent and fed to the tokenizer to get the input\_ids and attention\_mask.
For each label, label is choosen as "acceptable" if label is 1, else label is "unacceptable" and tokenized to get input\_ids and attention\_mask .
During training, these inputs\_ids having pad token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention\_mask of labels
is given as decoder attention mask.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-4
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: epsilon=1e-08
* num\_epochs: 3.0


### Training results




### Tokenization


Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
For each example, a sentence as been formed as "cola sentence: " + cola\_sent and fed to the tokenizer to get the input\_ids and attention\_mask.
For each label, label is choosen as "acceptable" if label is 1, else label is "unacceptable" and tokenized to get input\_ids and attention\_mask .
During training, these inputs\_ids having pad token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention\_mask of labels
is given as decoder attention mask. ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-4
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: epsilon=1e-08
* num\_epochs: 3.0 ### Training results
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Tokenization


Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
For each example, a sentence as been formed as "cola sentence: " + cola\_sent and fed to the tokenizer to get the input\_ids and attention\_mask.
For each label, label is choosen as "acceptable" if label is 1, else label is "unacceptable" and tokenized to get input\_ids and attention\_mask .
During training, these inputs\_ids having pad token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention\_mask of labels
is given as decoder attention mask. ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-4
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: epsilon=1e-08
* num\_epochs: 3.0 ### Training results
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Tokenization


Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
For each example, a sentence as been formed as "cola sentence: " + cola\_sent and fed to the tokenizer to get the input\_ids and attention\_mask.
For each label, label is choosen as "acceptable" if label is 1, else label is "unacceptable" and tokenized to get input\_ids and attention\_mask .
During training, these inputs\_ids having pad token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention\_mask of labels
is given as decoder attention mask.### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-4
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: epsilon=1e-08
* num\_epochs: 3.0### Training results