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language:
- en
license: apache-2.0
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: t5-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5869
---
# 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 | |