transformers pytorch t5 text2text-generation en dataset:glue license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
model-index:
- name: t5-base-finetuned-qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.9123
      name: Accuracy
---


# T5-base-finetuned-qqp

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

This model is T5 fine-tuned on GLUE QQP dataset. It acheives the following results on the **validation** set
- Accuracy: 0.9123


## 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 **"qqp question1: " + qqp_question1 + "question2: " + qqp_question2** and fed to the tokenizer to get the **input_ids** and **attention_mask**.
For each label, label is choosen as **"duplicate"** if label is 1, else label is **"not_duplicate"** 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: epsilon=1e-08
- num_epochs: 3

### Training results


|Epoch | Training Loss | Validation Accuracy |
|:----:|:-------------:|:-------------------:|
|   1  |    0.0672    | 0.8888            |
|   2  |    0.0428     | 0.9082           |
|   3  |    0.0231      | 0.9123            |
