---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: t5-large_boolq_dense_epochs-5
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: super_glue
      type: super_glue
      config: boolq
      split: validation
      args: boolq
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.846177370030581
---

<!-- 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-large_boolq_dense_epochs-5

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3715
- Accuracy: 0.8462

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6792        | 0.17  | 50   | 0.6652          | 0.6217   |
| 0.66          | 0.34  | 100  | 0.6595          | 0.6220   |
| 0.6614        | 0.51  | 150  | 0.6548          | 0.6232   |
| 0.636         | 0.68  | 200  | 0.6122          | 0.6985   |
| 0.4882        | 0.85  | 250  | 0.4702          | 0.7847   |
| 0.5068        | 1.02  | 300  | 0.4639          | 0.7862   |
| 0.3332        | 1.19  | 350  | 0.5297          | 0.7908   |
| 0.4296        | 1.36  | 400  | 0.3955          | 0.8373   |
| 0.356         | 1.53  | 450  | 0.4013          | 0.8410   |
| 0.3227        | 1.7   | 500  | 0.3715          | 0.8462   |
| 0.3516        | 1.87  | 550  | 0.3724          | 0.8428   |
| 0.2169        | 2.04  | 600  | 0.3906          | 0.8477   |
| 0.2199        | 2.21  | 650  | 0.4061          | 0.8572   |
| 0.1969        | 2.37  | 700  | 0.4351          | 0.8550   |
| 0.2713        | 2.54  | 750  | 0.5411          | 0.8584   |
| 0.2458        | 2.71  | 800  | 0.3924          | 0.8627   |
| 0.2134        | 2.88  | 850  | 0.3973          | 0.8630   |
| 0.1636        | 3.05  | 900  | 0.4933          | 0.8590   |
| 0.1108        | 3.22  | 950  | 0.9926          | 0.8621   |
| 0.1433        | 3.39  | 1000 | 0.6679          | 0.8602   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1