---
license: apache-2.0
base_model: t5-3b
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-3b_cola_dense_epochs-8_without_distillation_50
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8561840843720039
---

<!-- 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. -->

# t5-3b_cola_dense_epochs-8_without_distillation_50

This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6314
- Accuracy: 0.8562

## 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: 16
- eval_batch_size: 32
- seed: 1
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.508         | 0.19  | 50   | 0.4814          | 0.8054   |
| 0.4158        | 0.37  | 100  | 0.3697          | 0.8399   |
| 0.4471        | 0.56  | 150  | 0.3512          | 0.8543   |
| 0.3381        | 0.75  | 200  | 0.3653          | 0.8399   |
| 0.428         | 0.93  | 250  | 0.3373          | 0.8591   |
| 0.2148        | 1.12  | 300  | 1.6354          | 0.8533   |
| 0.1962        | 1.31  | 350  | 1.9031          | 0.8610   |
| 0.2383        | 1.5   | 400  | 0.6977          | 0.8600   |
| 0.2276        | 1.68  | 450  | 0.7896          | 0.8543   |
| 0.2574        | 1.87  | 500  | 0.5960          | 0.8571   |
| 0.0955        | 2.06  | 550  | 6.3365          | 0.8543   |
| 0.1537        | 2.24  | 600  | 0.7912          | 0.8667   |
| 0.0846        | 2.43  | 650  | 0.8280          | 0.8658   |
| 0.1852        | 2.62  | 700  | 0.4582          | 0.8581   |
| 0.1836        | 2.8   | 750  | 5.0320          | 0.8485   |
| 0.7772        | 2.99  | 800  | 1.2307          | 0.8600   |
| 0.0544        | 3.18  | 850  | 6.9846          | 0.8466   |
| 0.1017        | 3.36  | 900  | 1.1242          | 0.8552   |
| 0.0783        | 3.55  | 950  | 0.6369          | 0.8667   |
| 0.0627        | 3.74  | 1000 | 3.8335          | 0.8600   |
| 0.7314        | 3.93  | 1050 | 2.0148          | 0.8706   |
| 0.024         | 4.11  | 1100 | 5.1811          | 0.8648   |
| 0.0627        | 4.3   | 1150 | 4.7943          | 0.8773   |
| 0.069         | 4.49  | 1200 | 4.1017          | 0.8639   |
| 0.0443        | 4.67  | 1250 | 2.4810          | 0.8648   |
| 0.0295        | 4.86  | 1300 | 2.5363          | 0.8485   |
| 0.0411        | 5.05  | 1350 | 3.3954          | 0.8581   |
| 1.2558        | 5.23  | 1400 | 5.3373          | 0.8495   |
| 0.064         | 5.42  | 1450 | 6.3714          | 0.8658   |
| 0.0259        | 5.61  | 1500 | 7.3145          | 0.8639   |
| 0.0413        | 5.79  | 1550 | 6.4314          | 0.8667   |
| 0.0568        | 5.98  | 1600 | 4.7175          | 0.8648   |
| 0.049         | 6.17  | 1650 | 6.4853          | 0.8523   |
| 0.0689        | 6.36  | 1700 | 3.8090          | 0.8677   |
| 0.6785        | 6.54  | 1750 | 4.8987          | 0.8600   |
| 0.6287        | 6.73  | 1800 | 3.7412          | 0.8658   |
| 0.1197        | 6.92  | 1850 | 5.6841          | 0.8629   |
| 0.0528        | 7.1   | 1900 | 4.6580          | 0.8591   |
| 0.6495        | 7.29  | 1950 | 5.2935          | 0.8619   |
| 0.0764        | 7.48  | 2000 | 4.2176          | 0.8466   |
| 0.0438        | 7.66  | 2050 | 6.9325          | 0.8533   |
| 0.0583        | 7.85  | 2100 | 4.7150          | 0.8591   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.14.1