transformers pytorch t5 text-classification generated_from_trainer dataset:glue base_model:google-t5/t5-base base_model:finetune:google-t5/t5-base license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: t5-base
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: t5-base_cola_dense_epochs-5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - type: accuracy
      value: 0.822627037392138
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-base_cola_dense_epochs-5

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5026
- Accuracy: 0.8226

## 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: 32
- eval_batch_size: 64
- seed: 0
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5712        | 0.19  | 50   | 0.5805          | 0.6913   |
| 0.4693        | 0.37  | 100  | 0.6260          | 0.7661   |
| 0.4731        | 0.56  | 150  | 0.5279          | 0.8054   |
| 0.3707        | 0.75  | 200  | 0.5165          | 0.8025   |
| 0.4729        | 0.93  | 250  | 0.5145          | 0.8102   |
| 0.3929        | 1.12  | 300  | 0.4773          | 0.8188   |
| 0.3369        | 1.31  | 350  | 0.5014          | 0.8198   |
| 0.3757        | 1.49  | 400  | 0.5183          | 0.8188   |
| 0.4206        | 1.68  | 450  | 0.5743          | 0.8198   |
| 0.4196        | 1.87  | 500  | 0.5026          | 0.8226   |
| 0.3098        | 2.05  | 550  | 0.5289          | 0.8236   |
| 0.2852        | 2.24  | 600  | 0.5562          | 0.8265   |
| 0.2936        | 2.43  | 650  | 0.5312          | 0.8303   |
| 0.2072        | 2.61  | 700  | 0.4904          | 0.8313   |
| 0.2809        | 2.8   | 750  | 0.5394          | 0.8341   |
| 0.2685        | 2.99  | 800  | 0.5905          | 0.8332   |
| 0.2215        | 3.17  | 850  | 0.5835          | 0.8341   |
| 0.3543        | 3.36  | 900  | 0.5556          | 0.8332   |
| 0.239         | 3.54  | 950  | 0.5419          | 0.8351   |
| 0.257         | 3.73  | 1000 | 0.5587          | 0.8351   |
| 0.2958        | 3.92  | 1050 | 0.5982          | 0.8341   |
| 0.2785        | 4.1   | 1100 | 0.5978          | 0.8360   |
| 0.1975        | 4.29  | 1150 | 0.6067          | 0.8341   |
| 0.2222        | 4.48  | 1200 | 0.5947          | 0.8380   |


### Framework versions

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