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-6
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - type: accuracy
      value: 0.8149568552253116
      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-6

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: 0.4516
- Accuracy: 0.8150

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5922        | 0.19  | 50   | 0.5552          | 0.6913   |
| 0.4661        | 0.37  | 100  | 0.5390          | 0.7891   |
| 0.5097        | 0.56  | 150  | 0.4639          | 0.7929   |
| 0.3977        | 0.75  | 200  | 0.4841          | 0.8150   |
| 0.4205        | 0.93  | 250  | 0.5351          | 0.8169   |
| 0.3427        | 1.12  | 300  | 0.4983          | 0.8265   |
| 0.3216        | 1.31  | 350  | 0.4590          | 0.8198   |
| 0.3486        | 1.49  | 400  | 0.4620          | 0.8207   |
| 0.3683        | 1.68  | 450  | 0.5333          | 0.8274   |
| 0.4098        | 1.87  | 500  | 0.4516          | 0.8150   |
| 0.2971        | 2.05  | 550  | 0.5464          | 0.8332   |
| 0.2701        | 2.24  | 600  | 0.5428          | 0.8322   |
| 0.2758        | 2.43  | 650  | 0.5045          | 0.8255   |
| 0.2417        | 2.61  | 700  | 0.5153          | 0.8313   |
| 0.334         | 2.8   | 750  | 0.5276          | 0.8332   |
| 0.2691        | 2.99  | 800  | 0.5262          | 0.8313   |
| 0.2542        | 3.17  | 850  | 0.5558          | 0.8351   |
| 0.285         | 3.36  | 900  | 0.5150          | 0.8370   |
| 0.2593        | 3.54  | 950  | 0.5291          | 0.8293   |
| 0.3295        | 3.73  | 1000 | 0.5216          | 0.8303   |


### Framework versions

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