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_mare_ar16_ex0_size-32_epochs-5_collected-stats
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - type: accuracy
      value: 0.837967401725791
      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_mare_ar16_ex0_size-32_epochs-5_collected-stats

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: 0.4445
- Accuracy: 0.8380

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5829        | 0.19  | 50   | 0.5713          | 0.6913   |
| 0.4547        | 0.37  | 100  | 0.5431          | 0.7766   |
| 0.4778        | 0.56  | 150  | 0.5055          | 0.8044   |
| 0.4226        | 0.75  | 200  | 0.4686          | 0.8130   |
| 0.4085        | 0.93  | 250  | 0.4968          | 0.8207   |
| 0.3464        | 1.12  | 300  | 0.4517          | 0.8255   |
| 0.328         | 1.31  | 350  | 0.4798          | 0.8226   |
| 0.3369        | 1.49  | 400  | 0.4854          | 0.8284   |
| 0.401         | 1.68  | 450  | 0.5107          | 0.8255   |
| 0.4029        | 1.87  | 500  | 0.4445          | 0.8380   |
| 0.2603        | 2.05  | 550  | 0.5151          | 0.8351   |
| 0.2754        | 2.24  | 600  | 0.4883          | 0.8370   |
| 0.2721        | 2.43  | 650  | 0.4581          | 0.8437   |
| 0.2381        | 2.61  | 700  | 0.4775          | 0.8399   |
| 0.341         | 2.8   | 750  | 0.5196          | 0.8456   |
| 0.2879        | 2.99  | 800  | 0.5360          | 0.8399   |
| 0.2328        | 3.17  | 850  | 0.5213          | 0.8418   |
| 0.2864        | 3.36  | 900  | 0.4942          | 0.8408   |
| 0.2791        | 3.54  | 950  | 0.4915          | 0.8399   |
| 0.3247        | 3.73  | 1000 | 0.4879          | 0.8389   |


### Framework versions

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