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_ar9_ex29_size-16_epochs-3_decoder_all_sparsity20_mare_mlp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - type: accuracy
      value: 0.8341323106423778
      name: Accuracy
---

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# t5-base_cola_mare_ar9_ex29_size-16_epochs-3_decoder_all_sparsity20_mare_mlp

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: 0.6188
- Accuracy: 0.8341

## 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
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5148        | 0.19  | 50   | 0.8585          | 0.8188   |
| 0.4482        | 0.37  | 100  | 0.6410          | 0.8255   |
| 0.4572        | 0.56  | 150  | 0.6223          | 0.8284   |
| 0.4239        | 0.75  | 200  | 0.6037          | 0.8217   |
| 0.4716        | 0.93  | 250  | 0.5145          | 0.8313   |
| 0.3453        | 1.12  | 300  | 0.6653          | 0.8207   |
| 0.3124        | 1.31  | 350  | 0.5926          | 0.8341   |
| 0.3832        | 1.5   | 400  | 0.5726          | 0.8265   |
| 0.3035        | 1.68  | 450  | 0.5937          | 0.8313   |
| 0.3068        | 1.87  | 500  | 0.5681          | 0.8274   |
| 0.2659        | 2.06  | 550  | 0.6007          | 0.8265   |
| 0.3598        | 2.24  | 600  | 0.5988          | 0.8351   |
| 0.3051        | 2.43  | 650  | 0.5925          | 0.8360   |
| 0.309         | 2.62  | 700  | 0.6517          | 0.8332   |
| 0.209         | 2.8   | 750  | 0.6257          | 0.8332   |
| 0.3505        | 2.99  | 800  | 0.6252          | 0.8341   |


### Framework versions

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