---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-base_cola_moe_ex9_sp0_05_ar0_0_mare_mlp
  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: 0.0
---

<!-- 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-base_cola_moe_ex9_sp0_05_ar0_0_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: 3.9386
- Accuracy: 0.0

## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2328        | 0.09  | 25   | 1.1651          | 0.7383   |
| 0.764         | 0.19  | 50   | 0.7678          | 0.7287   |
| 0.6109        | 0.28  | 75   | 0.6739          | 0.7718   |
| 0.5633        | 0.37  | 100  | 0.5954          | 0.7661   |
| 0.5133        | 0.47  | 125  | 0.5870          | 0.7814   |
| 0.5224        | 0.56  | 150  | 0.5766          | 0.7785   |
| 0.4876        | 0.65  | 175  | 0.5574          | 0.7881   |
| 0.5157        | 0.75  | 200  | 0.5760          | 0.7881   |
| 0.4745        | 0.84  | 225  | 0.5327          | 0.7824   |
| 0.4612        | 0.93  | 250  | 0.5576          | 0.7900   |
| 0.4491        | 1.03  | 275  | 0.5174          | 0.7881   |
| 0.358         | 1.12  | 300  | 0.6065          | 0.7900   |
| 0.3363        | 1.21  | 325  | 0.6949          | 0.7919   |
| 0.4065        | 1.31  | 350  | 0.5112          | 0.7987   |
| 0.4044        | 1.4   | 375  | 0.5681          | 0.8063   |
| 0.3888        | 1.49  | 400  | 0.5422          | 0.7996   |
| 0.4992        | 1.59  | 425  | 0.5294          | 0.7900   |
| 0.4231        | 1.68  | 450  | 0.5221          | 0.8044   |
| 0.4912        | 1.77  | 475  | 0.4984          | 0.8130   |
| 0.4951        | 1.87  | 500  | 0.5109          | 0.8015   |
| 0.3117        | 1.96  | 525  | 0.5640          | 0.8044   |
| 0.3822        | 2.05  | 550  | 0.5524          | 0.8130   |
| 0.3886        | 2.15  | 575  | 0.6092          | 0.8121   |
| 0.305         | 2.24  | 600  | 0.5380          | 0.8111   |
| 0.4815        | 2.33  | 625  | 0.5478          | 0.8111   |
| 0.3298        | 2.43  | 650  | 0.5298          | 0.8150   |
| 0.3533        | 2.52  | 675  | 0.5043          | 0.8140   |
| 0.3706        | 2.61  | 700  | 0.5810          | 0.8178   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.11.6