---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-base_sst2_dense_epochs-8
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9231651376146789
---

<!-- 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_sst2_dense_epochs-8

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: 0.2179
- Accuracy: 0.9232

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6384        | 0.02  | 50   | 0.6360          | 0.7064   |
| 0.3416        | 0.05  | 100  | 0.2955          | 0.8922   |
| 0.29          | 0.07  | 150  | 0.2512          | 0.9094   |
| 0.2371        | 0.1   | 200  | 0.2511          | 0.9106   |
| 0.2059        | 0.12  | 250  | 0.2379          | 0.9174   |
| 0.2617        | 0.14  | 300  | 0.2299          | 0.9174   |
| 0.2266        | 0.17  | 350  | 0.2190          | 0.9243   |
| 0.2288        | 0.19  | 400  | 0.2292          | 0.9255   |
| 0.2385        | 0.21  | 450  | 0.2263          | 0.9232   |
| 0.161         | 0.24  | 500  | 0.2368          | 0.9243   |
| 0.158         | 0.26  | 550  | 0.2411          | 0.9174   |
| 0.2469        | 0.29  | 600  | 0.2381          | 0.9209   |
| 0.2417        | 0.31  | 650  | 0.2349          | 0.9163   |
| 0.1614        | 0.33  | 700  | 0.2251          | 0.9174   |
| 0.2764        | 0.36  | 750  | 0.2129          | 0.9266   |
| 0.1499        | 0.38  | 800  | 0.2248          | 0.9197   |
| 0.1376        | 0.4   | 850  | 0.2285          | 0.9232   |
| 0.1875        | 0.43  | 900  | 0.2324          | 0.9312   |
| 0.1819        | 0.45  | 950  | 0.2302          | 0.9220   |
| 0.2373        | 0.48  | 1000 | 0.2179          | 0.9232   |
| 0.0956        | 0.5   | 1050 | 0.2077          | 0.9278   |
| 0.2396        | 0.52  | 1100 | 0.3249          | 0.9266   |
| 0.2543        | 0.55  | 1150 | 0.4440          | 0.9243   |
| 0.0942        | 0.57  | 1200 | 0.1982          | 0.9312   |
| 0.1296        | 0.59  | 1250 | 0.4270          | 0.9335   |
| 0.1618        | 0.62  | 1300 | 0.1893          | 0.9392   |
| 0.1902        | 0.64  | 1350 | 0.1911          | 0.9381   |
| 0.1234        | 0.67  | 1400 | 0.1903          | 0.9346   |
| 0.1369        | 0.69  | 1450 | 0.4157          | 0.9335   |
| 0.1149        | 0.71  | 1500 | 0.4121          | 0.9323   |
| 0.1501        | 0.74  | 1550 | 0.6343          | 0.9358   |
| 0.1679        | 0.76  | 1600 | 0.5294          | 0.9323   |
| 0.1462        | 0.78  | 1650 | 0.4037          | 0.9392   |
| 0.2111        | 0.81  | 1700 | 0.4094          | 0.9323   |
| 0.0902        | 0.83  | 1750 | 0.4094          | 0.9346   |
| 0.1185        | 0.86  | 1800 | 0.4059          | 0.9323   |
| 0.1602        | 0.88  | 1850 | 0.2946          | 0.9323   |
| 0.1212        | 0.9   | 1900 | 0.3037          | 0.9312   |


### Framework versions

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