---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-large_sst2_dense_epochs-3
  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: 0.9575688073394495
---

<!-- 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-large_sst2_dense_epochs-3

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2376
- Accuracy: 0.9576

## 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: 256
- eval_batch_size: 256
- seed: 0
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2133        | 0.38  | 50   | 0.2188          | 0.9415   |
| 0.1655        | 0.76  | 100  | 0.3689          | 0.9518   |
| 0.1473        | 1.14  | 150  | 0.2660          | 0.9541   |
| 0.1092        | 1.52  | 200  | 0.2441          | 0.9576   |
| 0.1081        | 1.89  | 250  | 0.2395          | 0.9599   |
| 0.0785        | 2.27  | 300  | 0.3700          | 0.9599   |
| 0.119         | 2.65  | 350  | 0.3577          | 0.9530   |


### Framework versions

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