transformers pytorch t5 text-classification generated_from_trainer dataset:glue base_model:google-t5/t5-large base_model:finetune:google-t5/t5-large license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: t5-large
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: t5-large_cola_dense_epochs-7_decoder_all_sparsity10
  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
---

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# t5-large_cola_dense_epochs-7_decoder_all_sparsity10

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: 4.6969
- 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: 64
- eval_batch_size: 128
- seed: 1
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5441        | 0.37  | 25   | 0.5813          | 0.6913   |
| 0.3969        | 0.75  | 50   | 0.5219          | 0.8044   |
| 0.3537        | 1.12  | 75   | 0.4713          | 0.8313   |
| 0.2905        | 1.49  | 100  | 0.6308          | 0.8150   |
| 0.3157        | 1.87  | 125  | 0.4301          | 0.8341   |
| 0.2208        | 2.24  | 150  | 2.3147          | 0.8332   |
| 0.2231        | 2.61  | 175  | 0.4612          | 0.8341   |
| 0.2404        | 2.99  | 200  | 1.5471          | 0.8265   |
| 0.1697        | 3.36  | 225  | 0.8701          | 0.8313   |
| 0.131         | 3.73  | 250  | 1.2642          | 0.8380   |
| 0.1219        | 4.1   | 275  | 0.9926          | 0.8370   |
| 0.2647        | 4.48  | 300  | 5.1919          | 0.8341   |
| 0.1329        | 4.85  | 325  | 2.2726          | 0.8418   |
| 0.0857        | 5.22  | 350  | 4.2193          | 0.8370   |
| 0.0989        | 5.6   | 375  | 5.3604          | 0.8389   |
| 0.2557        | 5.97  | 400  | 3.0246          | 0.8341   |
| 0.2617        | 6.34  | 425  | 5.6630          | 0.8456   |
| 0.2526        | 6.72  | 450  | 6.0474          | 0.8360   |


### Framework versions

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