---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: t5-large_wic_dense_epochs-5
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: super_glue
      type: super_glue
      config: wic
      split: validation
      args: wic
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6598746081504702
---

<!-- 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_wic_dense_epochs-5

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7106
- Accuracy: 0.6599

## 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: 64
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6058        | 2.35  | 50   | 0.7125          | 0.6176   |
| 0.4662        | 4.71  | 100  | 0.7054          | 0.6614   |


### Framework versions

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