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---
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
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