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---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-base_rte_dense_sp0_ar0
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.0
---
<!-- 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_rte_dense_sp0_ar0
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: 0.9086
- Accuracy: 0.0
## 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: 8
- eval_batch_size: 16
- seed: 1
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6787 | 0.16 | 25 | 0.6850 | 0.5307 |
| 0.7034 | 0.32 | 50 | 0.6689 | 0.5704 |
| 0.6478 | 0.48 | 75 | 0.6356 | 0.6570 |
| 0.6889 | 0.64 | 100 | 0.6188 | 0.6859 |
| 0.588 | 0.8 | 125 | 0.5892 | 0.6859 |
| 0.5989 | 0.96 | 150 | 0.6802 | 0.6606 |
| 0.5392 | 1.12 | 175 | 0.5836 | 0.7329 |
| 0.5497 | 1.28 | 200 | 0.6758 | 0.6715 |
| 0.5567 | 1.44 | 225 | 0.7056 | 0.6643 |
| 0.5063 | 1.6 | 250 | 0.5617 | 0.7401 |
| 0.5644 | 1.76 | 275 | 0.5737 | 0.7256 |
| 0.6018 | 1.92 | 300 | 0.6179 | 0.7112 |
| 0.4554 | 2.08 | 325 | 0.5339 | 0.7509 |
| 0.3778 | 2.24 | 350 | 0.5495 | 0.7726 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.14.1
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