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---
license: apache-2.0
base_model: t5-3b
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-3b_rte_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: 0.8875502008032129
---
<!-- 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. -->
# t5-3b_rte_sp0_ar0
This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4268
- Accuracy: 0.8876
## 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: 4
- eval_batch_size: 8
- seed: 1
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 750
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7306 | 0.18 | 25 | 0.6913 | 0.5921 |
| 0.6717 | 0.36 | 50 | 0.4976 | 0.8339 |
| 0.3978 | 0.53 | 75 | 0.5226 | 0.8628 |
| 0.322 | 0.71 | 100 | 0.3902 | 0.8484 |
| 0.2958 | 0.89 | 125 | 0.3803 | 0.8881 |
| 0.2604 | 1.07 | 150 | 0.8628 | 0.8736 |
| 0.2011 | 1.25 | 175 | 0.7780 | 0.8953 |
| 0.263 | 1.42 | 200 | 2.1533 | 0.8881 |
| 0.2032 | 1.6 | 225 | 4.7955 | 0.8917 |
| 0.2536 | 1.78 | 250 | 1.7810 | 0.8989 |
| 0.1984 | 1.96 | 275 | 0.5119 | 0.8845 |
| 0.1495 | 2.14 | 300 | 0.5128 | 0.8845 |
| 0.1275 | 2.31 | 325 | 0.8602 | 0.8628 |
| 0.0955 | 2.49 | 350 | 1.3642 | 0.8773 |
| 0.3912 | 2.67 | 375 | 1.0186 | 0.8664 |
| 0.1108 | 2.85 | 400 | 2.1450 | 0.8592 |
| 0.0726 | 3.02 | 425 | 2.6801 | 0.8809 |
| 0.0937 | 3.2 | 450 | 5.2053 | 0.8736 |
| 1.0143 | 3.38 | 475 | 3.3979 | 0.8845 |
| 0.5754 | 3.56 | 500 | 4.2786 | 0.8989 |
| 0.2928 | 3.74 | 525 | 5.6543 | 0.8917 |
| 0.5633 | 3.91 | 550 | 6.7064 | 0.8845 |
| 1.0431 | 4.09 | 575 | 4.9205 | 0.8953 |
| 0.2839 | 4.27 | 600 | 4.2344 | 0.8809 |
| 0.5464 | 4.45 | 625 | 4.9598 | 0.8809 |
| 0.0031 | 4.63 | 650 | 5.3705 | 0.8881 |
| 0.5149 | 4.8 | 675 | 4.8105 | 0.8845 |
| 0.2702 | 4.98 | 700 | 6.9958 | 0.8953 |
| 0.7503 | 5.16 | 725 | 5.4360 | 0.8881 |
| 0.2639 | 5.34 | 750 | 5.4420 | 0.8917 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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