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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_sst2_dense_epochs-6_exp_size_16
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9185779816513762
---
<!-- 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_sst2_dense_epochs-6_exp_size_16
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: 0.2618
- Accuracy: 0.9186
## 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: 32
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6583 | 0.02 | 50 | 0.6411 | 0.6193 |
| 0.328 | 0.05 | 100 | 0.2547 | 0.9106 |
| 0.263 | 0.07 | 150 | 0.2392 | 0.9140 |
| 0.2337 | 0.1 | 200 | 0.2295 | 0.9197 |
| 0.216 | 0.12 | 250 | 0.2372 | 0.9255 |
| 0.2194 | 0.14 | 300 | 0.2485 | 0.9186 |
| 0.2214 | 0.17 | 350 | 0.2209 | 0.9220 |
| 0.2094 | 0.19 | 400 | 0.2270 | 0.9220 |
| 0.226 | 0.21 | 450 | 0.2156 | 0.9209 |
| 0.1687 | 0.24 | 500 | 0.2618 | 0.9186 |
| 0.1758 | 0.26 | 550 | 0.2279 | 0.9186 |
| 0.2362 | 0.29 | 600 | 0.2314 | 0.9220 |
| 0.2323 | 0.31 | 650 | 0.2442 | 0.9197 |
| 0.1809 | 0.33 | 700 | 0.2065 | 0.9300 |
| 0.2871 | 0.36 | 750 | 0.2135 | 0.9289 |
| 0.16 | 0.38 | 800 | 0.2115 | 0.9243 |
| 0.1438 | 0.4 | 850 | 0.2287 | 0.9255 |
| 0.1732 | 0.43 | 900 | 0.2153 | 0.9255 |
| 0.1847 | 0.45 | 950 | 0.3193 | 0.9278 |
| 0.257 | 0.48 | 1000 | 0.3176 | 0.9289 |
| 0.127 | 0.5 | 1050 | 0.1962 | 0.9300 |
| 0.1791 | 0.52 | 1100 | 0.1928 | 0.9346 |
| 0.2533 | 0.55 | 1150 | 0.1890 | 0.9335 |
| 0.0762 | 0.57 | 1200 | 0.2866 | 0.9335 |
| 0.1358 | 0.59 | 1250 | 0.4125 | 0.9335 |
| 0.1385 | 0.62 | 1300 | 0.4090 | 0.9323 |
| 0.184 | 0.64 | 1350 | 0.5092 | 0.9369 |
| 0.1213 | 0.67 | 1400 | 0.5033 | 0.9404 |
| 0.1597 | 0.69 | 1450 | 0.5152 | 0.9381 |
| 0.1179 | 0.71 | 1500 | 0.3992 | 0.9381 |
| 0.1689 | 0.74 | 1550 | 0.5163 | 0.9381 |
| 0.1678 | 0.76 | 1600 | 0.5114 | 0.9404 |
| 0.1673 | 0.78 | 1650 | 0.2786 | 0.9369 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.14.1
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