File size: 3,272 Bytes
5718511 6ec6c71 5718511 6ec6c71 5718511 6ec6c71 5718511 6ec6c71 5718511 6ec6c71 5718511 6ec6c71 5718511 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-large_cola_dense_epochs-5
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Accuracy
type: accuracy
value: 0.8813559322033898
---
<!-- 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_cola_dense_epochs-5
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4167
- Accuracy: 0.8814
## 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: 2
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6749 | 0.19 | 10 | 0.6270 | 0.7095 |
| 0.5772 | 0.37 | 20 | 0.5947 | 0.7101 |
| 0.6066 | 0.56 | 30 | 0.5545 | 0.7101 |
| 0.5355 | 0.75 | 40 | 0.4788 | 0.7475 |
| 0.4398 | 0.93 | 50 | 0.3992 | 0.8469 |
| 0.3932 | 1.12 | 60 | 0.3737 | 0.8638 |
| 0.3756 | 1.31 | 70 | 0.3606 | 0.8650 |
| 0.4004 | 1.5 | 80 | 0.3645 | 0.8603 |
| 0.3198 | 1.68 | 90 | 0.3201 | 0.8749 |
| 0.3129 | 1.87 | 100 | 0.3638 | 0.8697 |
| 0.2763 | 2.06 | 110 | 0.3091 | 0.8819 |
| 0.3207 | 2.24 | 120 | 0.3781 | 0.8673 |
| 0.2614 | 2.43 | 130 | 0.3351 | 0.8773 |
| 0.2909 | 2.62 | 140 | 0.3404 | 0.8662 |
| 0.2899 | 2.8 | 150 | 0.3277 | 0.8796 |
| 0.2687 | 2.99 | 160 | 0.3520 | 0.8679 |
| 0.1993 | 3.18 | 170 | 0.3319 | 0.8854 |
| 0.2584 | 3.36 | 180 | 0.3901 | 0.8732 |
| 0.2502 | 3.55 | 190 | 0.3766 | 0.8773 |
| 0.2234 | 3.74 | 200 | 0.3360 | 0.8895 |
| 0.2101 | 3.93 | 210 | 0.3334 | 0.8849 |
| 0.1708 | 4.11 | 220 | 0.3819 | 0.8714 |
| 0.1664 | 4.3 | 230 | 0.3690 | 0.8773 |
| 0.2217 | 4.49 | 240 | 0.4181 | 0.8814 |
| 0.2034 | 4.67 | 250 | 0.3607 | 0.8796 |
| 0.1948 | 4.86 | 260 | 0.4167 | 0.8814 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|