File size: 2,895 Bytes
5c5e1b2 |
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 |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_bert_agnews_padding20model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9481578947368421
---
<!-- 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. -->
# N_bert_agnews_padding20model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.9482
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.178 | 1.0 | 7500 | 0.2016 | 0.9387 |
| 0.1359 | 2.0 | 15000 | 0.1994 | 0.9463 |
| 0.1199 | 3.0 | 22500 | 0.2296 | 0.9439 |
| 0.0893 | 4.0 | 30000 | 0.2822 | 0.9433 |
| 0.0632 | 5.0 | 37500 | 0.2953 | 0.9384 |
| 0.0441 | 6.0 | 45000 | 0.3583 | 0.9458 |
| 0.0337 | 7.0 | 52500 | 0.3966 | 0.9433 |
| 0.0287 | 8.0 | 60000 | 0.4296 | 0.9434 |
| 0.0241 | 9.0 | 67500 | 0.4442 | 0.9414 |
| 0.0118 | 10.0 | 75000 | 0.5066 | 0.9405 |
| 0.0166 | 11.0 | 82500 | 0.4644 | 0.94 |
| 0.0118 | 12.0 | 90000 | 0.4789 | 0.9409 |
| 0.0115 | 13.0 | 97500 | 0.5151 | 0.9443 |
| 0.0075 | 14.0 | 105000 | 0.4855 | 0.9458 |
| 0.007 | 15.0 | 112500 | 0.5377 | 0.9430 |
| 0.0058 | 16.0 | 120000 | 0.5308 | 0.9458 |
| 0.0024 | 17.0 | 127500 | 0.5328 | 0.9451 |
| 0.0014 | 18.0 | 135000 | 0.5569 | 0.9462 |
| 0.0023 | 19.0 | 142500 | 0.5646 | 0.9480 |
| 0.0019 | 20.0 | 150000 | 0.5675 | 0.9482 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|