|
|
--- |
|
|
license: mit |
|
|
base_model: roberta-base |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- imdb |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: N_roberta_imdb_padding40model |
|
|
results: |
|
|
- task: |
|
|
name: Text Classification |
|
|
type: text-classification |
|
|
dataset: |
|
|
name: imdb |
|
|
type: imdb |
|
|
config: plain_text |
|
|
split: test |
|
|
args: plain_text |
|
|
metrics: |
|
|
- name: Accuracy |
|
|
type: accuracy |
|
|
value: 0.94952 |
|
|
--- |
|
|
|
|
|
<!-- 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_roberta_imdb_padding40model |
|
|
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.4922 |
|
|
- Accuracy: 0.9495 |
|
|
|
|
|
## 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 | |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
|
| 0.2081 | 1.0 | 1563 | 0.2432 | 0.9283 | |
|
|
| 0.1726 | 2.0 | 3126 | 0.1724 | 0.9493 | |
|
|
| 0.114 | 3.0 | 4689 | 0.2842 | 0.9384 | |
|
|
| 0.0767 | 4.0 | 6252 | 0.2583 | 0.9463 | |
|
|
| 0.0552 | 5.0 | 7815 | 0.3703 | 0.9420 | |
|
|
| 0.0357 | 6.0 | 9378 | 0.3342 | 0.9386 | |
|
|
| 0.0318 | 7.0 | 10941 | 0.3284 | 0.9462 | |
|
|
| 0.0316 | 8.0 | 12504 | 0.4194 | 0.9410 | |
|
|
| 0.0149 | 9.0 | 14067 | 0.4083 | 0.9483 | |
|
|
| 0.0175 | 10.0 | 15630 | 0.4237 | 0.9468 | |
|
|
| 0.0151 | 11.0 | 17193 | 0.4459 | 0.9457 | |
|
|
| 0.0113 | 12.0 | 18756 | 0.4569 | 0.9478 | |
|
|
| 0.0061 | 13.0 | 20319 | 0.4325 | 0.9482 | |
|
|
| 0.0034 | 14.0 | 21882 | 0.5188 | 0.9472 | |
|
|
| 0.0059 | 15.0 | 23445 | 0.4740 | 0.9484 | |
|
|
| 0.0078 | 16.0 | 25008 | 0.4421 | 0.9485 | |
|
|
| 0.0 | 17.0 | 26571 | 0.4819 | 0.9493 | |
|
|
| 0.0035 | 18.0 | 28134 | 0.4845 | 0.9492 | |
|
|
| 0.0 | 19.0 | 29697 | 0.5065 | 0.9486 | |
|
|
| 0.0013 | 20.0 | 31260 | 0.4922 | 0.9495 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.33.2 |
|
|
- Pytorch 2.0.1+cu117 |
|
|
- Datasets 2.14.5 |
|
|
- Tokenizers 0.13.3 |
|
|
|
