|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imdb |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: distilbert-imdb |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: imdb |
|
type: imdb |
|
config: plain_text |
|
split: test[6250:12500]+test[-12500:-6250] |
|
args: plain_text |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.93024 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9302304818748904 |
|
- name: Precision |
|
type: precision |
|
value: 0.93047490567909 |
|
- name: Recall |
|
type: recall |
|
value: 0.93024 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# distilbert-imdb |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2118 |
|
- Accuracy: 0.9302 |
|
- F1: 0.9302 |
|
- Precision: 0.9305 |
|
- Recall: 0.9302 |
|
|
|
## 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: 9072 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 2 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.2803 | 1.0 | 782 | 0.1874 | 0.9276 | 0.9276 | 0.9276 | 0.9276 | |
|
| 0.1143 | 2.0 | 1564 | 0.2118 | 0.9302 | 0.9302 | 0.9305 | 0.9302 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.15.2 |
|
|
