metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-imdb
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: 0.93956
- name: F1
type: f1
value: 0.9395537111681099
- name: Precision
type: precision
value: 0.939743003448315
- name: Recall
type: recall
value: 0.93956
bert-imdb
This model is a fine-tuned version of bert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2266
- Accuracy: 0.9396
- F1: 0.9396
- Precision: 0.9397
- Recall: 0.9396
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2223 | 1.0 | 1563 | 0.1898 | 0.9328 | 0.9327 | 0.9331 | 0.9328 |
| 0.1161 | 2.0 | 3126 | 0.2266 | 0.9396 | 0.9396 | 0.9397 | 0.9396 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2