metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: bert_imdb
results: []
datasets:
- stanfordnlp/imdb
bert_imdb
This model is a fine-tuned version of google-bert/bert-base-uncased on imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.3119
- Accuracy: 0.9403
- Recall: 0.9430
- Precision: 0.9379
To acccess my finetuning tutorial you can check the following repository.
Comparison with SOTA:
- DistilBERT 66M: 92.82
- BERT-base + ITPT: 95.63
- BERT-large: 95.49
Reference: Paperswithcode
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision |
|---|---|---|---|---|---|---|
| 0.2099 | 1.0 | 1563 | 0.2456 | 0.9102 | 0.8481 | 0.9683 |
| 0.1379 | 2.0 | 3126 | 0.2443 | 0.9274 | 0.8911 | 0.9608 |
| 0.0752 | 3.0 | 4689 | 0.2845 | 0.9391 | 0.9509 | 0.9290 |
| 0.0352 | 4.0 | 6252 | 0.3119 | 0.9403 | 0.9430 | 0.9379 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0