Instructions to use justmeomeo1/imdb_review_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use justmeomeo1/imdb_review_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="justmeomeo1/imdb_review_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("justmeomeo1/imdb_review_classification") model = AutoModelForSequenceClassification.from_pretrained("justmeomeo1/imdb_review_classification") - Notebooks
- Google Colab
- Kaggle
imdb_review_classification
This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3313
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2774 | 1.0 | 2813 | 0.2867 |
| 0.1256 | 2.0 | 5626 | 0.3313 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 3.3.2
- Tokenizers 0.22.1
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Model tree for justmeomeo1/imdb_review_classification
Base model
google-bert/bert-base-uncased