Instructions to use abscopaleffect2025/test-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abscopaleffect2025/test-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abscopaleffect2025/test-finetuned-imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abscopaleffect2025/test-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("abscopaleffect2025/test-finetuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
test-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.3973
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.6504 | 1.0 | 2 | 3.4787 |
| 3.7215 | 2.0 | 4 | 3.2854 |
| 3.4925 | 3.0 | 6 | 3.3148 |
| 3.644 | 4.0 | 8 | 3.7903 |
| 3.7121 | 5.0 | 10 | 3.5973 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for abscopaleffect2025/test-finetuned-imdb
Base model
distilbert/distilbert-base-uncased