Instructions to use devangb4/distilbert-base-uncased-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devangb4/distilbert-base-uncased-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="devangb4/distilbert-base-uncased-finetuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("devangb4/distilbert-base-uncased-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("devangb4/distilbert-base-uncased-finetuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: distilbert-base-uncased-finetuned-imdb | |
| results: [] | |
| <!-- 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-base-uncased-finetuned-imdb | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.0981 | |
| - Model Preparation Time: 0.0016 | |
| ## 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: 3.0 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------------:| | |
| | 3.367 | 1.0 | 958 | 3.1578 | 0.0016 | | |
| | 3.2338 | 2.0 | 1916 | 3.1152 | 0.0016 | | |
| | 3.1885 | 3.0 | 2874 | 3.0898 | 0.0016 | | |
| ### Framework versions | |
| - Transformers 4.57.3 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 | |