Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use hwting/fintuned-distilbert-imdb-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hwting/fintuned-distilbert-imdb-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hwting/fintuned-distilbert-imdb-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hwting/fintuned-distilbert-imdb-classification") model = AutoModelForSequenceClassification.from_pretrained("hwting/fintuned-distilbert-imdb-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: hwting/distilbert-base-uncased-finetuned-imdb | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: fintuned-distilbert-imdb-classification | |
| 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. --> | |
| # fintuned-distilbert-imdb-classification | |
| This model is a fine-tuned version of [hwting/distilbert-base-uncased-finetuned-imdb](https://huggingface.co/hwting/distilbert-base-uncased-finetuned-imdb) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2013 | |
| - Accuracy: 0.9298 | |
| - F1: 0.9302 | |
| ## 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 | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.2433 | 1.0 | 391 | 0.2010 | 0.9218 | 0.9201 | | |
| | 0.1644 | 2.0 | 782 | 0.1867 | 0.9312 | 0.9311 | | |
| | 0.1117 | 3.0 | 1173 | 0.2013 | 0.9298 | 0.9302 | | |
| ### Framework versions | |
| - Transformers 5.8.0 | |
| - Pytorch 2.11.0+cu130 | |
| - Datasets 4.8.5 | |
| - Tokenizers 0.22.2 | |