Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use jdmartinev/imdb-distilbert-head-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdmartinev/imdb-distilbert-head-only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jdmartinev/imdb-distilbert-head-only")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jdmartinev/imdb-distilbert-head-only") model = AutoModelForSequenceClassification.from_pretrained("jdmartinev/imdb-distilbert-head-only") - Notebooks
- Google Colab
- Kaggle
imdb-distilbert-head-only
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: 0.4145
- Accuracy: 0.822
- F1 Macro: 0.8220
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.4628 | 1.0 | 250 | 0.4431 | 0.812 | 0.8120 |
| 0.4440 | 2.0 | 500 | 0.4145 | 0.822 | 0.8220 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.8.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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Model tree for jdmartinev/imdb-distilbert-head-only
Base model
distilbert/distilbert-base-uncased