Instructions to use Azese/distilbert-imdb-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azese/distilbert-imdb-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Azese/distilbert-imdb-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Azese/distilbert-imdb-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("Azese/distilbert-imdb-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
distilbert-imdb-sentiment-analysis
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6972
- eval_model_preparation_time: 0.0023
- eval_accuracy: 0.4067
- eval_f1: 0.4035
- eval_runtime: 8.4538
- eval_samples_per_second: 35.487
- eval_steps_per_second: 2.248
- step: 0
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: 16
- eval_batch_size: 16
- 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
Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
- Downloads last month
- -
Model tree for Azese/distilbert-imdb-sentiment-analysis
Base model
distilbert/distilbert-base-uncased