README.md

metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - imdb
metrics:
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-imdb
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: imdb
          type: imdb
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9298

distilbert-base-uncased-finetuned-imdb

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:

• Loss: 0.1905
• Accuracy: 0.9298

Model description

More information needed

Intended uses & limitations

More information needed

How to Use

from transformers import pipeline

model_name = "avanishd/distilbert-base-uncased-finetuned-imdb"

classifier = pipeline("text-classification", model=model_name)

text = "I am happy"

prediction = classifier(text)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2573	1.0	1563	0.1905	0.9298

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1