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vsmolyakov
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distilbert_imdb

Text Classification
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distilbert
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text-embeddings-inference
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="vsmolyakov/distilbert_imdb")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("vsmolyakov/distilbert_imdb")
model = AutoModelForSequenceClassification.from_pretrained("vsmolyakov/distilbert_imdb")
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distilbert_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: 0.2340
  • Accuracy: 0.9318

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2299 1.0 1563 0.1938 0.9265
0.1521 2.0 3126 0.2340 0.9318

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Model size
67M params
Tensor type
F32
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Dataset used to train vsmolyakov/distilbert_imdb

Evaluation results