Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use lvwerra/distilbert-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lvwerra/distilbert-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lvwerra/distilbert-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lvwerra/distilbert-imdb") model = AutoModelForSequenceClassification.from_pretrained("lvwerra/distilbert-imdb") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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# distilbert-imdb
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1903
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- Accuracy: 0.928
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# distilbert-imdb
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset (training notebook is [here](https://huggingface.co/lvwerra/distilbert-imdb/blob/main/distilbert-imdb-training.ipynb)).
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It achieves the following results on the evaluation set:
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- Loss: 0.1903
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- Accuracy: 0.928
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