Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Toprak1yu/distilbert-imdb-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Toprak1yu/distilbert-imdb-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Toprak1yu/distilbert-imdb-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Toprak1yu/distilbert-imdb-classification") model = AutoModelForSequenceClassification.from_pretrained("Toprak1yu/distilbert-imdb-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.3647
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- Accuracy: 0.9205
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model-index:
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- name: results
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results: []
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datasets:
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- stanfordnlp/imdb
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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the stanfordnlp/imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3647
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- Accuracy: 0.9205
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