Instructions to use Minaaaa/deberta_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minaaaa/deberta_imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Minaaaa/deberta_imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Minaaaa/deberta_imdb") model = AutoModelForSequenceClassification.from_pretrained("Minaaaa/deberta_imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Minaaaa/deberta_imdb: direct link, hf CLI and curl.
- Browser
- Download file 574 Bytes
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https://huggingface.co/Minaaaa/deberta_imdb/resolve/main/README.md
- Command line
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hf download hf://Minaaaa/deberta_imdb/README.md
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curl -L -o README.md https://huggingface.co/Minaaaa/deberta_imdb/resolve/main/README.md
574 Bytes
| datasets: | |
| - imdb | |
| language: | |
| - en | |
| metrics: | |
| - accuracy | |
| library_name: transformers | |
| DeBERTa is a fine-tuned version of DeBERTa model. The dataset used for fine tuning is the IMDB dataset from Hugging Face. The number of instances used for training is 12 500, and 2 500 for testing. Metrics used for evaluation are classification oriented, because the model is used for sentiment analysis. The number of epochs used for training is 5, and the learning rate was set to lr = 5e-5. The results obtained are: | |
| Accuracy: 0.9312 | |
| Precision: 0.9268 | |
| Recall: 0.9371 | |
| F1-score: 0.9319 |