Instructions to use AbstractQbit/electra_large_imdb_regression_htsplice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractQbit/electra_large_imdb_regression_htsplice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AbstractQbit/electra_large_imdb_regression_htsplice")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AbstractQbit/electra_large_imdb_regression_htsplice") model = AutoModelForSequenceClassification.from_pretrained("AbstractQbit/electra_large_imdb_regression_htsplice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from AbstractQbit/electra_large_imdb_regression_htsplice: direct link, hf CLI and curl.
- Browser
- Download file 2.68 GB
-
https://huggingface.co/AbstractQbit/electra_large_imdb_regression_htsplice/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://AbstractQbit/electra_large_imdb_regression_htsplice@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/AbstractQbit/electra_large_imdb_regression_htsplice/resolve/refs%2Fpr%2F1/optimizer.pt
2.68 GB
- Xet hash:
- e134c84a37103a56d2eb75c93b086869866b17ec46f5edd45850d12613acba28
- Size of remote file:
- 2.68 GB
- SHA256:
- c89364d8b1c09caedd45a8e54f159a9c620b5b98691d69c90010bd614e0b78d7
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