Instructions to use brutusxu/distilbert-base-cross-encoder-first-p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brutusxu/distilbert-base-cross-encoder-first-p with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brutusxu/distilbert-base-cross-encoder-first-p")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brutusxu/distilbert-base-cross-encoder-first-p") model = AutoModelForSequenceClassification.from_pretrained("brutusxu/distilbert-base-cross-encoder-first-p", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 746bbd5dbcfe09f62aeb3d46d67c4b81da240019856f67825e41e1be56cedbd9
- Size of remote file:
- 268 MB
- SHA256:
- 6e124ff60ffa8749754b1b7deb4d62fb39b08cacf36c46afe8172c32c8c563ac
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