Text Classification
Transformers
Safetensors
English
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use lucas-lage/ReSB2-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucas-lage/ReSB2-Cross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lucas-lage/ReSB2-Cross-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lucas-lage/ReSB2-Cross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("lucas-lage/ReSB2-Cross-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4e5d4ffa9d5652bdee97a0f09653c55e0c624eccbb9cd02b2b8ea24d681102b
- Size of remote file:
- 5.84 kB
- SHA256:
- 0a3733c7eca6ad6dff61dc00a64df83bc93b9ee6e71d9dc8c3fd0de7ec6e3fc9
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