Instructions to use HydraLM/bge-large-classifier-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HydraLM/bge-large-classifier-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HydraLM/bge-large-classifier-32")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HydraLM/bge-large-classifier-32") model = AutoModelForSequenceClassification.from_pretrained("HydraLM/bge-large-classifier-32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1403ad3d1b15796dc46912fca1d8fc7deb3c4fcad3212d87657c7c7965988df4
- Size of remote file:
- 3.96 kB
- SHA256:
- 65fb81999f868eb2fc9a4bf5cd6f16b8bd7b12849555c7a5068d8afc87da741b
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