Text Classification
Transformers
Safetensors
bert
Generated from Trainer
custom_code
text-embeddings-inference
Instructions to use amazingvince/jina_embeddings_v2_base_code_multi_regression-simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amazingvince/jina_embeddings_v2_base_code_multi_regression-simple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amazingvince/jina_embeddings_v2_base_code_multi_regression-simple", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amazingvince/jina_embeddings_v2_base_code_multi_regression-simple", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("amazingvince/jina_embeddings_v2_base_code_multi_regression-simple", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 290f7be0baa3cebaaa06da96ed753b15ffbb01504325813a83c0f6109c315caa
- Size of remote file:
- 5.3 kB
- SHA256:
- 24b557a0a3a9b49eadc6ee06c77f0ff34a6360fdac18b7da6c0299f5a4d3dce7
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