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
File size: 322 Bytes
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"min_value": 0.0,
"max_value": 5.0,
"metadata": {
"dataset": "amazingvince/the-stack-smol-xs-scored-and-annotated-all-llama",
"task": "regression",
"num_examples": 8264,
"stats": {
"mean": 2.8814133591481124,
"min": 0.0,
"max": 5.0,
"std_dev": 0.7840681151181967
}
}
} |