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
| { | |
| "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 | |
| } | |
| } | |
| } |