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
| { | |
| "epoch": 2.9963706750544397, | |
| "eval_loss": 0.6114735007286072, | |
| "eval_mse": 0.6114734868893679, | |
| "eval_runtime": 13.3226, | |
| "eval_samples": 435, | |
| "eval_samples_per_second": 32.651, | |
| "eval_steps_per_second": 16.363 | |
| } |