Feature Extraction
sentence-transformers
OpenVINO
Transformers
Chinese
bert
sentence-similarity
nncf
8-bit precision
text-embeddings-inference
Instructions to use turingevo/bge-base-zh-v1.5-openvino-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use turingevo/bge-base-zh-v1.5-openvino-8bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("turingevo/bge-base-zh-v1.5-openvino-8bit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use turingevo/bge-base-zh-v1.5-openvino-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="turingevo/bge-base-zh-v1.5-openvino-8bit")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("turingevo/bge-base-zh-v1.5-openvino-8bit") model = AutoModel.from_pretrained("turingevo/bge-base-zh-v1.5-openvino-8bit") - Notebooks
- Google Colab
- Kaggle
| { | |
| "dtype": "int8", | |
| "input_info": null, | |
| "optimum_version": "1.25.2", | |
| "quantization_config": { | |
| "all_layers": null, | |
| "backup_precision": null, | |
| "bits": 8, | |
| "dataset": null, | |
| "dtype": "int8", | |
| "gptq": null, | |
| "group_size": -1, | |
| "ignored_scope": null, | |
| "lora_correction": null, | |
| "num_samples": null, | |
| "processor": null, | |
| "quant_method": "default", | |
| "ratio": 1.0, | |
| "scale_estimation": null, | |
| "sensitivity_metric": null, | |
| "sym": false, | |
| "tokenizer": null, | |
| "trust_remote_code": false | |
| }, | |
| "save_onnx_model": false, | |
| "transformers_version": "4.46.3" | |
| } | |