Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistraldual
sentence-similarity
custom_code
Instructions to use GeoGPT-Research-Project/GeoEmbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GeoGPT-Research-Project/GeoEmbedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GeoGPT-Research-Project/GeoEmbedding", trust_remote_code=True) 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 GeoGPT-Research-Project/GeoEmbedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GeoGPT-Research-Project/GeoEmbedding", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GeoGPT-Research-Project/GeoEmbedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import MistralConfig, AutoConfig | |
| class MistralDualConfig(MistralConfig): | |
| model_type = "mistraldual" | |
| def __init__( | |
| self, | |
| use_cache=False, | |
| **kwargs, | |
| ): | |
| super().__init__(use_cache=use_cache, **kwargs) | |
| AutoConfig.register("mistraldual", MistralDualConfig) | |
| MistralDualConfig.register_for_auto_class() | |