Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
MTEB
CMTEB
Transformers
Retrieval
STS
Classification
Clustering
custom_code
Eval Results
text-embeddings-inference
Instructions to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2", 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] - Notebooks
- Google Colab
- Kaggle
| { | |
| "dataset_revision": "20a6d6f312dd54037fe07a32d58e5e168867909d", | |
| "evaluation_time": 19.678945302963257, | |
| "kg_co2_emissions": null, | |
| "mteb_version": "1.14.5", | |
| "scores": { | |
| "test": [ | |
| { | |
| "cosine_pearson": 0.8228565793964485, | |
| "cosine_spearman": 0.7985114978228225, | |
| "euclidean_pearson": 0.7992199480089693, | |
| "euclidean_spearman": 0.798491830362091, | |
| "hf_subset": "default", | |
| "languages": [ | |
| "eng-Latn" | |
| ], | |
| "main_score": 0.7985114978228225, | |
| "manhattan_pearson": 0.7987158690228332, | |
| "manhattan_spearman": 0.797563582169067, | |
| "pearson": 0.8228565793964485, | |
| "spearman": 0.7985114978228225 | |
| } | |
| ] | |
| }, | |
| "task_name": "SICK-R" | |
| } |