Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use HaidarJomaa/Space-Time-MiniLM-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HaidarJomaa/Space-Time-MiniLM-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HaidarJomaa/Space-Time-MiniLM-v0") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use HaidarJomaa/Space-Time-MiniLM-v0 with Transformers:
# Load model directly from transformers import AutoTokenizer, SpaceTimeMiniLM tokenizer = AutoTokenizer.from_pretrained("HaidarJomaa/Space-Time-MiniLM-v0") model = SpaceTimeMiniLM.from_pretrained("HaidarJomaa/Space-Time-MiniLM-v0") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "nreimers/MiniLM-L6-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer", "model_max_length": 512} |