Text Classification
sentence-transformers
Safetensors
English
bert
out-of-distribution-detection
few-shot-learning
neuro-symbolic
buddhist-logic
safety
text-embeddings-inference
Instructions to use EVAMLab/apoha-bge-small-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EVAMLab/apoha-bge-small-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EVAMLab/apoha-bge-small-en-v1.5") 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
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |