Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
German
bert
feature-extraction
information retrieval
ir
documents retrieval
passage retrieval
beir
benchmark
qrel
sts
semantic search
text-embeddings-inference
Instructions to use PM-AI/bi-encoder_msmarco_bert-base_german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PM-AI/bi-encoder_msmarco_bert-base_german with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PM-AI/bi-encoder_msmarco_bert-base_german") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use PM-AI/bi-encoder_msmarco_bert-base_german with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PM-AI/bi-encoder_msmarco_bert-base_german") model = AutoModel.from_pretrained("PM-AI/bi-encoder_msmarco_bert-base_german", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "max_len": 512, | |
| "name_or_path": "models/bi-encoder_mmarco-google-german_margin-mse_gbert-base_bs75-epochs10/", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": false, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |