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
File size: 681 Bytes
65ab0e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"_name_or_path": "models/bi-encoder_mmarco-google-german_margin-mse_gbert-base_bs75-epochs10/",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.20.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 31102
}
|