Sentence Similarity
sentence-transformers
ONNX
Safetensors
German
bert
feature-extraction
loss:MatryoshkaLoss
custom_code
text-embeddings-inference
Instructions to use aari1995/German_Semantic_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aari1995/German_Semantic_V3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aari1995/German_Semantic_V3", trust_remote_code=True) sentences = [ "Bundeskanzler.", "Angela Merkel.", "Olaf Scholz.", "Tino Chrupalla." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "aari1995/German_Semantic_V3", | |
| "architectures": [ | |
| "JinaBertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "attn_implementation": null, | |
| "auto_map": { | |
| "AutoConfig": "configuration_bert.JinaBertConfig", | |
| "AutoModel": "modeling_bert.JinaBertModel", | |
| "AutoModelForMaskedLM": "modeling_bert.JinaBertForMaskedLM", | |
| "AutoModelForSequenceClassification": "modeling_bert.JinaBertForSequenceClassification" | |
| }, | |
| "classifier_dropout": null, | |
| "emb_pooler": null, | |
| "feed_forward_type": "original", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 8192, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "alibi", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.42.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |