Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use OrcaDB/bge-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OrcaDB/bge-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrcaDB/bge-base") 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] - Transformers
How to use OrcaDB/bge-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OrcaDB/bge-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OrcaDB/bge-base") model = AutoModel.from_pretrained("OrcaDB/bge-base") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -2
config.json
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"document_prompt":
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"query_prompt":
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"type_vocab_size": 2,
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"document_prompt": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"query_prompt": "Represent this sentence for searching relevant passages: ",
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"type_vocab_size": 2,
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