Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest") 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 avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest") model = AutoModel.from_pretrained("avsolatorio/00-600-11-1-6-2-0-0-768-512-cm_NOI_BAAI_bge-base-en-v1.5-20240704035539-latest") - Notebooks
- Google Colab
- Kaggle
File size: 740 Bytes
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"_name_or_path": "BAAI/bge-base-en-v1.5",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
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"id2label": {
"0": "LABEL_0"
},
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},
"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": "4.40.1",
"type_vocab_size": 2,
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"vocab_size": 30522
}
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