Feature Extraction
sentence-transformers
Safetensors
Transformers
mistral
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-en-icl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-en-icl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-en-icl") 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 BAAI/bge-en-icl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-en-icl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-en-icl") model = AutoModel.from_pretrained("BAAI/bge-en-icl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Make compatible to sentence-transformers (#10)
Browse files- Create 1_Pooling/config.json (0f70498f958347edab17f3b8779f9ecdcd1eeadc)
- Update 1_Pooling/config.json (a67fee46332d9fdc30942ae03ed1f6b0b032547a)
- Create sentence_bert_config.json (a2d0739b4e768032c2d0d1829ad1d5379bdda27c)
- Create modules.json (be17b6f9ac568ce4a977495f80968e53b999c3a8)
- Update sentence_bert_config.json (0eb14a491b64ce772c9def7f0ef3087b9f7e4588)
- Update tokenizer_config.json (d1a50b3624e901f12f9f4808126c30f6b01247a0)
- Update modules.json (807090377c246ba0eb0c6340fb6bbf7cca224a8a)
Co-authored-by: Michael <michaelfeil@users.noreply.huggingface.co>
- 1_Pooling/config.json +10 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- tokenizer_config.json +1 -1
1_Pooling/config.json
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{
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"word_embedding_dimension": 4096,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": true,
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"include_prompt": true
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}
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 32768,
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"do_lower_case": false
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}
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tokenizer_config.json
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length":
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"pad_token": "<unk>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 32768,
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"pad_token": "<unk>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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