Instructions to use facebook/spar-wiki-bm25-lexmodel-context-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/spar-wiki-bm25-lexmodel-context-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/spar-wiki-bm25-lexmodel-context-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/spar-wiki-bm25-lexmodel-context-encoder") model = AutoModel.from_pretrained("facebook/spar-wiki-bm25-lexmodel-context-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
typo fix for lambda tokens
#1
by berkatil - opened
README.md
CHANGED
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@@ -91,9 +91,9 @@ dpr_ctx_emb = dpr_ctx_encoder(**dpr_ctx_input).pooler_output
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# Compute Λ embeddings
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lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt')
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lexmodel_query_emb = lexmodel_query_encoder(**
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lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
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lexmodel_ctx_emb = lexmodel_context_encoder(**
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# Form SPAR embeddings via concatenation
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# Compute Λ embeddings
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lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt')
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lexmodel_query_emb = lexmodel_query_encoder(**lexmodel_query_input).last_hidden_state[:, 0, :]
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lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
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lexmodel_ctx_emb = lexmodel_context_encoder(**lexmodel_ctx_input).last_hidden_state[:, 0, :]
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# Form SPAR embeddings via concatenation
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