Feature Extraction
sentence-transformers
Safetensors
Transformers
mteb
modernbert
custom_code
Eval Results (legacy)
Instructions to use jxm/cde-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jxm/cde-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jxm/cde-small-v2", trust_remote_code=True) 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 jxm/cde-small-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/cde-small-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxm/cde-small-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Integrate with Sentence Transformers v5.4
#13
by tomaarsen HF Staff - opened
sentence_transformers_impl.py
CHANGED
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@@ -54,17 +54,21 @@ class Transformer(nn.Module):
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if config_args is None:
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config_args = {}
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if not model_args.get("trust_remote_code", False):
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raise ValueError(
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"You need to set `trust_remote_code=True` to load this model."
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)
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self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args
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self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config,
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self.tokenizer = AutoTokenizer.from_pretrained(
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model_name_or_path,
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cache_dir=cache_dir,
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**tokenizer_args,
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)
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if config_args is None:
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config_args = {}
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if cache_dir is not None:
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config_args["cache_dir"] = cache_dir
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model_args["cache_dir"] = cache_dir
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tokenizer_args["cache_dir"] = cache_dir
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if not model_args.get("trust_remote_code", False):
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raise ValueError(
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"You need to set `trust_remote_code=True` to load this model."
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)
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self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args)
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self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)
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self.tokenizer = AutoTokenizer.from_pretrained(
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model_name_or_path,
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**tokenizer_args,
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)
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