Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
echo
feature-extraction
echo-dsrn
linear-complexity
recurrent-hybrid
custom_code
Instructions to use ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
config: restore left padding — published MTEB benchmarks were measured with left padding; card parity is the priority (right-padded training lands in the next model version)
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -19,7 +19,7 @@
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"pad_to_multiple_of": null,
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"pad_token": "<|endoftext|>",
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"pad_token_type_id": 0,
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-
"padding_side": "
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "TokenizersBackend",
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"pad_to_multiple_of": null,
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"pad_token": "<|endoftext|>",
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"pad_token_type_id": 0,
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+
"padding_side": "left",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "TokenizersBackend",
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