Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hum-Works/lodestone-base-4096-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hum-Works/lodestone-base-4096-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hum-Works/lodestone-base-4096-v1", 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] - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload Mosaic_Model.ipynb
This file required for replication is missing from huggingface and we got a question about itf8de74c
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Update README
Add in an additional package for setupe33bf52
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Using seq_length Config Rather than max_position_embeddings
Unverified signatureNeither of these settings are used to run the model, but are referenced by external spaces like the MTEB leaderboard. Since the model doesn't technically have a max_position_embeddings due to the way ALiBi works, its a little more accurate to report a seq_length.5e05a52
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Correct Sequence Length in Config.json
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Add seq_length to config.json to Display Properly on NTEB Leaderboard
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Add Consolidated MTEB CQADupstackRetrieval Benchmark to README
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Add CQADupstack Benchmarks
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Turn off Inference on HuggingFace as it Does Not Work Due to Remote Code
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Corrected Quick Start Instructions and Author Links
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Add Author Acknowledgements
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Initial Commit
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initial commit
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