Sentence Similarity
sentence-transformers
Safetensors
Turkish
xlm-roberta
turkish
embeddings
retrieval
distillation
text-embeddings-inference
Instructions to use fredoline005/ajan-embed-q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fredoline005/ajan-embed-q with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fredoline005/ajan-embed-q") 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] - Notebooks
- Google Colab
- Kaggle
Limitations: the undistilled-e5-base ablation has been run, it is in the table
Browse filesThe limitations section still said the ablation was not yet run while the benchmark table above it reported that ablation. Corrected, and states plainly that the ablation beats this model on the 4-task average and loses on retrieval.
README.md
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@@ -63,8 +63,10 @@ Use `query: ` / `passage: ` prefixes for retrieval (inherited from the e5 family
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## Limitations (honest)
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- Benchmarked on **2 Turkish tasks, no confidence intervals** — margins are modest.
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- The win over e5-small is partly the larger base
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- Distillation is bounded by the teacher; the native-Turkish-tokenizer edge is v1.
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- Pin a corpus snapshot + add PII/dedup filtering for a production retrain.
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## Limitations (honest)
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- Benchmarked on **2 Turkish tasks, no confidence intervals** — margins are modest.
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- The win over e5-small is partly the larger base. The undistilled-e5-base ablation
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HAS now been run and is in the table above: it beats this model on the 4-task
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average (0.624 vs 0.613) and loses to it on retrieval (0.444 vs 0.465). Distillation
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bought retrieval and cost general STS.
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- Distillation is bounded by the teacher; the native-Turkish-tokenizer edge is v1.
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- Pin a corpus snapshot + add PII/dedup filtering for a production retrain.
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