Restore unchanged a8m-pt from 4606e9e8a489fd1e7bfe0bff703e3628d438bd17

README.md

@@ -0,0 +1,66 @@
+---
+language:
+- multilingual
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- embeddings
+- modernbert
+- matryoshka
+- multilingual
+- retrieval
+---
+
+# bekko-embedding-v1-a8m-pt
+
+Pretrained checkpoint for the bekko embedding v1 a8m release candidate. This checkpoint is the pre-fine-tuning model and is mainly useful as a base model for downstream fine-tuning or ablation work.
+
+This upload is based on the UniR v9 QAT pretraining run:
+
+`mmB-L4-bs8192-v1-20260301-QAT-unir_v9-full-sharedid-timing/20260504_183724/final`
+
+## Model Details
+
+- Architecture: ModernBERT-style encoder, 4 layers, hidden size 384
+- Pooling: mean pooling
+- Embedding dimension: 384
+- Maximum sequence length: 8192 tokens
+- Supported truncate dimensions: 384, 256, 128, 64
+
+The model was trained with Matryoshka representation learning, so embeddings can be truncated to the supported dimensions above. Use 384 dimensions when you need the full representation, and use 256, 128, or 64 dimensions for smaller indexes or faster retrieval.
+
+## Intended Use
+
+This is the pre-fine-tuning checkpoint. For most retrieval use cases, prefer the fine-tuned model:
+
+`hotchpotch/bekko-embedding-v1-a8m`
+
+Use this pretrain checkpoint when you need to:
+
+- reproduce or audit the fine-tuning recipe,
+- run downstream fine-tuning from the same base,
+- compare pretrain vs fine-tuned behavior,
+- run ablations against the UniR v9 QAT pretrain base.
+
+## Usage
+
+For retrieval, prefer `encode_query()` for queries and `encode_document()` for documents. Normalize embeddings when using cosine similarity or dot-product search on normalized vectors.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("hotchpotch/bekko-embedding-v1-a8m-pt")
+
+queries = ["What is multilingual retrieval?"]
+documents = ["Multilingual retrieval searches documents across languages."]
+
+query_embeddings = model.encode_query(queries, normalize_embeddings=True)
+document_embeddings = model.encode_document(documents, normalize_embeddings=True)
+
+scores = query_embeddings @ document_embeddings.T
+```
+
+## Notes
+
+This repository is intended for private evaluation and release-candidate validation. The model configuration in this upload is set for 8192-token inference.

config_sentence_transformers.json

@@ -11,4 +11,4 @@
   },
   "default_prompt_name": null,
   "similarity_fn_name": "cosine"
-}
+}

eval_result.json

@@ -0,0 +1,274 @@
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+    "nano_miracl_mean_ndcg@10": 0.6747877783323876
+  },
+  "timestamp_utc": "2026-07-06T18:00:23.553943+00:00"
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b4fe25becb21b90d7cca210d8446bbcc450dff2fee461c32baf41ef442dd2ef4
+oid sha256:b474c13e826f1652bffef917e1e3a1dccb172607087ccd0d5a0a61dc91c83cc7
 size 211952896

tokenizer_config.json

@@ -2004,12 +2004,12 @@
   "eos_token": "<eos>",
   "extra_special_tokens": {},
   "mask_token": "<mask>",
-  "max_length": 512,
+  "max_length": 8192,
   "model_input_names": [
     "input_ids",
     "attention_mask"
   ],
-  "model_max_length": 512,
+  "model_max_length": 8192,
   "pad_to_multiple_of": null,
   "pad_token": "<pad>",
   "pad_token_type_id": 0,