Add fine-tuned BGE-M3 for banking domain

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
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+    "pooling_mode_weightedmean_tokens": false,
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+    "include_prompt": true
+}

README.md

@@ -0,0 +1,28 @@
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+base_model: BAAI/bge-m3
+tags:
+- retrieval
+- banking
+- bge
+- multilingual
+- sentence-transformers
+license: apache-2.0
+---
+
+# bge_m3_multiplenegativesrankingloss
+
+Fine-tuned from **BAAI/bge-m3** cho domain ngân hàng (banking).
+Dùng cho: semantic search / retrieval / similarity.
+
+## Cách dùng (Sentence-Transformers)
+
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("duylb54/bge_m3_multiplenegativesrankingloss")
+emb = model.encode(
+    ["Điều kiện mở thẻ tín dụng là gì?"],
+    normalize_embeddings=True,
+    convert_to_tensor=True
+)

config.json

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+    "XLMRobertaModel"
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+  "layer_norm_eps": 1e-05,
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+  "__version__": {
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+    "transformers": "4.55.3",
+    "pytorch": "2.6.0+cu124"
+  },
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+  "prompts": {
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+    "document": ""
+  },
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+  "similarity_fn_name": "cosine"
+}

model.safetensors

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modules.json

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+    "path": "",
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+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+}

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tokenizer_config.json

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