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---
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:972
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- loss:MatryoshkaLoss
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- loss:MultipleNegativesRankingLoss
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base_model: keepitreal/vietnamese-sbert
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widget:
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- source_sentence: Giày Boot Nữ Dr. Martens 1460
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sentences:
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- Boot nữ đế chống nước và dầu
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- chống nắng an toàn cho da nhạy cảm không gây kích ứng
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- sản phẩm đi kèm hộp, dây sạc Type-C và sách hướng dẫn chi tiết
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- source_sentence: Giày Sneaker Nike Air Force 1
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sentences:
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- quần tây nam vải co giãn nhẹ, linh hoạt khi di chuyển
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- quần short thể thao nữ vải mát lạnh, co giãn cực tốt
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- Giày sneaker Air Force 1 logo Swoosh
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- source_sentence: Giày Loafer Da Bóng Nam Cổ Điển
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sentences:
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- đế cao su chống trượt giúp di chuyển an toàn
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- Sony WH-1000XM5 hỗ trợ Hi-Res Audio Wireless
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- màn hình cảm ứng 13.4 inch tỉ lệ 16:10, độ phân giải 4K UHD+ sắc nét, màu sống
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động, góc nhìn rộng
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- source_sentence: Giày Cao Gót Christian Louboutin
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sentences:
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- rửa mặt bằng sữa Some By Mi giúp kiểm soát dầu hiệu quả
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- quần tây nữ xếp ly phía trước tạo hiệu ứng dáng cao
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- Giày Louboutin lót mềm thoải mái
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- source_sentence: Áo Sơ Mi Nữ Tay Dài Họa Tiết Caro
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sentences:
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- áo sơ mi nữ tay dài họa tiết caro nhỏ cổ điển trẻ trung
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- khuy cài phía trước được thiết kế ẩn tinh tế
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- các thiết kế như cạp cao tôn dáng, gấu tua rua, chi tiết rách nhẹ
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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model-index:
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- name: SentenceTransformer based on keepitreal/vietnamese-sbert
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dim 768
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type: dim_768
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metrics:
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- type: cosine_accuracy@1
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|
value: 0.0
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|
name: Cosine Accuracy@1
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|
- type: cosine_accuracy@3
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|
value: 0.009259259259259259
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|
|
name: Cosine Accuracy@3
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|
- type: cosine_accuracy@5
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value: 0.027777777777777776
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
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value: 0.2777777777777778
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name: Cosine Accuracy@10
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|
- type: cosine_precision@1
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|
value: 0.0
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|
name: Cosine Precision@1
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- type: cosine_precision@3
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|
value: 0.0030864197530864196
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name: Cosine Precision@3
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|
- type: cosine_precision@5
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value: 0.005555555555555557
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name: Cosine Precision@5
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- type: cosine_precision@10
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value: 0.027777777777777783
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.0
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.009259259259259259
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name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.027777777777777776
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.2777777777777778
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name: Cosine Recall@10
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|
- type: cosine_ndcg@10
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value: 0.09190266762601638
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name: Cosine Ndcg@10
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|
|
- type: cosine_mrr@10
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value: 0.03934817754262198
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name: Cosine Mrr@10
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|
|
- type: cosine_map@100
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value: 0.049514242466381884
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|
name: Cosine Map@100
|
|
|
- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dim 512
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type: dim_512
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metrics:
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- type: cosine_accuracy@1
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value: 0.0
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|
name: Cosine Accuracy@1
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|
- type: cosine_accuracy@3
|
|
|
value: 0.009259259259259259
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|
name: Cosine Accuracy@3
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|
- type: cosine_accuracy@5
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Accuracy@5
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|
- type: cosine_accuracy@10
|
|
|
value: 0.25925925925925924
|
|
|
name: Cosine Accuracy@10
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|
- type: cosine_precision@1
|
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|
value: 0.0
|
|
|
name: Cosine Precision@1
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|
|
- type: cosine_precision@3
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|
value: 0.0030864197530864196
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|
|
name: Cosine Precision@3
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|
|
- type: cosine_precision@5
|
|
|
value: 0.001851851851851852
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.02592592592592593
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|
|
name: Cosine Precision@10
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|
|
- type: cosine_recall@1
|
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|
value: 0.0
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Recall@3
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|
|
- type: cosine_recall@5
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.25925925925925924
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.08541654381551299
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.03630217519106408
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.04747912217641579
|
|
|
name: Cosine Map@100
|
|
|
- task:
|
|
|
type: information-retrieval
|
|
|
name: Information Retrieval
|
|
|
dataset:
|
|
|
name: dim 256
|
|
|
type: dim_256
|
|
|
metrics:
|
|
|
- type: cosine_accuracy@1
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Accuracy@1
|
|
|
- type: cosine_accuracy@3
|
|
|
value: 0.018518518518518517
|
|
|
name: Cosine Accuracy@3
|
|
|
- type: cosine_accuracy@5
|
|
|
value: 0.018518518518518517
|
|
|
name: Cosine Accuracy@5
|
|
|
- type: cosine_accuracy@10
|
|
|
value: 0.26851851851851855
|
|
|
name: Cosine Accuracy@10
|
|
|
- type: cosine_precision@1
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Precision@1
|
|
|
- type: cosine_precision@3
|
|
|
value: 0.006172839506172839
|
|
|
name: Cosine Precision@3
|
|
|
- type: cosine_precision@5
|
|
|
value: 0.003703703703703704
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.026851851851851856
|
|
|
name: Cosine Precision@10
|
|
|
- type: cosine_recall@1
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.018518518518518517
|
|
|
name: Cosine Recall@3
|
|
|
- type: cosine_recall@5
|
|
|
value: 0.018518518518518517
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.26851851851851855
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.09499080067258153
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.046222810111699
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.05635122599163806
|
|
|
name: Cosine Map@100
|
|
|
- task:
|
|
|
type: information-retrieval
|
|
|
name: Information Retrieval
|
|
|
dataset:
|
|
|
name: dim 128
|
|
|
type: dim_128
|
|
|
metrics:
|
|
|
- type: cosine_accuracy@1
|
|
|
value: 0.0
|
|
|
name: Cosine Accuracy@1
|
|
|
- type: cosine_accuracy@3
|
|
|
value: 0.0
|
|
|
name: Cosine Accuracy@3
|
|
|
- type: cosine_accuracy@5
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Accuracy@5
|
|
|
- type: cosine_accuracy@10
|
|
|
value: 0.24074074074074073
|
|
|
name: Cosine Accuracy@10
|
|
|
- type: cosine_precision@1
|
|
|
value: 0.0
|
|
|
name: Cosine Precision@1
|
|
|
- type: cosine_precision@3
|
|
|
value: 0.0
|
|
|
name: Cosine Precision@3
|
|
|
- type: cosine_precision@5
|
|
|
value: 0.001851851851851852
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.024074074074074074
|
|
|
name: Cosine Precision@10
|
|
|
- type: cosine_recall@1
|
|
|
value: 0.0
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.0
|
|
|
name: Cosine Recall@3
|
|
|
- type: cosine_recall@5
|
|
|
value: 0.009259259259259259
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.24074074074074073
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.07767536292853462
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.03192239858906525
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.042847271207784025
|
|
|
name: Cosine Map@100
|
|
|
- task:
|
|
|
type: information-retrieval
|
|
|
name: Information Retrieval
|
|
|
dataset:
|
|
|
name: dim 64
|
|
|
type: dim_64
|
|
|
metrics:
|
|
|
- type: cosine_accuracy@1
|
|
|
value: 0.0
|
|
|
name: Cosine Accuracy@1
|
|
|
- type: cosine_accuracy@3
|
|
|
value: 0.0
|
|
|
name: Cosine Accuracy@3
|
|
|
- type: cosine_accuracy@5
|
|
|
value: 0.0
|
|
|
name: Cosine Accuracy@5
|
|
|
- type: cosine_accuracy@10
|
|
|
value: 0.2222222222222222
|
|
|
name: Cosine Accuracy@10
|
|
|
- type: cosine_precision@1
|
|
|
value: 0.0
|
|
|
name: Cosine Precision@1
|
|
|
- type: cosine_precision@3
|
|
|
value: 0.0
|
|
|
name: Cosine Precision@3
|
|
|
- type: cosine_precision@5
|
|
|
value: 0.0
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.022222222222222223
|
|
|
name: Cosine Precision@10
|
|
|
- type: cosine_recall@1
|
|
|
value: 0.0
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.0
|
|
|
name: Cosine Recall@3
|
|
|
- type: cosine_recall@5
|
|
|
value: 0.0
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.2222222222222222
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.0710622436721403
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.028813932980599647
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.039492961403908525
|
|
|
name: Cosine Map@100
|
|
|
---
|
|
|
|
|
|
# SentenceTransformer based on keepitreal/vietnamese-sbert
|
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
|
|
|
|
|
## Model Details
|
|
|
|
|
|
### Model Description
|
|
|
- **Model Type:** Sentence Transformer
|
|
|
- **Base model:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 -->
|
|
|
- **Maximum Sequence Length:** 256 tokens
|
|
|
- **Output Dimensionality:** 768 dimensions
|
|
|
- **Similarity Function:** Cosine Similarity
|
|
|
- **Training Dataset:**
|
|
|
- json
|
|
|
<!-- - **Language:** Unknown -->
|
|
|
<!-- - **License:** Unknown -->
|
|
|
|
|
|
### Model Sources
|
|
|
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
|
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
|
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
|
|
|
|
|
### Full Model Architecture
|
|
|
|
|
|
```
|
|
|
SentenceTransformer(
|
|
|
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
|
|
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
|
|
)
|
|
|
```
|
|
|
|
|
|
## Usage
|
|
|
|
|
|
### Direct Usage (Sentence Transformers)
|
|
|
|
|
|
First install the Sentence Transformers library:
|
|
|
|
|
|
```bash
|
|
|
pip install -U sentence-transformers
|
|
|
```
|
|
|
|
|
|
Then you can load this model and run inference.
|
|
|
```python
|
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
|
|
# Download from the 🤗 Hub
|
|
|
model = SentenceTransformer("NghiBuine/search-ecommerce-product-model")
|
|
|
# Run inference
|
|
|
sentences = [
|
|
|
'Áo Sơ Mi Nữ Tay Dài Họa Tiết Caro',
|
|
|
'áo sơ mi nữ tay dài họa tiết caro nhỏ cổ điển trẻ trung',
|
|
|
'các thiết kế như cạp cao tôn dáng, gấu tua rua, chi tiết rách nhẹ',
|
|
|
]
|
|
|
embeddings = model.encode(sentences)
|
|
|
print(embeddings.shape)
|
|
|
# [3, 768]
|
|
|
|
|
|
# Get the similarity scores for the embeddings
|
|
|
similarities = model.similarity(embeddings, embeddings)
|
|
|
print(similarities.shape)
|
|
|
# [3, 3]
|
|
|
```
|
|
|
|
|
|
<!--
|
|
|
### Direct Usage (Transformers)
|
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
|
|
|
</details>
|
|
|
-->
|
|
|
|
|
|
<!--
|
|
|
### Downstream Usage (Sentence Transformers)
|
|
|
|
|
|
You can finetune this model on your own dataset.
|
|
|
|
|
|
<details><summary>Click to expand</summary>
|
|
|
|
|
|
</details>
|
|
|
-->
|
|
|
|
|
|
<!--
|
|
|
### Out-of-Scope Use
|
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
|
|
-->
|
|
|
|
|
|
## Evaluation
|
|
|
|
|
|
### Metrics
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Dataset: `dim_768`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"truncate_dim": 768
|
|
|
}
|
|
|
```
|
|
|
|
|
|
| Metric | Value |
|
|
|
|:--------------------|:-----------|
|
|
|
| cosine_accuracy@1 | 0.0 |
|
|
|
| cosine_accuracy@3 | 0.0093 |
|
|
|
| cosine_accuracy@5 | 0.0278 |
|
|
|
| cosine_accuracy@10 | 0.2778 |
|
|
|
| cosine_precision@1 | 0.0 |
|
|
|
| cosine_precision@3 | 0.0031 |
|
|
|
| cosine_precision@5 | 0.0056 |
|
|
|
| cosine_precision@10 | 0.0278 |
|
|
|
| cosine_recall@1 | 0.0 |
|
|
|
| cosine_recall@3 | 0.0093 |
|
|
|
| cosine_recall@5 | 0.0278 |
|
|
|
| cosine_recall@10 | 0.2778 |
|
|
|
| **cosine_ndcg@10** | **0.0919** |
|
|
|
| cosine_mrr@10 | 0.0393 |
|
|
|
| cosine_map@100 | 0.0495 |
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Dataset: `dim_512`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"truncate_dim": 512
|
|
|
}
|
|
|
```
|
|
|
|
|
|
| Metric | Value |
|
|
|
|:--------------------|:-----------|
|
|
|
| cosine_accuracy@1 | 0.0 |
|
|
|
| cosine_accuracy@3 | 0.0093 |
|
|
|
| cosine_accuracy@5 | 0.0093 |
|
|
|
| cosine_accuracy@10 | 0.2593 |
|
|
|
| cosine_precision@1 | 0.0 |
|
|
|
| cosine_precision@3 | 0.0031 |
|
|
|
| cosine_precision@5 | 0.0019 |
|
|
|
| cosine_precision@10 | 0.0259 |
|
|
|
| cosine_recall@1 | 0.0 |
|
|
|
| cosine_recall@3 | 0.0093 |
|
|
|
| cosine_recall@5 | 0.0093 |
|
|
|
| cosine_recall@10 | 0.2593 |
|
|
|
| **cosine_ndcg@10** | **0.0854** |
|
|
|
| cosine_mrr@10 | 0.0363 |
|
|
|
| cosine_map@100 | 0.0475 |
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Dataset: `dim_256`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"truncate_dim": 256
|
|
|
}
|
|
|
```
|
|
|
|
|
|
| Metric | Value |
|
|
|
|:--------------------|:----------|
|
|
|
| cosine_accuracy@1 | 0.0093 |
|
|
|
| cosine_accuracy@3 | 0.0185 |
|
|
|
| cosine_accuracy@5 | 0.0185 |
|
|
|
| cosine_accuracy@10 | 0.2685 |
|
|
|
| cosine_precision@1 | 0.0093 |
|
|
|
| cosine_precision@3 | 0.0062 |
|
|
|
| cosine_precision@5 | 0.0037 |
|
|
|
| cosine_precision@10 | 0.0269 |
|
|
|
| cosine_recall@1 | 0.0093 |
|
|
|
| cosine_recall@3 | 0.0185 |
|
|
|
| cosine_recall@5 | 0.0185 |
|
|
|
| cosine_recall@10 | 0.2685 |
|
|
|
| **cosine_ndcg@10** | **0.095** |
|
|
|
| cosine_mrr@10 | 0.0462 |
|
|
|
| cosine_map@100 | 0.0564 |
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Dataset: `dim_128`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"truncate_dim": 128
|
|
|
}
|
|
|
```
|
|
|
|
|
|
| Metric | Value |
|
|
|
|:--------------------|:-----------|
|
|
|
| cosine_accuracy@1 | 0.0 |
|
|
|
| cosine_accuracy@3 | 0.0 |
|
|
|
| cosine_accuracy@5 | 0.0093 |
|
|
|
| cosine_accuracy@10 | 0.2407 |
|
|
|
| cosine_precision@1 | 0.0 |
|
|
|
| cosine_precision@3 | 0.0 |
|
|
|
| cosine_precision@5 | 0.0019 |
|
|
|
| cosine_precision@10 | 0.0241 |
|
|
|
| cosine_recall@1 | 0.0 |
|
|
|
| cosine_recall@3 | 0.0 |
|
|
|
| cosine_recall@5 | 0.0093 |
|
|
|
| cosine_recall@10 | 0.2407 |
|
|
|
| **cosine_ndcg@10** | **0.0777** |
|
|
|
| cosine_mrr@10 | 0.0319 |
|
|
|
| cosine_map@100 | 0.0428 |
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Dataset: `dim_64`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"truncate_dim": 64
|
|
|
}
|
|
|
```
|
|
|
|
|
|
| Metric | Value |
|
|
|
|:--------------------|:-----------|
|
|
|
| cosine_accuracy@1 | 0.0 |
|
|
|
| cosine_accuracy@3 | 0.0 |
|
|
|
| cosine_accuracy@5 | 0.0 |
|
|
|
| cosine_accuracy@10 | 0.2222 |
|
|
|
| cosine_precision@1 | 0.0 |
|
|
|
| cosine_precision@3 | 0.0 |
|
|
|
| cosine_precision@5 | 0.0 |
|
|
|
| cosine_precision@10 | 0.0222 |
|
|
|
| cosine_recall@1 | 0.0 |
|
|
|
| cosine_recall@3 | 0.0 |
|
|
|
| cosine_recall@5 | 0.0 |
|
|
|
| cosine_recall@10 | 0.2222 |
|
|
|
| **cosine_ndcg@10** | **0.0711** |
|
|
|
| cosine_mrr@10 | 0.0288 |
|
|
|
| cosine_map@100 | 0.0395 |
|
|
|
|
|
|
<!--
|
|
|
## Bias, Risks and Limitations
|
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
|
|
-->
|
|
|
|
|
|
<!--
|
|
|
### Recommendations
|
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
|
|
-->
|
|
|
|
|
|
## Training Details
|
|
|
|
|
|
### Training Dataset
|
|
|
|
|
|
#### json
|
|
|
|
|
|
* Dataset: json
|
|
|
* Size: 972 training samples
|
|
|
* Columns: <code>positive</code> and <code>anchor</code>
|
|
|
* Approximate statistics based on the first 972 samples:
|
|
|
| | positive | anchor |
|
|
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
|
|
|
| type | string | string |
|
|
|
| details | <ul><li>min: 4 tokens</li><li>mean: 11.73 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.29 tokens</li><li>max: 41 tokens</li></ul> |
|
|
|
* Samples:
|
|
|
| positive | anchor |
|
|
|
|:---------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
|
|
| <code>Giày Thể Thao Nữ Chunky Sneaker Hồng Pastel</code> | <code>đế cao 4.5cm giúp hack dáng tăng chiều cao vẫn thoải mái</code> |
|
|
|
| <code>Bộ Xếp Hình Gỗ 3D Động Vật Rừng</code> | <code>rèn luyện kỹ năng nhận dạng hình khối và phát triển khả năng quan sát</code> |
|
|
|
| <code>Rubik Mirror Cube Biến Hình 3x3</code> | <code>vỏ Rubik mạ gương vàng bóng nổi bật và sang trọng</code> |
|
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"loss": "MultipleNegativesRankingLoss",
|
|
|
"matryoshka_dims": [
|
|
|
768,
|
|
|
512,
|
|
|
256,
|
|
|
128,
|
|
|
64
|
|
|
],
|
|
|
"matryoshka_weights": [
|
|
|
1,
|
|
|
1,
|
|
|
1,
|
|
|
1,
|
|
|
1
|
|
|
],
|
|
|
"n_dims_per_step": -1
|
|
|
}
|
|
|
```
|
|
|
|
|
|
### Training Hyperparameters
|
|
|
#### Non-Default Hyperparameters
|
|
|
|
|
|
- `eval_strategy`: epoch
|
|
|
- `per_device_train_batch_size`: 32
|
|
|
- `gradient_accumulation_steps`: 16
|
|
|
- `learning_rate`: 2e-05
|
|
|
- `num_train_epochs`: 4
|
|
|
- `bf16`: True
|
|
|
- `load_best_model_at_end`: True
|
|
|
|
|
|
#### All Hyperparameters
|
|
|
<details><summary>Click to expand</summary>
|
|
|
|
|
|
- `overwrite_output_dir`: False
|
|
|
- `do_predict`: False
|
|
|
- `eval_strategy`: epoch
|
|
|
- `prediction_loss_only`: True
|
|
|
- `per_device_train_batch_size`: 32
|
|
|
- `per_device_eval_batch_size`: 8
|
|
|
- `per_gpu_train_batch_size`: None
|
|
|
- `per_gpu_eval_batch_size`: None
|
|
|
- `gradient_accumulation_steps`: 16
|
|
|
- `eval_accumulation_steps`: None
|
|
|
- `learning_rate`: 2e-05
|
|
|
- `weight_decay`: 0.0
|
|
|
- `adam_beta1`: 0.9
|
|
|
- `adam_beta2`: 0.999
|
|
|
- `adam_epsilon`: 1e-08
|
|
|
- `max_grad_norm`: 1.0
|
|
|
- `num_train_epochs`: 4
|
|
|
- `max_steps`: -1
|
|
|
- `lr_scheduler_type`: linear
|
|
|
- `lr_scheduler_kwargs`: {}
|
|
|
- `warmup_ratio`: 0.0
|
|
|
- `warmup_steps`: 0
|
|
|
- `log_level`: passive
|
|
|
- `log_level_replica`: warning
|
|
|
- `log_on_each_node`: True
|
|
|
- `logging_nan_inf_filter`: True
|
|
|
- `save_safetensors`: True
|
|
|
- `save_on_each_node`: False
|
|
|
- `save_only_model`: False
|
|
|
- `restore_callback_states_from_checkpoint`: False
|
|
|
- `no_cuda`: False
|
|
|
- `use_cpu`: False
|
|
|
- `use_mps_device`: False
|
|
|
- `seed`: 42
|
|
|
- `data_seed`: None
|
|
|
- `jit_mode_eval`: False
|
|
|
- `use_ipex`: False
|
|
|
- `bf16`: True
|
|
|
- `fp16`: False
|
|
|
- `fp16_opt_level`: O1
|
|
|
- `half_precision_backend`: auto
|
|
|
- `bf16_full_eval`: False
|
|
|
- `fp16_full_eval`: False
|
|
|
- `tf32`: None
|
|
|
- `local_rank`: 0
|
|
|
- `ddp_backend`: None
|
|
|
- `tpu_num_cores`: None
|
|
|
- `tpu_metrics_debug`: False
|
|
|
- `debug`: []
|
|
|
- `dataloader_drop_last`: False
|
|
|
- `dataloader_num_workers`: 0
|
|
|
- `dataloader_prefetch_factor`: None
|
|
|
- `past_index`: -1
|
|
|
- `disable_tqdm`: False
|
|
|
- `remove_unused_columns`: True
|
|
|
- `label_names`: None
|
|
|
- `load_best_model_at_end`: True
|
|
|
- `ignore_data_skip`: False
|
|
|
- `fsdp`: []
|
|
|
- `fsdp_min_num_params`: 0
|
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None
|
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
|
|
- `deepspeed`: None
|
|
|
- `label_smoothing_factor`: 0.0
|
|
|
- `optim`: adamw_torch
|
|
|
- `optim_args`: None
|
|
|
- `adafactor`: False
|
|
|
- `group_by_length`: False
|
|
|
- `length_column_name`: length
|
|
|
- `ddp_find_unused_parameters`: None
|
|
|
- `ddp_bucket_cap_mb`: None
|
|
|
- `ddp_broadcast_buffers`: False
|
|
|
- `dataloader_pin_memory`: True
|
|
|
- `dataloader_persistent_workers`: False
|
|
|
- `skip_memory_metrics`: True
|
|
|
- `use_legacy_prediction_loop`: False
|
|
|
- `push_to_hub`: False
|
|
|
- `resume_from_checkpoint`: None
|
|
|
- `hub_model_id`: None
|
|
|
- `hub_strategy`: every_save
|
|
|
- `hub_private_repo`: False
|
|
|
- `hub_always_push`: False
|
|
|
- `gradient_checkpointing`: False
|
|
|
- `gradient_checkpointing_kwargs`: None
|
|
|
- `include_inputs_for_metrics`: False
|
|
|
- `eval_do_concat_batches`: True
|
|
|
- `fp16_backend`: auto
|
|
|
- `push_to_hub_model_id`: None
|
|
|
- `push_to_hub_organization`: None
|
|
|
- `mp_parameters`:
|
|
|
- `auto_find_batch_size`: False
|
|
|
- `full_determinism`: False
|
|
|
- `torchdynamo`: None
|
|
|
- `ray_scope`: last
|
|
|
- `ddp_timeout`: 1800
|
|
|
- `torch_compile`: False
|
|
|
- `torch_compile_backend`: None
|
|
|
- `torch_compile_mode`: None
|
|
|
- `dispatch_batches`: None
|
|
|
- `split_batches`: None
|
|
|
- `include_tokens_per_second`: False
|
|
|
- `include_num_input_tokens_seen`: False
|
|
|
- `neftune_noise_alpha`: None
|
|
|
- `optim_target_modules`: None
|
|
|
- `batch_eval_metrics`: False
|
|
|
- `prompts`: None
|
|
|
- `batch_sampler`: batch_sampler
|
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### Training Logs
|
|
|
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|
|
|
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
|
|
|
| **0.5161** | **1** | **0.0934** | **0.0877** | **0.09** | **0.0803** | **0.0773** |
|
|
|
| 1.5484 | 3 | 0.0905 | 0.0836 | 0.0923 | 0.0782 | 0.0708 |
|
|
|
| 2.0645 | 4 | 0.0919 | 0.0854 | 0.0950 | 0.0777 | 0.0711 |
|
|
|
|
|
|
* The bold row denotes the saved checkpoint.
|
|
|
|
|
|
### Framework Versions
|
|
|
- Python: 3.11.9
|
|
|
- Sentence Transformers: 4.1.0
|
|
|
- Transformers: 4.41.2
|
|
|
- PyTorch: 2.6.0+cu124
|
|
|
- Accelerate: 1.7.0
|
|
|
- Datasets: 2.19.1
|
|
|
- Tokenizers: 0.19.1
|
|
|
|
|
|
## Citation
|
|
|
|
|
|
### BibTeX
|
|
|
|
|
|
#### Sentence Transformers
|
|
|
```bibtex
|
|
|
@inproceedings{reimers-2019-sentence-bert,
|
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
|
|
author = "Reimers, Nils and Gurevych, Iryna",
|
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
|
|
month = "11",
|
|
|
year = "2019",
|
|
|
publisher = "Association for Computational Linguistics",
|
|
|
url = "https://arxiv.org/abs/1908.10084",
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### MatryoshkaLoss
|
|
|
```bibtex
|
|
|
@misc{kusupati2024matryoshka,
|
|
|
title={Matryoshka Representation Learning},
|
|
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
|
|
|
year={2024},
|
|
|
eprint={2205.13147},
|
|
|
archivePrefix={arXiv},
|
|
|
primaryClass={cs.LG}
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### MultipleNegativesRankingLoss
|
|
|
```bibtex
|
|
|
@misc{henderson2017efficient,
|
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
|
|
year={2017},
|
|
|
eprint={1705.00652},
|
|
|
archivePrefix={arXiv},
|
|
|
primaryClass={cs.CL}
|
|
|
}
|
|
|
```
|
|
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