diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,5281 @@ +--- +library_name: sentence-transformers +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:181350 +- loss:MatryoshkaLoss +- loss:MultipleNegativesRankingLoss +base_model: bkai-foundation-models/vietnamese-bi-encoder +metrics: +- cosine_accuracy@1 +- cosine_accuracy@3 +- cosine_accuracy@5 +- cosine_accuracy@10 +- cosine_precision@1 +- cosine_precision@3 +- cosine_precision@5 +- cosine_precision@10 +- cosine_recall@1 +- cosine_recall@3 +- cosine_recall@5 +- cosine_recall@10 +- cosine_ndcg@10 +- cosine_mrr@10 +- cosine_map@100 +widget: +- source_sentence: quyết_định điều_chuyển tài_sản công có yêu_cầu_là quyết_định điều_chuyển + tài_sản hoặc văn_bản hồi_đáp . + sentences: + - cấp lại giấy chứng_nhận đăng_ký tàu cá do cấp nào thực_hiện ? + - thủ_tục cấp lại giấy_phép thành_lập văn_phòng đại_diện tại việt_nam của doanh_nghiệp + kinh_doanh dịch_vụ lữ_hành nước_ngoài trong trường_hợp giấy_phép thành_lập văn_phòng + đại_diện bị mất , bị hủy_hoại , bị hư_hỏng * hoặc bị tiêu_hủy có yêu_cầu thành_phần + hồ_sơ những gì ? + - quyết_định điều_chuyển tài_sản công có kết_quả_thực_hiện là gì ? +- source_sentence: 'chuyển trường đối_với học_sinh trung_học cơ_sở . có phí , lệ_phí + là : trực_tiếp : trực_tuyến : dịch_vụ bưu_chính :' + sentences: + - cấp giấy chứng_nhận là lương_y cho các đối_tượng quy_định tại khoản 6 , điều 1 + , thông_tư số 29 / 2015 / tt - byt có cách_thức nộp hồ_sơ như thế_nào ? + - chuyển trường đối_với học_sinh trung_học cơ_sở . có phí , lệ_phí là bao_nhiêu + ? + - chuyển_đổi công_ty trách_nhiệm hữu_hạn một thành_viên thành_công_ty trách_nhiệm + hữu_hạn hai thành_viên trở lên có cách_thức nộp hồ_sơ như thế_nào ? +- source_sentence: thủ_tục đăng_ký khai_tử do cơ_quan ủy_ban nhân_dân & nbsp ; xã + , & nbsp ; phường , & nbsp ; thị_trấn . thực_hiện + sentences: + - tiếp_nhận người có công vào cơ_sở nuôi_dưỡng , điều_dưỡng người có công do tỉnh + quản_lý_do cấp nào thực_hiện ? + - cấp giấy_phép hoạt_động đối_với cơ_sở khám bệnh , chữa bệnh khi thay_đổi người + chịu trách_nhiệm chuyên_môn của cơ_sở khám bệnh , chữa bệnh thuộc thẩm_quyền của + sở y_tế được thực_hiện mức_độ mấy ? + - thủ_tục đăng_ký khai_tử do cơ_quan nào giải_quyết ? +- source_sentence: thẩm_định nội_dung điều_chỉnh báo_cáo nghiên_cứu khả_thi , quyết_định + phê_duyệt điều_chỉnh dự_án ppp do nhà đầu_tư đề_xuất có_thể nộp hồ_sơ trực_tiếp + sentences: + - tiếp_tục hưởng trợ_cấp thất_nghiệp do cấp nào thực_hiện ? + - thủ_tục tặng giấy_khen của chủ_tịch ubnd cấp huyện về thành_tích thi_đua theo + đợt , chuyên_đề có kết_quả_thực_hiện là gì ? + - thẩm_định nội_dung điều_chỉnh báo_cáo nghiên_cứu khả_thi , quyết_định phê_duyệt + điều_chỉnh dự_án ppp do nhà đầu_tư đề_xuất có cách_thức nộp hồ_sơ như thế_nào + ? +- source_sentence: thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có yêu_cầu_là thẻ + hướng_dẫn_viên du_lịch nội_địa + sentences: + - đăng_ký thay_đổi nội_dung giấy chứng_nhận đăng_ký thành_lập đối_với cơ_sở trợ_giúp + xã_hội ngoài công_lập thuộc thẩm_quyền thành_lập của phòng lao_động – thương_binh + và xã_hội có cách_thức nộp hồ_sơ như thế_nào ? + - thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có kết_quả_thực_hiện là gì ? + - cấp giấy_phép hoạt_động khám , chữa bệnh_nhân_đạo đối_với bệnh_viện trên địa_bàn + quản_lý của sở y_tế ( trừ các bệnh_viện thuộc thẩm_quyền của bộ y_tế và bộ quốc_phòng + ) và áp_dụng đối_với trường_hợp khi thay_đổi hình_thức tổ_chức , chia tách , hợp_nhất + , sáp_nhập có phí , lệ_phí là bao_nhiêu ? +pipeline_tag: sentence-similarity +model-index: +- name: SentenceTransformer based on bkai-foundation-models/vietnamese-bi-encoder + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: dim 768 + type: dim_768 + metrics: + - type: cosine_accuracy@1 + value: 0.07468982630272952 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.2205955334987593 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.36535980148883374 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7285856079404467 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.07468982630272952 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.07353184449958643 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.07307196029776675 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07285856079404467 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07468982630272952 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.2205955334987593 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.36535980148883374 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.7285856079404467 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3322745331449966 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.2149912954429121 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.2250978733669177 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: dim 512 + type: dim_512 + metrics: + - type: cosine_accuracy@1 + value: 0.07310173697270471 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.21578163771712158 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.36660049627791563 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7284863523573201 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.07310173697270471 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.07192721257237386 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.07332009925558312 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.072848635235732 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07310173697270471 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.21578163771712158 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.36660049627791563 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.7284863523573201 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.33129337564687533 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.2137159990547113 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.22387747149230866 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: dim 256 + type: dim_256 + metrics: + - type: cosine_accuracy@1 + value: 0.07404466501240695 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.2159801488833747 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.3646650124069479 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7280893300248139 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.07404466501240695 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.07199338296112488 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.07293300248138959 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07280893300248138 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07404466501240695 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.2159801488833747 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.3646650124069479 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.7280893300248139 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3309867900949761 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.21350974831620226 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.22381299254974726 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: dim 128 + type: dim_128 + metrics: + - type: cosine_accuracy@1 + value: 0.07295285359801489 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.21826302729528535 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.3652605459057072 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7269975186104218 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.07295285359801489 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.07275434243176178 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.07305210918114144 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07269975186104219 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07295285359801489 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.21826302729528535 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.3652605459057072 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.7269975186104218 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3306176448598645 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.21326590255622857 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.22361829866448288 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: dim 64 + type: dim_64 + metrics: + - type: cosine_accuracy@1 + value: 0.07240694789081886 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.21602977667493797 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.36084367245657567 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7230272952853598 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.07240694789081886 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.07200992555831265 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.07216873449131514 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07230272952853598 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07240694789081886 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.21602977667493797 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.36084367245657567 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.7230272952853598 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3283541672853546 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.21160439560439992 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.22212575725630068 + name: Cosine Map@100 +--- + +# SentenceTransformer based on bkai-foundation-models/vietnamese-bi-encoder + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) 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:** [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) +- **Maximum Sequence Length:** 256 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity +- **Training Dataset:** + - json + + + +### 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("anhtuansh/vietnamese-bi-encoder-Financial-Matryoshka-2e-200k") +# Run inference +sentences = [ + 'thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có yêu_cầu_là thẻ hướng_dẫn_viên du_lịch nội_địa', + 'thủ_tục cấp thẻ hướng_dẫn_viên du_lịch nội_địa có kết_quả_thực_hiện là gì ?', + 'cấp giấy_phép hoạt_động khám , chữa bệnh_nhân_đạo đối_với bệnh_viện trên địa_bàn quản_lý của sở y_tế ( trừ các bệnh_viện thuộc thẩm_quyền của bộ y_tế và bộ quốc_phòng ) và áp_dụng đối_với trường_hợp khi thay_đổi hình_thức tổ_chức , chia tách , hợp_nhất , sáp_nhập có phí , lệ_phí là bao_nhiêu ?', +] +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] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Information Retrieval + +* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` +* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) + +| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | +|:--------------------|:-----------|:-----------|:----------|:-----------|:-----------| +| cosine_accuracy@1 | 0.0747 | 0.0731 | 0.074 | 0.073 | 0.0724 | +| cosine_accuracy@3 | 0.2206 | 0.2158 | 0.216 | 0.2183 | 0.216 | +| cosine_accuracy@5 | 0.3654 | 0.3666 | 0.3647 | 0.3653 | 0.3608 | +| cosine_accuracy@10 | 0.7286 | 0.7285 | 0.7281 | 0.727 | 0.723 | +| cosine_precision@1 | 0.0747 | 0.0731 | 0.074 | 0.073 | 0.0724 | +| cosine_precision@3 | 0.0735 | 0.0719 | 0.072 | 0.0728 | 0.072 | +| cosine_precision@5 | 0.0731 | 0.0733 | 0.0729 | 0.0731 | 0.0722 | +| cosine_precision@10 | 0.0729 | 0.0728 | 0.0728 | 0.0727 | 0.0723 | +| cosine_recall@1 | 0.0747 | 0.0731 | 0.074 | 0.073 | 0.0724 | +| cosine_recall@3 | 0.2206 | 0.2158 | 0.216 | 0.2183 | 0.216 | +| cosine_recall@5 | 0.3654 | 0.3666 | 0.3647 | 0.3653 | 0.3608 | +| cosine_recall@10 | 0.7286 | 0.7285 | 0.7281 | 0.727 | 0.723 | +| **cosine_ndcg@10** | **0.3323** | **0.3313** | **0.331** | **0.3306** | **0.3284** | +| cosine_mrr@10 | 0.215 | 0.2137 | 0.2135 | 0.2133 | 0.2116 | +| cosine_map@100 | 0.2251 | 0.2239 | 0.2238 | 0.2236 | 0.2221 | + + + + + +## Training Details + +### Training Dataset + +#### json + +* Dataset: json +* Size: 181,350 training samples +* Columns: positive and anchor +* Approximate statistics based on the first 1000 samples: + | | positive | anchor | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | positive | anchor | + |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | tính tiền cấp quyền khai_thác tài_nguyên nước đối_với công_trình chưa vận_hành cấp tỉnh có yêu_cầu_là . | tính tiền cấp quyền khai_thác tài_nguyên nước đối_với công_trình chưa vận_hành cấp tỉnh có yêu_cầu gì ? | + | xóa_đăng_ký hành_nghề và thu_hồi thẻ công_chứng_viên trường_hợp công_chứng_viên không còn hành_nghề tại tổ_chức hành_nghề công_chứng được thực_hiện mức_độ trực_tuyến toàn_trình | xóa_đăng_ký hành_nghề và thu_hồi thẻ công_chứng_viên trường_hợp công_chứng_viên không còn hành_nghề tại tổ_chức hành_nghề công_chứng được thực_hiện mức_độ mấy ? | + | thủ_tục cấp giấy chứng_nhận đủ điều_kiện kinh_doanh hoạt_động thể_thao đối_với môn mô_tô nước trên biển có trình_tự thực_hiện như sau : doanh_nghiệp gửi hồ_sơ đến cơ_quan chuyên_môn về thể_dục , thể_thao thuộc ủy_ban nhân nhân cấp tỉnh nơi đăng_ký địa_điểm kinh_doanh hoạt_động thể_thao hoặc nơi doanh_nghiệp có trụ_sở chính trong trường_hợp_doanh_nghiệp có nhiều địa_điểm kinh_doanh hoạt_động thể_thao . cơ_quan chuyên_môn về thể_dục , thể_thao thuộc ủy_ban nhân_dân cấp tỉnh ( sau đây gọi là cơ_quan cấp giấy chứng_nhận đủ điều_kiện ) cấp cho doanh_nghiệp giấy tiếp_nhận hồ_sơ . trường_hợp hồ_sơ cần sửa_đổi , bổ_sung , cơ_quan cấp giấy chứng_nhận đủ điều_kiện thông_báo trực_tiếp hoặc bằng văn_bản những nội_dung cần sửa_đổi , bổ_sung đến doanh_nghiệp trong thời_hạn 03 ngày làm_việc , kể từ ngày nhận hồ_sơ . | thủ_tục cấp giấy chứng_nhận đủ điều_kiện kinh_doanh hoạt_động thể_thao đối_với môn mô_tô nước trên biển có trình_tự thực_hiện như thế_nào ? | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 768, + 512 + ], + "matryoshka_weights": [ + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +### Evaluation Dataset + +#### json + +* Dataset: json +* Size: 20,150 evaluation samples +* Columns: positive and anchor +* Approximate statistics based on the first 1000 samples: + | | positive | anchor | + |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | positive | anchor | + |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004 có yêu_cầu hồ_sơ gồm : 1 . hồ_sơ do người sử_dụng đất nộp 01 bộ tại sở tài_nguyên và môi_trường gồm : văn_bản đề_nghị điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ ban_hành trước ngày 01 tháng 7 năm 2004 theo mẫu_số 03 ban_hành kèm theo thông_tư 33 / 2017 / tt - btnmtbản sao quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004bản sao giấy chứng_nhận quyền sử_dụng đất hoặc giấy chứng_nhận quyền sở_hữu nhà ở và quyền sử_dụng đất ở hoặc giấy chứng_nhận quyền sử_dụng đất , quyền sở_hữu nhà ở và tài_sản khác gắn liền với đất đã cấp ( nếu có ) bản_sao giấy_phép đầu_tư hoặc giấy chứng_nhận đầu_tư hoặc giấy đăng_ký kinh_doanh hoặc văn_bản chấp_thuận chủ_t... | điều_chỉnh quyết_định thu_hồi đất , giao đất , cho thuê đất , cho phép chuyển mục_đích sử_dụng đất của thủ_tướng chính_phủ đã ban_hành trước ngày 01 tháng 7 năm 2004 có yêu_cầu thành_phần hồ_sơ những gì ? | + | thủ_tục thông_báo hủy kết_quả phong phẩm hoặc suy cử chức_sắc đối_với các trường_hợp quy_định tại khoản 2 điều 33 của luật tín_ngưỡng , tôn_giáo được thực_hiện mức_độ trực_tuyến toàn_trình | thủ_tục thông_báo hủy kết_quả phong phẩm hoặc suy cử chức_sắc đối_với các trường_hợp quy_định tại khoản 2 điều 33 của luật tín_ngưỡng , tôn_giáo được thực_hiện mức_độ mấy ? | + | thủ_tục đính_chính giấy chứng_nhận đã cấp có phí , lệ_phí là : trực_tiếp : | thủ_tục đính_chính giấy chứng_nhận đã cấp có phí , lệ_phí là bao_nhiêu ? | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 768, + 512 + ], + "matryoshka_weights": [ + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: epoch +- `per_device_train_batch_size`: 2 +- `per_device_eval_batch_size`: 2 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 2e-05 +- `num_train_epochs`: 1 +- `lr_scheduler_type`: cosine +- `warmup_ratio`: 0.1 +- `fp16`: True +- `tf32`: False +- `load_best_model_at_end`: True +- `optim`: adamw_torch_fused +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: epoch +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 2 +- `per_device_eval_batch_size`: 2 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `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`: 1 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.1 +- `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`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: False +- `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_fused +- `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`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | Validation Loss | 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 | 0 | - | - | 0.2067 | 0.2044 | 0.1865 | 0.1756 | 0.1644 | +| 0.0002 | 10 | 0.002 | - | - | - | - | - | - | +| 0.0004 | 20 | 0.0 | - | - | - | - | - | - | +| 0.0007 | 30 | 0.0001 | - | - | - | - | - | - | +| 0.0009 | 40 | 0.0003 | - | - | - | - | - | - | +| 0.0011 | 50 | 0.0004 | - | - | - | - | - | - | +| 0.0013 | 60 | 0.0001 | - | - | - | - | - | - | +| 0.0015 | 70 | 0.0003 | - | - | - | - | - | - | +| 0.0018 | 80 | 0.0001 | - | - | - | - | - | - | +| 0.0020 | 90 | 0.0001 | - | - | - | - | - | - | +| 0.0022 | 100 | 0.0001 | - | - | - | - | - | - | +| 0.0024 | 110 | 0.0005 | - | - | - | - | - | - | +| 0.0026 | 120 | 0.0001 | - | - | - | - | - | - | +| 0.0029 | 130 | 0.0001 | - | - | - | - | - | - | +| 0.0031 | 140 | 0.0002 | - | - | - | - | - | - | +| 0.0033 | 150 | 0.0 | - | - | - | - | - | - | +| 0.0035 | 160 | 0.0001 | - | - | - | - | - | - | +| 0.0037 | 170 | 0.0001 | - | - | - | - | - | - | +| 0.0040 | 180 | 0.0001 | - | - | - | - | - | - | +| 0.0042 | 190 | 0.0001 | - | - | - | - | - | - | +| 0.0044 | 200 | 0.0145 | - | - | - | - | - | - | +| 0.0046 | 210 | 0.0001 | - | - | - | - | - | - | +| 0.0049 | 220 | 0.0002 | - | - | - | - | - | - | +| 0.0051 | 230 | 0.0001 | - | - | - | - | - | - | +| 0.0053 | 240 | 0.0 | - | - | - | - | - | - | +| 0.0055 | 250 | 0.0001 | - | - | - | - | - | - | +| 0.0057 | 260 | 0.0 | - | - | - | - | - | - | +| 0.0060 | 270 | 0.0 | - | - | - | - | - | - | +| 0.0062 | 280 | 0.0 | - | - | - | - | - | - | +| 0.0064 | 290 | 0.0 | - | - | - | - | - | - | +| 0.0066 | 300 | 0.0 | - | - | - | - | - | - | +| 0.0068 | 310 | 0.0003 | - | - | - | - | - | - | +| 0.0071 | 320 | 0.0001 | - | - | - | - | - | - | +| 0.0073 | 330 | 0.0 | - | - | - | - | - | - | +| 0.0075 | 340 | 0.0001 | - | - | - | - | - | - | +| 0.0077 | 350 | 0.0044 | - | - | - | - | - | - | +| 0.0079 | 360 | 0.0001 | - | - | - | - | - | - | +| 0.0082 | 370 | 0.0243 | - | - | - | - | - | - | +| 0.0084 | 380 | 0.0014 | - | - | - | - | - | - | +| 0.0086 | 390 | 0.0 | - | - | - | - | - | - | +| 0.0088 | 400 | 0.0001 | - | - | - | - | - | - | +| 0.0090 | 410 | 0.0 | - | - | - | - | - | - | +| 0.0093 | 420 | 0.0 | - | - | - | - | - | - | +| 0.0095 | 430 | 0.0011 | - | - | - | - | - | - | +| 0.0097 | 440 | 0.0 | - | - | - | - | - | - | +| 0.0099 | 450 | 0.0 | - | - | - | - | - | - | +| 0.0101 | 460 | 0.0 | - | - | - | - | - | - | +| 0.0104 | 470 | 0.0 | - | - | - | - | - | - | +| 0.0106 | 480 | 0.018 | - | - | - | - | - | - | +| 0.0108 | 490 | 0.0001 | - | - | - | - | - | - | +| 0.0110 | 500 | 0.0 | - | - | - | - | - | - | +| 0.0112 | 510 | 0.016 | - | - | - | - | - | - | +| 0.0115 | 520 | 0.0 | - | - | - | - | - | - | +| 0.0117 | 530 | 0.0005 | - | - | - | - | - | - | +| 0.0119 | 540 | 0.0 | - | - | - | - | - | - | +| 0.0121 | 550 | 0.0 | - | - | - | - | - | - | +| 0.0124 | 560 | 0.0 | - | - | - | - | - | - | +| 0.0126 | 570 | 0.0 | - | - | - | - | - | - | +| 0.0128 | 580 | 0.0 | - | - | - | - | - | - | +| 0.0130 | 590 | 0.0 | - | - | - | - | - | - | +| 0.0132 | 600 | 0.0 | - | - | - | - | - | - | +| 0.0135 | 610 | 0.0 | - | - | - | - | - | - | +| 0.0137 | 620 | 0.0 | - | - | - | - | - | - | +| 0.0139 | 630 | 0.0 | - | - | - | - | - | - | +| 0.0141 | 640 | 0.0 | - | - | - | - | - | - | +| 0.0143 | 650 | 0.0 | - | - | - | - | - | - | +| 0.0146 | 660 | 0.0 | - | - | - | - | - | - | +| 0.0148 | 670 | 0.0 | - | - | - | - | - | - | +| 0.0150 | 680 | 0.0 | - | - | - | - | - | - | +| 0.0152 | 690 | 0.0 | - | - | - | - | - | - | +| 0.0154 | 700 | 0.0 | - | - | - | - | - | - | +| 0.0157 | 710 | 0.0 | - | - | - | - | - | - | +| 0.0159 | 720 | 0.0002 | - | - | - | - | - | - | +| 0.0161 | 730 | 0.0 | - | - | - | - | - | - | +| 0.0163 | 740 | 0.0 | - | - | - | - | - | - | +| 0.0165 | 750 | 0.0 | - | - | - | - | - | - | +| 0.0168 | 760 | 0.0 | - | - | - | - | - | - | +| 0.0170 | 770 | 0.0001 | - | - | - | - | - | - | +| 0.0172 | 780 | 0.0 | - | - | - | - | - | - | +| 0.0174 | 790 | 0.0 | - | - | - | - | - | - | +| 0.0176 | 800 | 0.0001 | - | - | - | - | - | - | +| 0.0179 | 810 | 0.0 | - | - | - | - | - | - | +| 0.0181 | 820 | 0.0 | - | - | - | - | - | - | +| 0.0183 | 830 | 0.0068 | - | - | - | - | - | - | +| 0.0185 | 840 | 0.0 | - | - | - | - | - | - | +| 0.0187 | 850 | 0.0 | - | - | - | - | - | - | +| 0.0190 | 860 | 0.0 | - | - | - | - | - | - | +| 0.0192 | 870 | 0.0 | - | - | - | - | - | - | +| 0.0194 | 880 | 0.0 | - | - | - | - | - | - | +| 0.0196 | 890 | 0.0 | - | - | - | - | - | - | +| 0.0199 | 900 | 0.0 | - | - | - | - | - | - | +| 0.0201 | 910 | 0.0001 | - | - | - | - | - | - | +| 0.0203 | 920 | 0.0 | - | - | - | - | - | - | +| 0.0205 | 930 | 0.0 | - | - | - | - | - | - | +| 0.0207 | 940 | 0.0 | - | - | - | - | - | - | +| 0.0210 | 950 | 0.0 | - | - | - | - | - | - | +| 0.0212 | 960 | 0.0005 | - | - | - | - | - | - | +| 0.0214 | 970 | 0.0 | - | - | - | - | - | - | +| 0.0216 | 980 | 0.0005 | - | - | - | - | - | - | +| 0.0218 | 990 | 0.0 | - | - | - | - | - | - | +| 0.0221 | 1000 | 0.0 | - | - | - | - | - | - | +| 0.0223 | 1010 | 0.0 | - | - | - | - | - | - | +| 0.0225 | 1020 | 0.0001 | - | - | - | - | - | - | +| 0.0227 | 1030 | 0.0 | - | - | - | - | - | - | +| 0.0229 | 1040 | 0.0 | - | - | - | - | - | - | +| 0.0232 | 1050 | 0.0 | - | - | - | - | - | - | +| 0.0234 | 1060 | 0.0 | - | - | - | - | - | - | +| 0.0236 | 1070 | 0.0 | - | - | - | - | - | - | +| 0.0238 | 1080 | 0.0069 | - | - | - | - | - | - | +| 0.0240 | 1090 | 0.0 | - | - | - | - | - | - | +| 0.0243 | 1100 | 0.0 | - | - | - | - | - | - | +| 0.0245 | 1110 | 0.0055 | - | - | - | - | - | - | +| 0.0247 | 1120 | 0.0 | - | - | - | - | - | - | +| 0.0249 | 1130 | 0.0015 | - | - | - | - | - | - | +| 0.0251 | 1140 | 0.0 | - | - | - | - | - | - | +| 0.0254 | 1150 | 0.0 | - | - | - | - | - | - | +| 0.0256 | 1160 | 0.0 | - | - | - | - | - | - | +| 0.0258 | 1170 | 0.0 | - | - | - | - | - | - | +| 0.0260 | 1180 | 0.0 | - | - | - | - | - | - | +| 0.0262 | 1190 | 0.0001 | - | - | - | - | - | - | +| 0.0265 | 1200 | 0.0 | - | - | - | - | - | - | +| 0.0267 | 1210 | 0.0 | - | - | - | - | - | - | +| 0.0269 | 1220 | 0.0028 | - | - | - | - | - | - | +| 0.0271 | 1230 | 0.0 | - | - | - | - | - | - | +| 0.0274 | 1240 | 0.0 | - | - | - | - | - | - | +| 0.0276 | 1250 | 0.0 | - | - | - | - | - | - | +| 0.0278 | 1260 | 0.0 | - | - | - | - | - | - | +| 0.0280 | 1270 | 0.0 | - | - | - | - | - | - | +| 0.0282 | 1280 | 0.0001 | - | - | - | - | - | - | +| 0.0285 | 1290 | 0.0 | - | - | - | - | - | - | +| 0.0287 | 1300 | 0.0 | - | - | - | - | - | - | +| 0.0289 | 1310 | 0.0003 | - | - | - | - | - | - | +| 0.0291 | 1320 | 0.0005 | - | - | - | - | - | - | +| 0.0293 | 1330 | 0.0 | - | - | - | - | - | - | +| 0.0296 | 1340 | 0.0003 | - | - | - | - | - | - | +| 0.0298 | 1350 | 0.0 | - | - | - | - | - | - | +| 0.0300 | 1360 | 0.0 | - | - | - | - | - | - | +| 0.0302 | 1370 | 0.0001 | - | - | - | - | - | - | +| 0.0304 | 1380 | 0.0 | - | - | - | - | - | - | +| 0.0307 | 1390 | 0.0001 | - | - | - | - | - | - | +| 0.0309 | 1400 | 0.0 | - | - | - | - | - | - | +| 0.0311 | 1410 | 0.0 | - | - | - | - | - | - | +| 0.0313 | 1420 | 0.0001 | - | - | - | - | - | - | +| 0.0315 | 1430 | 0.0 | - | - | - | - | - | - | +| 0.0318 | 1440 | 0.0 | - | - | - | - | - | - | +| 0.0320 | 1450 | 0.0 | - | - | - | - | - | - | +| 0.0322 | 1460 | 0.0004 | - | - | - | - | - | - | +| 0.0324 | 1470 | 0.0037 | - | - | - | - | - | - | +| 0.0326 | 1480 | 0.0 | - | - | - | - | - | - | +| 0.0329 | 1490 | 0.0 | - | - | - | - | - | - | +| 0.0331 | 1500 | 0.0008 | - | - | - | - | - | - | +| 0.0333 | 1510 | 0.0 | - | - | - | - | - | - | +| 0.0335 | 1520 | 0.0 | - | - | - | - | - | - | +| 0.0337 | 1530 | 0.0 | - | - | - | - | - | - | +| 0.0340 | 1540 | 0.0 | - | - | - | - | - | - | +| 0.0342 | 1550 | 0.0 | - | - | - | - | - | - | +| 0.0344 | 1560 | 0.0001 | - | - | - | - | - | - | +| 0.0346 | 1570 | 0.0 | - | - | - | - | - | - | +| 0.0348 | 1580 | 0.0 | - | - | - | - | - | - | +| 0.0351 | 1590 | 0.0 | - | - | - | - | - | - | +| 0.0353 | 1600 | 0.0 | - | - | - | - | - | - | +| 0.0355 | 1610 | 0.0 | - | - | - | - | - | - | +| 0.0357 | 1620 | 0.0023 | - | - | - | - | - | - | +| 0.0360 | 1630 | 0.0 | - | - | - | - | - | - | +| 0.0362 | 1640 | 0.0005 | - | - | - | - | - | - | +| 0.0364 | 1650 | 0.0 | - | - | - | - | - | - | +| 0.0366 | 1660 | 0.0 | - | - | - | - | - | - | +| 0.0368 | 1670 | 0.0 | - | - | - | - | - | - | +| 0.0371 | 1680 | 0.0023 | - | - | - | - | - | - | +| 0.0373 | 1690 | 0.0041 | - | - | - | - | - | - | +| 0.0375 | 1700 | 0.0003 | - | - | - | - | - | - | +| 0.0377 | 1710 | 0.0 | - | - | - | - | - | - | +| 0.0379 | 1720 | 0.0003 | - | - | - | - | - | - | +| 0.0382 | 1730 | 0.0001 | - | - | - | - | - | - | +| 0.0384 | 1740 | 0.0 | - | - | - | - | - | - | +| 0.0386 | 1750 | 0.0003 | - | - | - | - | - | - | +| 0.0388 | 1760 | 0.0002 | - | - | - | - | - | - | +| 0.0390 | 1770 | 0.0 | - | - | - | - | - | - | +| 0.0393 | 1780 | 0.0 | - | - | - | - | - | - | +| 0.0395 | 1790 | 0.0 | - | - | - | - | - | - | +| 0.0397 | 1800 | 0.0 | - | - | - | - | - | - | +| 0.0399 | 1810 | 0.0 | - | - | - | - | - | - | +| 0.0401 | 1820 | 0.0 | - | - | - | - | - | - | +| 0.0404 | 1830 | 0.0 | - | - | - | - | - | - | +| 0.0406 | 1840 | 0.02 | - | - | - | - | - | - | +| 0.0408 | 1850 | 0.0 | - | - | - | - | - | - | +| 0.0410 | 1860 | 0.0 | - | - | - | - | - | - | +| 0.0412 | 1870 | 0.0 | - | - | - | - | - | - | +| 0.0415 | 1880 | 0.0 | - | - | - | - | - | - | +| 0.0417 | 1890 | 0.0 | - | - | - | - | - | - | +| 0.0419 | 1900 | 0.0 | - | - | - | - | - | - | +| 0.0421 | 1910 | 0.0 | - | - | - | - | - | - | +| 0.0423 | 1920 | 0.0029 | - | - | - | - | - | - | +| 0.0426 | 1930 | 0.0001 | - | - | - | - | - | - | +| 0.0428 | 1940 | 0.0 | - | - | - | - | - | - | +| 0.0430 | 1950 | 0.0 | - | - | - | - | - | - | +| 0.0432 | 1960 | 0.0 | - | - | - | - | - | - | +| 0.0435 | 1970 | 0.0 | - | - | - | - | - | - | +| 0.0437 | 1980 | 0.0 | - | - | - | - | - | - | +| 0.0439 | 1990 | 0.0 | - | - | - | - | - | - | +| 0.0441 | 2000 | 0.0 | - | - | - | - | - | - | +| 0.0443 | 2010 | 0.0 | - | - | - | - | - | - | +| 0.0446 | 2020 | 0.0 | - | - | - | - | - | - | +| 0.0448 | 2030 | 0.0 | - | - | - | - | - | - | +| 0.0450 | 2040 | 0.0 | - | - | - | - | - | - | +| 0.0452 | 2050 | 0.0 | - | - | - | - | - | - | +| 0.0454 | 2060 | 0.0 | - | - | - | - | - | - | +| 0.0457 | 2070 | 0.0 | - | - | - | - | - | - | +| 0.0459 | 2080 | 0.0 | - | - | - | - | - | - | +| 0.0461 | 2090 | 0.0 | - | - | - | - | - | - | +| 0.0463 | 2100 | 0.0 | - | - | - | - | - | - | +| 0.0465 | 2110 | 0.0 | - | - | - | - | - | - | +| 0.0468 | 2120 | 0.0 | - | - | - | - | - | - | +| 0.0470 | 2130 | 0.0 | - | - | - | - | - | - | +| 0.0472 | 2140 | 0.0 | - | - | - | - | - | - | +| 0.0474 | 2150 | 0.0 | - | - | - | - | - | - | +| 0.0476 | 2160 | 0.0 | - | - | - | - | - | - | +| 0.0479 | 2170 | 0.0 | - | - | - | - | - | - | +| 0.0481 | 2180 | 0.0 | - | - | - | - | - | - | +| 0.0483 | 2190 | 0.0 | - | - | - | - | - | - | +| 0.0485 | 2200 | 0.0 | - | - | - | - | - | - | +| 0.0487 | 2210 | 0.0 | - | - | - | - | - | - | +| 0.0490 | 2220 | 0.0 | - | - | - | - | - | - | +| 0.0492 | 2230 | 0.005 | - | - | - | - | - | - | +| 0.0494 | 2240 | 0.0 | - | - | - | - | - | - | +| 0.0496 | 2250 | 0.0 | - | - | - | - | - | - | +| 0.0498 | 2260 | 0.0003 | - | - | - | - | - | - | +| 0.0501 | 2270 | 0.0 | - | - | - | - | - | - | +| 0.0503 | 2280 | 0.0001 | - | - | - | - | - | - | +| 0.0505 | 2290 | 0.0 | - | - | - | - | - | - | +| 0.0507 | 2300 | 0.0001 | - | - | - | - | - | - | +| 0.0510 | 2310 | 0.0 | - | - | - | - | - | - | +| 0.0512 | 2320 | 0.0 | - | - | - | - | - | - | +| 0.0514 | 2330 | 0.0 | - | - | - | - | - | - | +| 0.0516 | 2340 | 0.0 | - | - | - | - | - | - | +| 0.0518 | 2350 | 0.0 | - | - | - | - 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| - | - | - | - | - | +| 0.0567 | 2570 | 0.0 | - | - | - | - | - | - | +| 0.0569 | 2580 | 0.0 | - | - | - | - | - | - | +| 0.0571 | 2590 | 0.0 | - | - | - | - | - | - | +| 0.0573 | 2600 | 0.0134 | - | - | - | - | - | - | +| 0.0576 | 2610 | 0.0 | - | - | - | - | - | - | +| 0.0578 | 2620 | 0.008 | - | - | - | - | - | - | +| 0.0580 | 2630 | 0.0 | - | - | - | - | - | - | +| 0.0582 | 2640 | 0.0001 | - | - | - | - | - | - | +| 0.0585 | 2650 | 0.0 | - | - | - | - | - | - | +| 0.0587 | 2660 | 0.0 | - | - | - | - | - | - | +| 0.0589 | 2670 | 0.0 | - | - | - | - | - | - | +| 0.0591 | 2680 | 0.0001 | - | - | - | - | - | - | +| 0.0593 | 2690 | 0.0 | - | - | - | - | - | - | +| 0.0596 | 2700 | 0.0 | - | - | - | - | - | - | +| 0.0598 | 2710 | 0.0 | - | - | - | - | - | - | +| 0.0600 | 2720 | 0.0 | - | - | - | - | - | - | +| 0.0602 | 2730 | 0.0 | - | - | - | - | - | - | +| 0.0604 | 2740 | 0.0 | - | - | - | - | - | - | +| 0.0607 | 2750 | 0.0 | - | - | - | - | - | - | +| 0.0609 | 2760 | 0.0003 | - | - | - | - | - | - | +| 0.0611 | 2770 | 0.0 | - | - | - | - | - | - | +| 0.0613 | 2780 | 0.0 | - | - | - | - | - | - | +| 0.0615 | 2790 | 0.0 | - | - | - | - | - | - | +| 0.0618 | 2800 | 0.0 | - | - | - | - | - | - | +| 0.0620 | 2810 | 0.0 | - | - | - | - | - | - | +| 0.0622 | 2820 | 0.0001 | - | - | - | - | - | - | +| 0.0624 | 2830 | 0.0 | - | - | - | - | - | - | +| 0.0626 | 2840 | 0.0001 | - | - | - | - | - | - | +| 0.0629 | 2850 | 0.0 | - | - | - | - | - | - | +| 0.0631 | 2860 | 0.0 | - | - | - | - | - | - | +| 0.0633 | 2870 | 0.0 | - | - | - | - | - | - | +| 0.0635 | 2880 | 0.0 | - | - | - | - | - | - | +| 0.0637 | 2890 | 0.0 | - | - | - | - | - | - | +| 0.0640 | 2900 | 0.0 | - | - | - | - | - | - | +| 0.0642 | 2910 | 0.0 | - | - | - | - | - | - | +| 0.0644 | 2920 | 0.0 | - | - | - | - | - | - | +| 0.0646 | 2930 | 0.0 | - | - | - | - | - | - | +| 0.0648 | 2940 | 0.0 | - | - | - | - | - | - | +| 0.0651 | 2950 | 0.0 | - | - | - | - | - | - | +| 0.0653 | 2960 | 0.0 | - | - | - | - | - | - | +| 0.0655 | 2970 | 0.0 | - | - | - | - | - | - | +| 0.0657 | 2980 | 0.0 | - | - | - | - | - | - | +| 0.0659 | 2990 | 0.0 | - | - | - | - | - | - | +| 0.0662 | 3000 | 0.0 | - | - | - | - | - | - | +| 0.0664 | 3010 | 0.0 | - | - | - | - | - | - | +| 0.0666 | 3020 | 0.0 | - | - | - | - | - | - | +| 0.0668 | 3030 | 0.0 | - | - | - | - | - | - | +| 0.0671 | 3040 | 0.0 | - | - | - | - | - | - | +| 0.0673 | 3050 | 0.0 | - | - | - | - | - | - | +| 0.0675 | 3060 | 0.0 | - | - | - | - | - | - | +| 0.0677 | 3070 | 0.0 | - | - | - | - | - | - | +| 0.0679 | 3080 | 0.0 | - | - | - | - | - | - | +| 0.0682 | 3090 | 0.0 | - | - | - | - | - | - | +| 0.0684 | 3100 | 0.0 | - | - | - | - | - | - | +| 0.0686 | 3110 | 0.0 | - | - | - | - | - | - | +| 0.0688 | 3120 | 0.0001 | - | - | - | - | - | - | +| 0.0690 | 3130 | 0.0 | - | - | - | - | - | - | +| 0.0693 | 3140 | 0.0 | - | - | - | - | - | - | +| 0.0695 | 3150 | 0.0 | - | - | - | - | - | - | +| 0.0697 | 3160 | 0.0 | - | - | - | - | - | - | +| 0.0699 | 3170 | 0.0 | - | - | - | - | - | - 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| - | - | - | - | - | +| 0.0748 | 3390 | 0.0 | - | - | - | - | - | - | +| 0.0750 | 3400 | 0.0002 | - | - | - | - | - | - | +| 0.0752 | 3410 | 0.0 | - | - | - | - | - | - | +| 0.0754 | 3420 | 0.0 | - | - | - | - | - | - | +| 0.0757 | 3430 | 0.0 | - | - | - | - | - | - | +| 0.0759 | 3440 | 0.0 | - | - | - | - | - | - | +| 0.0761 | 3450 | 0.0 | - | - | - | - | - | - | +| 0.0763 | 3460 | 0.0 | - | - | - | - | - | - | +| 0.0765 | 3470 | 0.0 | - | - | - | - | - | - | +| 0.0768 | 3480 | 0.0 | - | - | - | - | - | - | +| 0.0770 | 3490 | 0.0 | - | - | - | - | - | - | +| 0.0772 | 3500 | 0.0 | - | - | - | - | - | - | +| 0.0774 | 3510 | 0.0 | - | - | - | - | - | - | +| 0.0776 | 3520 | 0.0 | - | - | - | - | - | - | +| 0.0779 | 3530 | 0.0001 | - | - | - | - | - | - | +| 0.0781 | 3540 | 0.0 | - | - | - | - | - | - | +| 0.0783 | 3550 | 0.0 | - | - | - | - | - | - | +| 0.0785 | 3560 | 0.0 | - | - | - | - | - | - | +| 0.0787 | 3570 | 0.0 | - | - | - | - | - | - | +| 0.0790 | 3580 | 0.0 | - | - | - | - | - | - | +| 0.0792 | 3590 | 0.0005 | - | - | - | - | - | - | +| 0.0794 | 3600 | 0.0 | - | - | - | - | - | - | +| 0.0796 | 3610 | 0.0 | - | - | - | - | - | - | +| 0.0798 | 3620 | 0.0001 | - | - | - | - | - | - | +| 0.0801 | 3630 | 0.0001 | - | - | - | - | - | - | +| 0.0803 | 3640 | 0.0 | - | - | - | - | - | - | +| 0.0805 | 3650 | 0.0 | - | - | - | - | - | - | +| 0.0807 | 3660 | 0.0066 | - | - | - | - | - | - | +| 0.0809 | 3670 | 0.0 | - | - | - | - | - | - | +| 0.0812 | 3680 | 0.0 | - | - | - | - | - | - | +| 0.0814 | 3690 | 0.0001 | - | - | - | - | - | - | +| 0.0816 | 3700 | 0.0002 | - | - | - | - | - | - | +| 0.0818 | 3710 | 0.0 | - | - | - | - | - | - | +| 0.0821 | 3720 | 0.0 | - | - | - | - | - | - | +| 0.0823 | 3730 | 0.0 | - | - | - | - | - | - | +| 0.0825 | 3740 | 0.0 | - | - | - | - | - | - | +| 0.0827 | 3750 | 0.0 | - | - | - | - | - | - | +| 0.0829 | 3760 | 0.0 | - | - | - | - | - | - | +| 0.0832 | 3770 | 0.0 | - | - | - | - | - | - | +| 0.0834 | 3780 | 0.0 | - | - | - | - | - | - 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| - | - | - | - | - | +| 0.0882 | 4000 | 0.0001 | - | - | - | - | - | - | +| 0.0884 | 4010 | 0.0001 | - | - | - | - | - | - | +| 0.0887 | 4020 | 0.0 | - | - | - | - | - | - | +| 0.0889 | 4030 | 0.0 | - | - | - | - | - | - | +| 0.0891 | 4040 | 0.0 | - | - | - | - | - | - | +| 0.0893 | 4050 | 0.0 | - | - | - | - | - | - | +| 0.0896 | 4060 | 0.0001 | - | - | - | - | - | - | +| 0.0898 | 4070 | 0.0 | - | - | - | - | - | - | +| 0.0900 | 4080 | 0.0 | - | - | - | - | - | - | +| 0.0902 | 4090 | 0.0 | - | - | - | - | - | - | +| 0.0904 | 4100 | 0.0 | - | - | - | - | - | - | +| 0.0907 | 4110 | 0.0 | - | - | - | - | - | - | +| 0.0909 | 4120 | 0.0 | - | - | - | - | - | - | +| 0.0911 | 4130 | 0.0 | - | - | - | - | - | - | +| 0.0913 | 4140 | 0.0001 | - | - | - | - | - | - | +| 0.0915 | 4150 | 0.0 | - | - | - | - | - | - | +| 0.0918 | 4160 | 0.0 | - | - | - | - | - | - | +| 0.0920 | 4170 | 0.0 | - | - | - | - | - | - | +| 0.0922 | 4180 | 0.0 | - | - | - | - | - | - | +| 0.0924 | 4190 | 0.0 | - | - | - 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| - | - | - | - | - | +| 0.9641 | 43710 | 0.0 | - | - | - | - | - | - | +| 0.9643 | 43720 | 0.0 | - | - | - | - | - | - | +| 0.9645 | 43730 | 0.0 | - | - | - | - | - | - | +| 0.9648 | 43740 | 0.0 | - | - | - | - | - | - | +| 0.9650 | 43750 | 0.0 | - | - | - | - | - | - | +| 0.9652 | 43760 | 0.0655 | - | - | - | - | - | - | +| 0.9654 | 43770 | 0.0 | - | - | - | - | - | - | +| 0.9656 | 43780 | 0.0 | - | - | - | - | - | - | +| 0.9659 | 43790 | 0.0 | - | - | - | - | - | - | +| 0.9661 | 43800 | 0.0 | - | - | - | - | - | - | +| 0.9663 | 43810 | 0.0 | - | - | - | - | - | - | +| 0.9665 | 43820 | 0.0 | - | - | - | - | - | - | +| 0.9667 | 43830 | 0.0 | - | - | - | - | - | - | +| 0.9670 | 43840 | 0.0 | - | - | - | - | - | - | +| 0.9672 | 43850 | 0.0 | - | - | - | - | - | - | +| 0.9674 | 43860 | 0.0 | - | - | - | - | - | - | +| 0.9676 | 43870 | 0.0 | - | - | - | - | - | - | +| 0.9679 | 43880 | 0.0 | - | - | - | - | - | - | +| 0.9681 | 43890 | 0.0 | - | - | - | - | - | - | +| 0.9683 | 43900 | 0.0 | - | - | - | - | - | - | +| 0.9685 | 43910 | 0.0 | - | - | - | - | - | - | +| 0.9687 | 43920 | 0.0 | - | - | - | - | - | - | +| 0.9690 | 43930 | 0.0 | - | - | - | - | - | - | +| 0.9692 | 43940 | 0.0 | - | - | - | - | - | - | +| 0.9694 | 43950 | 0.0 | - | - | - | - | - | - | +| 0.9696 | 43960 | 0.0 | - | - | - | - | - | - | +| 0.9698 | 43970 | 0.0 | - | - | - | - | - | - | +| 0.9701 | 43980 | 0.0 | - | - | - | - | - | - | +| 0.9703 | 43990 | 0.0 | - | - | - | - | - | - | +| 0.9705 | 44000 | 0.0 | - | - | - | - | - | - | +| 0.9707 | 44010 | 0.0001 | - | - | - | - | - | - | +| 0.9709 | 44020 | 0.0 | - | - | - | - | - | - | +| 0.9712 | 44030 | 0.0 | - | - | - | - | - | - | +| 0.9714 | 44040 | 0.0 | - | - | - | - | - | - | +| 0.9716 | 44050 | 0.0 | - | - | - | - | - | - | +| 0.9718 | 44060 | 0.0 | - | - | - | - | - | - | +| 0.9720 | 44070 | 0.0 | - | - | - | - | - | - | +| 0.9723 | 44080 | 0.0 | - | - | - | - | - | - | +| 0.9725 | 44090 | 0.0 | - | - | - | - | - | - | +| 0.9727 | 44100 | 0.0 | - | - | - | - | - | - | +| 0.9729 | 44110 | 0.0 | - | - | - | - | - | - | +| 0.9731 | 44120 | 0.0 | - | - | - | - | - | - | +| 0.9734 | 44130 | 0.0 | - | - | - | - | - | - | +| 0.9736 | 44140 | 0.0 | - | - | - | - | - | - | +| 0.9738 | 44150 | 0.0 | - | - | - | - | - | - | +| 0.9740 | 44160 | 0.0 | - | - | - | - | - | - | +| 0.9742 | 44170 | 0.0 | - | - | - | - | - | - | +| 0.9745 | 44180 | 0.0 | - | - | - | - | - | - | +| 0.9747 | 44190 | 0.0 | - | - | - | - | - | - | +| 0.9749 | 44200 | 0.0 | - | - | - | - | - | - | +| 0.9751 | 44210 | 0.0 | - | - | - | - | - | - | +| 0.9754 | 44220 | 0.0 | - | - | - | - | - | - | +| 0.9756 | 44230 | 0.0 | - | - | - | - | - | - | +| 0.9758 | 44240 | 0.0 | - | - | - | - | - | - | +| 0.9760 | 44250 | 0.0 | - | - | - | - | - | - | +| 0.9762 | 44260 | 0.0 | - | - | - | - | - | - | +| 0.9765 | 44270 | 0.0 | - | - | - | - | - | - | +| 0.9767 | 44280 | 0.0 | - | - | - | - | - | - | +| 0.9769 | 44290 | 0.0 | - | - | - | - | - | - | +| 0.9771 | 44300 | 0.0 | - | - | - | - | - | - | +| 0.9773 | 44310 | 0.0 | - | - | - | - | - | - | +| 0.9776 | 44320 | 0.0 | - | - | - | - | - | - | +| 0.9778 | 44330 | 0.0 | - | - | - | - | - | - | +| 0.9780 | 44340 | 0.0 | - | - | - | - | - | - | +| 0.9782 | 44350 | 0.0 | - | - | - | - | - | - | +| 0.9784 | 44360 | 0.0 | - | - | - | - | - | - | +| 0.9787 | 44370 | 0.0 | - | - | - | - | - | - | +| 0.9789 | 44380 | 0.0 | - | - | - | - | - | - | +| 0.9791 | 44390 | 0.0 | - | - | - | - | - | - | +| 0.9793 | 44400 | 0.0 | - | - | - | - | - | - | +| 0.9795 | 44410 | 0.0 | - | - | - | - | - | - | +| 0.9798 | 44420 | 0.0 | - | - | - | - | - | - | +| 0.9800 | 44430 | 0.0 | - | - | - | - | - | - | +| 0.9802 | 44440 | 0.0 | - | - | - | - | - | - | +| 0.9804 | 44450 | 0.0 | - | - | - | - | - | - | +| 0.9806 | 44460 | 0.0 | - | - | - | - | - | - | +| 0.9809 | 44470 | 0.0 | - | - | - | - | - | - | +| 0.9811 | 44480 | 0.0 | - | - | - | - | - | - | +| 0.9813 | 44490 | 0.0 | - | - | - | - | - | - | +| 0.9815 | 44500 | 0.0 | - | - | - | - | - | - | +| 0.9817 | 44510 | 0.0 | - | - | - | - | - | - | +| 0.9820 | 44520 | 0.0 | - | - | - | - | - | - | +| 0.9822 | 44530 | 0.0 | - | - | - | - | - | - | +| 0.9824 | 44540 | 0.0 | - | - | - | - | - | - | +| 0.9826 | 44550 | 0.0 | - | - | - | - | - | - | +| 0.9829 | 44560 | 0.0 | - | - | - | - | - | - | +| 0.9831 | 44570 | 0.0 | - | - | - | - | - | - | +| 0.9833 | 44580 | 0.0 | - | - | - | - | - | - | +| 0.9835 | 44590 | 0.0 | - | - | - | - | - | - | +| 0.9837 | 44600 | 0.0 | - | - | - | - | - | - | +| 0.9840 | 44610 | 0.0 | - | - | - | - | - | - | +| 0.9842 | 44620 | 0.0 | - | - | - | - | - | - | +| 0.9844 | 44630 | 0.0 | - | - | - | - | - | - | +| 0.9846 | 44640 | 0.0 | - | - | - | - | - | - | +| 0.9848 | 44650 | 0.0 | - | - | - | - | - | - | +| 0.9851 | 44660 | 0.0 | - | - | - | - | - | - | +| 0.9853 | 44670 | 0.0 | - | - | - | - | - | - | +| 0.9855 | 44680 | 0.0 | - | - | - | - | - | - | +| 0.9857 | 44690 | 0.0 | - | - | - | - | - | - | +| 0.9859 | 44700 | 0.0 | - | - | - | - | - | - | +| 0.9862 | 44710 | 0.0 | - | - | - | - | - | - | +| 0.9864 | 44720 | 0.0 | - | - | - | - | - | - | +| 0.9866 | 44730 | 0.0 | - | - | - | - | - | - | +| 0.9868 | 44740 | 0.0 | - | - | - | - | - | - | +| 0.9870 | 44750 | 0.0 | - | - | - | - | - | - | +| 0.9873 | 44760 | 0.0 | - | - | - | - | - | - | +| 0.9875 | 44770 | 0.0 | - | - | - | - | - | - | +| 0.9877 | 44780 | 0.0 | - | - | - | - | - | - | +| 0.9879 | 44790 | 0.0 | - | - | - | - | - | - | +| 0.9881 | 44800 | 0.0 | - | - | - | - | - | - | +| 0.9884 | 44810 | 0.0 | - | - | - | - | - | - | +| 0.9886 | 44820 | 0.0 | - | - | - | - | - | - | +| 0.9888 | 44830 | 0.0 | - | - | - | - | - | - | +| 0.9890 | 44840 | 0.0 | - | - | - | - | - | - | +| 0.9892 | 44850 | 0.0 | - | - | - | - | - | - | +| 0.9895 | 44860 | 0.0 | - | - | - | - | - | - | +| 0.9897 | 44870 | 0.0 | - | - | - | - | - | - | +| 0.9899 | 44880 | 0.0 | - | - | - | - | - | - | +| 0.9901 | 44890 | 0.0 | - | - | - | - | - | - | +| 0.9904 | 44900 | 0.0 | - | - | - | - | - | - | +| 0.9906 | 44910 | 0.0 | - | - | - | - | - | - | +| 0.9908 | 44920 | 0.0 | - | - | - | - | - | - | +| 0.9910 | 44930 | 0.0 | - | - | - | - | - | - | +| 0.9912 | 44940 | 0.0 | - | - | - | - | - | - | +| 0.9915 | 44950 | 0.0 | - | - | - | - | - | - | +| 0.9917 | 44960 | 0.0 | - | - | - | - | - | - | +| 0.9919 | 44970 | 0.0 | - | - | - | - | - | - | +| 0.9921 | 44980 | 0.0 | - | - | - | - | - | - | +| 0.9923 | 44990 | 0.0 | - | - | - | - | - | - | +| 0.9926 | 45000 | 0.0 | - | - | - | - | - | - | +| 0.9928 | 45010 | 0.0 | - | - | - | - | - | - | +| 0.9930 | 45020 | 0.0 | - | - | - | - | - | - | +| 0.9932 | 45030 | 0.0 | - | - | - | - | - | - | +| 0.9934 | 45040 | 0.0 | - | - | - | - | - | - | +| 0.9937 | 45050 | 0.0 | - | - | - | - | - | - | +| 0.9939 | 45060 | 0.0 | - | - | - | - | - | - | +| 0.9941 | 45070 | 0.0 | - | - | - | - | - | - | +| 0.9943 | 45080 | 0.0 | - | - | - | - | - | - | +| 0.9945 | 45090 | 0.0 | - | - | - | - | - | - | +| 0.9948 | 45100 | 0.0 | - | - | - | - | - | - | +| 0.9950 | 45110 | 0.0 | - | - | - | - | - | - | +| 0.9952 | 45120 | 0.0 | - | - | - | - | - | - | +| 0.9954 | 45130 | 0.0 | - | - | - | - | - | - | +| 0.9956 | 45140 | 0.0 | - | - | - | - | - | - | +| 0.9959 | 45150 | 0.0 | - | - | - | - | - | - | +| 0.9961 | 45160 | 0.0 | - | - | - | - | - | - | +| 0.9963 | 45170 | 0.0 | - | - | - | - | - | - | +| 0.9965 | 45180 | 0.0 | - | - | - | - | - | - | +| 0.9967 | 45190 | 0.0 | - | - | - | - | - | - | +| 0.9970 | 45200 | 0.0 | - | - | - | - | - | - | +| 0.9972 | 45210 | 0.0 | - | - | - | - | - | - | +| 0.9974 | 45220 | 0.0 | - | - | - | - | - | - | +| 0.9976 | 45230 | 0.0 | - | - | - | - | - | - | +| 0.9978 | 45240 | 0.0 | - | - | - | - | - | - | +| 0.9981 | 45250 | 0.0 | - | - | - | - | - | - | +| 0.9983 | 45260 | 0.0 | - | - | - | - | - | - | +| 0.9985 | 45270 | 0.0 | - | - | - | - | - | - | +| 0.9987 | 45280 | 0.0 | - | - | - | - | - | - | +| 0.9990 | 45290 | 0.0 | - | - | - | - | - | - | +| 0.9992 | 45300 | 0.0 | - | - | - | - | - | - | +| 0.9994 | 45310 | 0.0001 | - | - | - | - | - | - | +| 0.9996 | 45320 | 0.0 | - | - | - | - | - | - | +| 0.9998 | 45330 | 0.0 | - | - | - | - | - | - | +| **1.0** | **45337** | **-** | **0.0006** | **0.3323** | **0.3313** | **0.331** | **0.3306** | **0.3284** | + +* The bold row denotes the saved checkpoint. +
+ +### Framework Versions +- Python: 3.10.13 +- Sentence Transformers: 3.3.1 +- Transformers: 4.41.2 +- PyTorch: 2.1.2 +- Accelerate: 0.29.3 +- 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} +} +``` + + + + + + \ No newline at end of file