SentenceTransformer based on hiieu/halong_embedding

This is a sentence-transformers model finetuned from hiieu/halong_embedding 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: hiieu/halong_embedding
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("anhtuansh/halong_embedding-Financial-Matryoshka-1e-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 and dim_512
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512
cosine_accuracy@1	0.0772	0.0773
cosine_accuracy@3	0.2277	0.2275
cosine_accuracy@5	0.3827	0.3768
cosine_accuracy@10	0.7556	0.7566
cosine_precision@1	0.0772	0.0773
cosine_precision@3	0.0759	0.0758
cosine_precision@5	0.0765	0.0754
cosine_precision@10	0.0756	0.0757
cosine_recall@1	0.0772	0.0773
cosine_recall@3	0.2277	0.2275
cosine_recall@5	0.3827	0.3768
cosine_recall@10	0.7556	0.7566
cosine_ndcg@10	0.3447	0.3445
cosine_mrr@10	0.223	0.2226
cosine_map@100	0.2346	0.2341

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
min: 14 tokens
mean: 147.11 tokens
max: 512 tokens

min: 18 tokens
mean: 58.38 tokens
max: 512 tokens

	positive	anchor
type	string	string
details	min: 14 tokens mean: 147.11 tokens max: 512 tokens	min: 18 tokens mean: 58.38 tokens max: 512 tokens

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 with these parameters:

{
    "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
min: 17 tokens
mean: 140.54 tokens
max: 512 tokens

min: 18 tokens
mean: 58.04 tokens
max: 293 tokens

	positive	anchor
type	string	string
details	min: 17 tokens mean: 140.54 tokens max: 512 tokens	min: 18 tokens mean: 58.04 tokens max: 293 tokens

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 with these parameters:

{
    "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
0	0	-	-	0.2210	0.2171
0.0002	10	0.0497	-	-	-
0.0004	20	0.0396	-	-	-
0.0007	30	0.0246	-	-	-
0.0009	40	0.0846	-	-	-
0.0011	50	0.0078	-	-	-
0.0013	60	0.1394	-	-	-
0.0015	70	0.0095	-	-	-
0.0018	80	0.0084	-	-	-
0.0020	90	0.002	-	-	-
0.0022	100	0.0053	-	-	-
0.0024	110	0.0676	-	-	-
0.0026	120	0.0761	-	-	-
0.0029	130	0.0044	-	-	-
0.0031	140	0.0004	-	-	-
0.0033	150	0.0012	-	-	-
0.0035	160	0.0121	-	-	-
0.0037	170	0.0054	-	-	-
0.0040	180	0.0038	-	-	-
0.0042	190	0.0022	-	-	-
0.0044	200	0.0036	-	-	-
0.0046	210	0.0021	-	-	-
0.0049	220	0.0018	-	-	-
0.0051	230	0.0009	-	-	-
0.0053	240	0.0005	-	-	-
0.0055	250	0.0009	-	-	-
0.0057	260	0.0007	-	-	-
0.0060	270	0.0355	-	-	-
0.0062	280	0.0002	-	-	-
0.0064	290	0.001	-	-	-
0.0066	300	0.0014	-	-	-
0.0068	310	0.0008	-	-	-
0.0071	320	0.0019	-	-	-
0.0073	330	0.0006	-	-	-
0.0075	340	0.0006	-	-	-
0.0077	350	0.0016	-	-	-
0.0079	360	0.0003	-	-	-
0.0082	370	0.048	-	-	-
0.0084	380	0.0	-	-	-
0.0086	390	0.0002	-	-	-
0.0088	400	0.001	-	-	-
0.0090	410	0.0026	-	-	-
0.0093	420	0.0	-	-	-
0.0095	430	0.0016	-	-	-
0.0097	440	0.0001	-	-	-
0.0099	450	0.0	-	-	-
0.0101	460	0.0	-	-	-
0.0104	470	0.0001	-	-	-
0.0106	480	0.0397	-	-	-
0.0108	490	0.0005	-	-	-
0.0110	500	0.0056	-	-	-
0.0112	510	0.0194	-	-	-
0.0115	520	0.0	-	-	-
0.0117	530	0.0012	-	-	-
0.0119	540	0.0	-	-	-
0.0121	550	0.0	-	-	-
0.0124	560	0.0002	-	-	-
0.0126	570	0.0001	-	-	-
0.0128	580	0.0	-	-	-
0.0130	590	0.0001	-	-	-
0.0132	600	0.0002	-	-	-
0.0135	610	0.0	-	-	-
0.0137	620	0.0	-	-	-
0.0139	630	0.0033	-	-	-
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	0.0001	-	-	-
0.0157	710	0.0001	-	-	-
0.0159	720	0.0	-	-	-
0.0161	730	0.0	-	-	-
0.0163	740	0.0	-	-	-
0.0165	750	0.0	-	-	-
0.0168	760	0.0	-	-	-
0.0170	770	0.0	-	-	-
0.0172	780	0.0001	-	-	-
0.0174	790	0.0	-	-	-
0.0176	800	0.0001	-	-	-
0.0179	810	0.0	-	-	-
0.0181	820	0.0	-	-	-
0.0183	830	0.0053	-	-	-
0.0185	840	0.0	-	-	-
0.0187	850	0.0	-	-	-
0.0190	860	0.0002	-	-	-
0.0192	870	0.0	-	-	-
0.0194	880	0.0001	-	-	-
0.0196	890	0.0007	-	-	-
0.0199	900	0.0	-	-	-
0.0201	910	0.009	-	-	-
0.0203	920	0.0	-	-	-
0.0205	930	0.0002	-	-	-
0.0207	940	0.0	-	-	-
0.0210	950	0.0	-	-	-
0.0212	960	0.0006	-	-	-
0.0214	970	0.0	-	-	-
0.0216	980	0.0034	-	-	-
0.0218	990	0.0	-	-	-
0.0221	1000	0.0	-	-	-
0.0223	1010	0.0001	-	-	-
0.0225	1020	0.0	-	-	-
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	0.0238	-	-	-
0.0240	1090	0.0	-	-	-
0.0243	1100	0.0	-	-	-
0.0245	1110	0.0008	-	-	-
0.0247	1120	0.0	-	-	-
0.0249	1130	0.0013	-	-	-
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	0.0001	-	-	-
0.0269	1220	0.003	-	-	-
0.0271	1230	0.0	-	-	-
0.0274	1240	0.0004	-	-	-
0.0276	1250	0.0	-	-	-
0.0278	1260	0.0	-	-	-
0.0280	1270	0.0	-	-	-
0.0282	1280	0.0001	-	-	-
0.0285	1290	0.0001	-	-	-
0.0287	1300	0.0	-	-	-
0.0289	1310	0.0002	-	-	-
0.0291	1320	0.0055	-	-	-
0.0293	1330	0.0	-	-	-
0.0296	1340	0.0	-	-	-
0.0298	1350	0.0	-	-	-
0.0300	1360	0.0	-	-	-
0.0302	1370	0.0001	-	-	-
0.0304	1380	0.0	-	-	-
0.0307	1390	0.0	-	-	-
0.0309	1400	0.0	-	-	-
0.0311	1410	0.0	-	-	-
0.0313	1420	0.0	-	-	-
0.0315	1430	0.0	-	-	-
0.0318	1440	0.0001	-	-	-
0.0320	1450	0.0	-	-	-
0.0322	1460	0.0	-	-	-
0.0324	1470	0.0001	-	-	-
0.0326	1480	0.0	-	-	-
0.0329	1490	0.0	-	-	-
0.0331	1500	0.0001	-	-	-
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0.0340	1540	0.0	-	-	-
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0.0344	1560	0.0001	-	-	-
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0.0348	1580	0.0	-	-	-
0.0351	1590	0.0011	-	-	-
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0.0357	1620	0.0002	-	-	-
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0.0362	1640	0.0008	-	-	-
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0.0371	1680	0.0001	-	-	-
0.0373	1690	0.008	-	-	-
0.0375	1700	0.0013	-	-	-
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0.0384	1740	0.002	-	-	-
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0.0388	1760	0.0008	-	-	-
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0.0393	1780	0.0	-	-	-
0.0395	1790	0.0	-	-	-
0.0397	1800	0.0	-	-	-
0.0399	1810	0.0001	-	-	-
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0.0406	1840	0.0039	-	-	-
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0.0419	1900	0.0	-	-	-
0.0421	1910	0.0	-	-	-
0.0423	1920	0.0042	-	-	-
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	0.0002	-	-	-
0.0443	2010	0.0	-	-	-
0.0446	2020	0.0	-	-	-
0.0448	2030	0.0	-	-	-
0.0450	2040	0.0	-	-	-
0.0452	2050	0.0001	-	-	-
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0.0459	2080	0.0	-	-	-
0.0461	2090	0.0001	-	-	-
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0.0609	2760	0.0002	-	-	-
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0.0622	2820	0.0001	-	-	-
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0.0662	3000	0.0	-	-	-
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0.0684	3100	0.0	-	-	-
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0.0706	3200	0.0	-	-	-
0.0708	3210	0.0003	-	-	-
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0.0715	3240	0.0	-	-	-
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0.0721	3270	0.0001	-	-	-
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0.0871	3950	0.0	-	-	-
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0.0876	3970	0.0019	-	-	-
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0.0880	3990	0.0	-	-	-
0.0882	4000	0.0002	-	-	-
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0.0889	4030	0.0002	-	-	-
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0.0893	4050	0.0	-	-	-
0.0896	4060	0.0001	-	-	-
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0.0900	4080	0.0	-	-	-
0.0902	4090	0.0	-	-	-
0.0904	4100	0.0	-	-	-
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0.0951	4310	0.0009	-	-	-
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0.0955	4330	0.0011	-	-	-
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0.0959	4350	0.0001	-	-	-
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0.0964	4370	0.0001	-	-	-
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0.0968	4390	0.0	-	-	-
0.0970	4400	0.0	-	-	-
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0.0975	4420	0.0001	-	-	-
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0.1010	4580	0.04	-	-	-
0.1012	4590	0.0	-	-	-
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0.1021	4630	0.0005	-	-	-
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0.1030	4670	0.0	-	-	-
0.1032	4680	0.0	-	-	-
0.1034	4690	0.0002	-	-	-
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0.1039	4710	0.0	-	-	-
0.1041	4720	0.0	-	-	-
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0.1045	4740	0.0001	-	-	-
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0.1050	4760	0.0	-	-	-
0.1052	4770	0.0	-	-	-
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0.1057	4790	0.0	-	-	-
0.1059	4800	0.0001	-	-	-
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0.1063	4820	0.0	-	-	-
0.1065	4830	0.0	-	-	-
0.1068	4840	0.0	-	-	-
0.1070	4850	0.0001	-	-	-
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0.1074	4870	0.0	-	-	-
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0.1081	4900	0.0	-	-	-
0.1083	4910	0.0001	-	-	-
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0.1090	4940	0.0444	-	-	-
0.1092	4950	0.0001	-	-	-
0.1094	4960	0.0001	-	-	-
0.1096	4970	0.0001	-	-	-
0.1098	4980	0.0	-	-	-
0.1101	4990	0.0001	-	-	-
0.1103	5000	0.0	-	-	-
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0.1109	5030	0.0583	-	-	-
0.1112	5040	0.0	-	-	-
0.1114	5050	0.0015	-	-	-
0.1116	5060	0.0006	-	-	-
0.1118	5070	0.0029	-	-	-
0.1120	5080	0.0024	-	-	-
0.1123	5090	0.002	-	-	-
0.1125	5100	0.0002	-	-	-
0.1127	5110	0.0008	-	-	-
0.1129	5120	0.0001	-	-	-
0.1132	5130	0.0001	-	-	-
0.1134	5140	0.0	-	-	-
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0.1149	5210	0.0005	-	-	-
0.1151	5220	0.0399	-	-	-
0.1154	5230	0.0	-	-	-
0.1156	5240	0.0001	-	-	-
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0.1160	5260	0.0	-	-	-
0.1162	5270	0.0001	-	-	-
0.1165	5280	0.0	-	-	-
0.1167	5290	0.0007	-	-	-
0.1169	5300	0.0003	-	-	-
0.1171	5310	0.0001	-	-	-
0.1173	5320	0.0	-	-	-
0.1176	5330	0.0	-	-	-
0.1178	5340	0.0	-	-	-
0.1180	5350	0.0	-	-	-
0.1182	5360	0.0009	-	-	-
0.1184	5370	0.0	-	-	-
0.1187	5380	0.0	-	-	-
0.1189	5390	0.0047	-	-	-
0.1191	5400	0.0	-	-	-
0.1193	5410	0.0001	-	-	-
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0.1198	5430	0.0	-	-	-
0.1200	5440	0.0	-	-	-
0.1202	5450	0.0	-	-	-
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0.1213	5500	0.0	-	-	-
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0.1224	5550	0.0	-	-	-
0.1226	5560	0.0	-	-	-
0.1229	5570	0.0002	-	-	-
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0.1235	5600	0.0	-	-	-
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0.1242	5630	0.0	-	-	-
0.1244	5640	0.0	-	-	-
0.1246	5650	0.0001	-	-	-
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0.1251	5670	0.0	-	-	-
0.1253	5680	0.0	-	-	-
0.1255	5690	0.0	-	-	-
0.1257	5700	0.0	-	-	-
0.1259	5710	0.0289	-	-	-
0.1262	5720	0.0122	-	-	-
0.1264	5730	0.0	-	-	-
0.1266	5740	0.0001	-	-	-
0.1268	5750	0.0001	-	-	-
0.1270	5760	0.0002	-	-	-
0.1273	5770	0.0	-	-	-
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0.1383	6270	0.0011	-	-	-
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0.1500	6800	0.0	-	-	-
0.1502	6810	0.0	-	-	-
0.1504	6820	0.0	-	-	-
0.1506	6830	0.0727	-	-	-
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0.1522	6900	0.0014	-	-	-
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0.1540	6980	0.0	-	-	-
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0.1562	7080	0.0006	-	-	-
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0.1566	7100	0.0001	-	-	-
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0.1575	7140	0.0	-	-	-
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0.1670	7570	0.0216	-	-	-
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0.1795	8140	0.0001	-	-	-
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0.1864	8450	0.0	-	-	-
0.1866	8460	0.0	-	-	-
0.1868	8470	0.0002	-	-	-
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0.1873	8490	0.0	-	-	-
0.1875	8500	0.0	-	-	-
0.1877	8510	0.0	-	-	-
0.1879	8520	0.0	-	-	-
0.1881	8530	0.0	-	-	-
0.1884	8540	0.0	-	-	-
0.1886	8550	0.0	-	-	-
0.1888	8560	0.0	-	-	-
0.1890	8570	0.0	-	-	-
0.1892	8580	0.0	-	-	-
0.1895	8590	0.0001	-	-	-
0.1897	8600	0.0	-	-	-
0.1899	8610	0.0	-	-	-
0.1901	8620	0.0	-	-	-
0.1904	8630	0.0	-	-	-
0.1906	8640	0.0	-	-	-
0.1908	8650	0.0	-	-	-
0.1910	8660	0.0	-	-	-
0.1912	8670	0.0	-	-	-
0.1915	8680	0.0	-	-	-
0.1917	8690	0.0	-	-	-
0.1919	8700	0.0	-	-	-
0.1921	8710	0.0	-	-	-
0.1923	8720	0.0	-	-	-
0.1926	8730	0.0	-	-	-
0.1928	8740	0.0	-	-	-
0.1930	8750	0.0	-	-	-
0.1932	8760	0.0	-	-	-
0.1934	8770	0.0	-	-	-
0.1937	8780	0.0	-	-	-
0.1939	8790	0.0	-	-	-
0.1941	8800	0.0	-	-	-
0.1943	8810	0.0	-	-	-
0.1945	8820	0.0002	-	-	-
0.1948	8830	0.0	-	-	-
0.1950	8840	0.0	-	-	-
0.1952	8850	0.0	-	-	-
0.1954	8860	0.0	-	-	-
0.1956	8870	0.0111	-	-	-
0.1959	8880	0.0	-	-	-
0.1961	8890	0.0002	-	-	-
0.1963	8900	0.0	-	-	-
0.1965	8910	0.0	-	-	-
0.1967	8920	0.0	-	-	-
0.1970	8930	0.0	-	-	-
0.1972	8940	0.0	-	-	-
0.1974	8950	0.0	-	-	-
0.1976	8960	0.0	-	-	-
0.1978	8970	0.0	-	-	-
0.1981	8980	0.0	-	-	-
0.1983	8990	0.0	-	-	-
0.1985	9000	0.0	-	-	-
0.1987	9010	0.0	-	-	-
0.1990	9020	0.0	-	-	-
0.1992	9030	0.0	-	-	-
0.1994	9040	0.0001	-	-	-
0.1996	9050	0.0	-	-	-
0.1998	9060	0.0	-	-	-
0.2001	9070	0.0	-	-	-
0.2003	9080	0.0	-	-	-
0.2005	9090	0.0	-	-	-
0.2007	9100	0.0	-	-	-
0.2009	9110	0.0	-	-	-
0.2012	9120	0.0	-	-	-
0.2014	9130	0.0001	-	-	-
0.2016	9140	0.0	-	-	-
0.2018	9150	0.0021	-	-	-
0.2020	9160	0.0	-	-	-
0.2023	9170	0.0003	-	-	-
0.2025	9180	0.0001	-	-	-
0.2027	9190	0.0001	-	-	-
0.2029	9200	0.0	-	-	-
0.2031	9210	0.0	-	-	-
0.2034	9220	0.0	-	-	-
0.2036	9230	0.0	-	-	-
0.2038	9240	0.0	-	-	-
0.2040	9250	0.0001	-	-	-
0.2042	9260	0.0	-	-	-
0.2045	9270	0.0	-	-	-
0.2047	9280	0.0	-	-	-
0.2049	9290	0.0007	-	-	-
0.2051	9300	0.0	-	-	-
0.2053	9310	0.0	-	-	-
0.2056	9320	0.0032	-	-	-
0.2058	9330	0.0001	-	-	-
0.2060	9340	0.0003	-	-	-
0.2062	9350	0.0	-	-	-
0.2065	9360	0.0001	-	-	-
0.2067	9370	0.0024	-	-	-
0.2069	9380	0.0098	-	-	-
0.2071	9390	0.0	-	-	-
0.2073	9400	0.0001	-	-	-
0.2076	9410	0.0	-	-	-
0.2078	9420	0.0568	-	-	-
0.2080	9430	0.0001	-	-	-
0.2082	9440	0.0001	-	-	-
0.2084	9450	0.0	-	-	-
0.2087	9460	0.0001	-	-	-
0.2089	9470	0.0	-	-	-
0.2091	9480	0.0001	-	-	-
0.2093	9490	0.0	-	-	-
0.2095	9500	0.0	-	-	-
0.2098	9510	0.0	-	-	-
0.2100	9520	0.0	-	-	-
0.2102	9530	0.0	-	-	-
0.2104	9540	0.0	-	-	-
0.2106	9550	0.0	-	-	-
0.2109	9560	0.0	-	-	-
0.2111	9570	0.0	-	-	-
0.2113	9580	0.0	-	-	-
0.2115	9590	0.0	-	-	-
0.2117	9600	0.0	-	-	-
0.2120	9610	0.0	-	-	-
0.2122	9620	0.0	-	-	-
0.2124	9630	0.0002	-	-	-
0.2126	9640	0.0001	-	-	-
0.2128	9650	0.0	-	-	-
0.2131	9660	0.0	-	-	-
0.2133	9670	0.0	-	-	-
0.2135	9680	0.0002	-	-	-
0.2137	9690	0.0	-	-	-
0.2140	9700	0.0001	-	-	-
0.2142	9710	0.052	-	-	-
0.2144	9720	0.0	-	-	-
0.2146	9730	0.0	-	-	-
0.2148	9740	0.0001	-	-	-
0.2151	9750	0.0	-	-	-
0.2153	9760	0.0001	-	-	-
0.2155	9770	0.0	-	-	-
0.2157	9780	0.0005	-	-	-
0.2159	9790	0.0	-	-	-
0.2162	9800	0.0	-	-	-
0.2164	9810	0.0	-	-	-
0.2166	9820	0.0	-	-	-
0.2168	9830	0.0	-	-	-
0.2170	9840	0.0	-	-	-
0.2173	9850	0.0	-	-	-
0.2175	9860	0.0002	-	-	-
0.2177	9870	0.0008	-	-	-
0.2179	9880	0.0005	-	-	-
0.2181	9890	0.0003	-	-	-
0.2184	9900	0.0	-	-	-
0.2186	9910	0.0	-	-	-
0.2188	9920	0.0	-	-	-
0.2190	9930	0.0	-	-	-
0.2192	9940	0.0002	-	-	-
0.2195	9950	0.0	-	-	-
0.2197	9960	0.0	-	-	-
0.2199	9970	0.0	-	-	-
0.2201	9980	0.0	-	-	-
0.2203	9990	0.0	-	-	-
0.2206	10000	0.0	-	-	-
0.2208	10010	0.0	-	-	-
0.2210	10020	0.0	-	-	-
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0.2215	10040	0.0	-	-	-
0.2217	10050	0.0	-	-	-
0.2219	10060	0.0	-	-	-
0.2221	10070	0.0	-	-	-
0.2223	10080	0.0	-	-	-
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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	0.0001	-	-	-
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	0.0021	-	-	-
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	0.0038	-	-	-
0.9996	45320	0.0	-	-	-
0.9998	45330	0.0	-	-	-
1.0	45337	-	0.0003	0.3447	0.3445

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.3.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.29.3
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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

@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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Model size

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Tensor type

F32

Model tree for anhtuansh/halong_embedding-Financial-Matryoshka-1e-200k

Base model

intfloat/multilingual-e5-base

Finetuned

contextboxai/halong_embedding

Finetuned

(28)

this model

Papers for anhtuansh/halong_embedding-Financial-Matryoshka-1e-200k

Evaluation results

Cosine Accuracy@1 on dim 768
self-reported

0.077
Cosine Accuracy@3 on dim 768
self-reported

0.228
Cosine Accuracy@5 on dim 768
self-reported

0.383
Cosine Accuracy@10 on dim 768
self-reported

0.756
Cosine Precision@1 on dim 768
self-reported

0.077
Cosine Precision@3 on dim 768
self-reported

0.076
Cosine Precision@5 on dim 768
self-reported

0.077
Cosine Precision@10 on dim 768
self-reported

0.076
Cosine Recall@1 on dim 768
self-reported

0.077
Cosine Recall@3 on dim 768
self-reported

0.228
Cosine Recall@5 on dim 768
self-reported

0.383
Cosine Recall@10 on dim 768
self-reported

0.756
Cosine Ndcg@10 on dim 768
self-reported

0.345
Cosine Mrr@10 on dim 768
self-reported

0.223
Cosine Map@100 on dim 768
self-reported

0.235
Cosine Accuracy@1 on dim 512
self-reported

0.077
Cosine Accuracy@3 on dim 512
self-reported

0.228
Cosine Accuracy@5 on dim 512
self-reported

0.377
Cosine Accuracy@10 on dim 512
self-reported

0.757
Cosine Precision@1 on dim 512
self-reported

0.077