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qcsun
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financial-embedding

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:377615
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
xet
Community

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-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
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价调整引发热议，机构认为或提振短期流动性',
    '市场关注Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价变动，分析称该调整可能改善短期交易活跃度',
    'HLGN+',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9768, 0.0959],
#         [0.9768, 1.0000, 0.1028],
#         [0.0959, 0.1028, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

  • Size: 377,615 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 3 tokens
    • mean: 14.38 tokens
    • max: 60 tokens
    • min: 3 tokens
    • mean: 14.38 tokens
    • max: 60 tokens
  • Samples:
    sentence_0 sentence_1
    苍南仪表 苍南自动化仪表
    KINS Technology Group, Inc. Warrant 2020- 2025 on KINS Tech Grp KINZW
    兴业合金(00505.HK)技术面呈现多头排列，短期或延续上涨趋势 00505.HK兴业合金日线图出现买入信号，技术派看好后市走势
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • num_train_epochs: 30
  • fp16: True
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 30
  • 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: False
  • fp16: True
  • 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: False
  • 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: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss
0.0424 500 0.6966
0.0847 1000 0.4987
0.1271 1500 0.463
0.1695 2000 0.4364
0.2118 2500 0.4041
0.2542 3000 0.3923
0.2966 3500 0.3788
0.3390 4000 0.3603
0.3813 4500 0.3442
0.4237 5000 0.3388
0.4661 5500 0.3252
0.5084 6000 0.3133
0.5508 6500 0.311
0.5932 7000 0.3027
0.6355 7500 0.283
0.6779 8000 0.2847
0.7203 8500 0.279
0.7626 9000 0.2753
0.8050 9500 0.2647
0.8474 10000 0.2687
0.8898 10500 0.2572
0.9321 11000 0.2562
0.9745 11500 0.2351
1.0169 12000 0.2254
1.0592 12500 0.1966
1.1016 13000 0.2082
1.1440 13500 0.1856
1.1863 14000 0.1916
1.2287 14500 0.2003
1.2711 15000 0.1959
1.3134 15500 0.1857
1.3558 16000 0.1854
1.3982 16500 0.1797
1.4406 17000 0.1774
1.4829 17500 0.1813
1.5253 18000 0.1717
1.5677 18500 0.1638
1.6100 19000 0.1658
1.6524 19500 0.1764
1.6948 20000 0.1681
1.7371 20500 0.1589
1.7795 21000 0.1539
1.8219 21500 0.1575
1.8642 22000 0.1558
1.9066 22500 0.158
1.9490 23000 0.1467
1.9914 23500 0.1504
2.0337 24000 0.1221
2.0761 24500 0.1112
2.1185 25000 0.109
2.1608 25500 0.1106
2.2032 26000 0.1131
2.2456 26500 0.1078
2.2879 27000 0.1042
2.3303 27500 0.1024
2.3727 28000 0.1012
2.4150 28500 0.1088
2.4574 29000 0.1022
2.4998 29500 0.1067
2.5422 30000 0.105
2.5845 30500 0.0982
2.6269 31000 0.1033
2.6693 31500 0.1029
2.7116 32000 0.0988
2.7540 32500 0.0999
2.7964 33000 0.094
2.8387 33500 0.0912
2.8811 34000 0.0952
2.9235 34500 0.0953
2.9659 35000 0.0947
3.0082 35500 0.0857
3.0506 36000 0.0697
3.0930 36500 0.067
3.1353 37000 0.063
3.1777 37500 0.0673
3.2201 38000 0.067
3.2624 38500 0.0684
3.3048 39000 0.0643
3.3472 39500 0.0656
3.3895 40000 0.0657
3.4319 40500 0.071
3.4743 41000 0.0671
3.5167 41500 0.0601
3.5590 42000 0.0614
3.6014 42500 0.061
3.6438 43000 0.0599
3.6861 43500 0.0586
3.7285 44000 0.0613
3.7709 44500 0.0604
3.8132 45000 0.06
3.8556 45500 0.0539
3.8980 46000 0.0576
3.9403 46500 0.0605
3.9827 47000 0.0563
4.0251 47500 0.0485
4.0675 48000 0.0409
4.1098 48500 0.0426
4.1522 49000 0.0437
4.1946 49500 0.0422
4.2369 50000 0.0395
4.2793 50500 0.0395
4.3217 51000 0.0425
4.3640 51500 0.0379
4.4064 52000 0.0428
4.4488 52500 0.0412
4.4911 53000 0.0399
4.5335 53500 0.04
4.5759 54000 0.0416
4.6183 54500 0.0351
4.6606 55000 0.037
4.7030 55500 0.0408
4.7454 56000 0.038
4.7877 56500 0.04
4.8301 57000 0.0384
4.8725 57500 0.0372
4.9148 58000 0.0393
4.9572 58500 0.038
4.9996 59000 0.044
5.0419 59500 0.0278
5.0843 60000 0.0257
5.1267 60500 0.0272
5.1691 61000 0.0322
5.2114 61500 0.0234
5.2538 62000 0.029
5.2962 62500 0.0255
5.3385 63000 0.0238
5.3809 63500 0.0287
5.4233 64000 0.0239
5.4656 64500 0.0273
5.5080 65000 0.028
5.5504 65500 0.0283
5.5927 66000 0.027
5.6351 66500 0.0255
5.6775 67000 0.0258
5.7199 67500 0.025
5.7622 68000 0.0251
5.8046 68500 0.0261
5.8470 69000 0.027
5.8893 69500 0.0245
5.9317 70000 0.0266
5.9741 70500 0.0237
6.0164 71000 0.0201
6.0588 71500 0.0166
6.1012 72000 0.0199
6.1435 72500 0.0209
6.1859 73000 0.0189
6.2283 73500 0.0202
6.2707 74000 0.0189
6.3130 74500 0.0157
6.3554 75000 0.0164
6.3978 75500 0.0179
6.4401 76000 0.0186
6.4825 76500 0.0201
6.5249 77000 0.0169
6.5672 77500 0.0201
6.6096 78000 0.0172
6.6520 78500 0.0203
6.6943 79000 0.0181
6.7367 79500 0.0178
6.7791 80000 0.0181
6.8215 80500 0.0181
6.8638 81000 0.0191
6.9062 81500 0.0162
6.9486 82000 0.0189
6.9909 82500 0.0189
7.0333 83000 0.0138
7.0757 83500 0.0152
7.1180 84000 0.0115
7.1604 84500 0.0137
7.2028 85000 0.0126
7.2451 85500 0.0137
7.2875 86000 0.0139
7.3299 86500 0.0145
7.3723 87000 0.0122
7.4146 87500 0.0146
7.4570 88000 0.0142
7.4994 88500 0.0131
7.5417 89000 0.0146
7.5841 89500 0.0137
7.6265 90000 0.0125
7.6688 90500 0.0121
7.7112 91000 0.0134
7.7536 91500 0.014
7.7959 92000 0.0116
7.8383 92500 0.0109
7.8807 93000 0.0128
7.9231 93500 0.0162
7.9654 94000 0.0138
8.0078 94500 0.014
8.0502 95000 0.0104
8.0925 95500 0.0105
8.1349 96000 0.0111
8.1773 96500 0.0099
8.2196 97000 0.0107
8.2620 97500 0.0127
8.3044 98000 0.0104
8.3468 98500 0.0112
8.3891 99000 0.0095
8.4315 99500 0.0099
8.4739 100000 0.0091
8.5162 100500 0.0096
8.5586 101000 0.0116
8.6010 101500 0.0106
8.6433 102000 0.01
8.6857 102500 0.0104
8.7281 103000 0.009
8.7704 103500 0.0089
8.8128 104000 0.0099
8.8552 104500 0.0117
8.8976 105000 0.01
8.9399 105500 0.0112
8.9823 106000 0.0103
9.0247 106500 0.0079
9.0670 107000 0.0083
9.1094 107500 0.0086
9.1518 108000 0.0084
9.1941 108500 0.0097
9.2365 109000 0.0081
9.2789 109500 0.009
9.3212 110000 0.0084
9.3636 110500 0.0072
9.4060 111000 0.0107
9.4484 111500 0.0082
9.4907 112000 0.0098
9.5331 112500 0.0089
9.5755 113000 0.0104
9.6178 113500 0.0083
9.6602 114000 0.0081
9.7026 114500 0.0087
9.7449 115000 0.0072
9.7873 115500 0.0086
9.8297 116000 0.0096
9.8720 116500 0.0087
9.9144 117000 0.0079
9.9568 117500 0.0087
9.9992 118000 0.008
10.0415 118500 0.0073
10.0839 119000 0.0058
10.1263 119500 0.0076
10.1686 120000 0.0055
10.2110 120500 0.0072
10.2534 121000 0.007
10.2957 121500 0.0075
10.3381 122000 0.0067
10.3805 122500 0.0076
10.4228 123000 0.0078
10.4652 123500 0.0073
10.5076 124000 0.0076
10.5500 124500 0.0071
10.5923 125000 0.0068
10.6347 125500 0.0062
10.6771 126000 0.0071
10.7194 126500 0.0065
10.7618 127000 0.0063
10.8042 127500 0.006
10.8465 128000 0.0055
10.8889 128500 0.0073
10.9313 129000 0.0068
10.9736 129500 0.0079
11.0160 130000 0.0056
11.0584 130500 0.0045
11.1008 131000 0.0058
11.1431 131500 0.0055
11.1855 132000 0.0062
11.2279 132500 0.0066
11.2702 133000 0.0052
11.3126 133500 0.0063
11.3550 134000 0.0059
11.3973 134500 0.0058
11.4397 135000 0.0046
11.4821 135500 0.006
11.5244 136000 0.0046
11.5668 136500 0.0059
11.6092 137000 0.0072
11.6516 137500 0.0062
11.6939 138000 0.0055
11.7363 138500 0.0055
11.7787 139000 0.0069
11.8210 139500 0.0073
11.8634 140000 0.0063
11.9058 140500 0.0067
11.9481 141000 0.0061
11.9905 141500 0.005
12.0329 142000 0.0054
12.0752 142500 0.0063
12.1176 143000 0.0046
12.1600 143500 0.0054
12.2024 144000 0.0041
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12.3295 145500 0.0046
12.3718 146000 0.0046
12.4142 146500 0.0058
12.4566 147000 0.005
12.4989 147500 0.0049
12.5413 148000 0.0053
12.5837 148500 0.0042
12.6260 149000 0.0046
12.6684 149500 0.0049
12.7108 150000 0.0042
12.7532 150500 0.0046
12.7955 151000 0.004
12.8379 151500 0.0052
12.8803 152000 0.0045
12.9226 152500 0.0048
12.9650 153000 0.0065
13.0074 153500 0.0039
13.0497 154000 0.0043
13.0921 154500 0.0039
13.1345 155000 0.0037
13.1768 155500 0.0058
13.2192 156000 0.0038
13.2616 156500 0.004
13.3040 157000 0.0044
13.3463 157500 0.0047
13.3887 158000 0.0042
13.4311 158500 0.0034
13.4734 159000 0.0056
13.5158 159500 0.0041
13.5582 160000 0.004
13.6005 160500 0.0052
13.6429 161000 0.0043
13.6853 161500 0.0039
13.7277 162000 0.0055
13.7700 162500 0.0046
13.8124 163000 0.0058
13.8548 163500 0.0037
13.8971 164000 0.0047
13.9395 164500 0.0049
13.9819 165000 0.0047
14.0242 165500 0.0042
14.0666 166000 0.0035
14.1090 166500 0.0043
14.1513 167000 0.0034
14.1937 167500 0.0032
14.2361 168000 0.0044
14.2785 168500 0.004
14.3208 169000 0.003
14.3632 169500 0.005
14.4056 170000 0.003
14.4479 170500 0.0041
14.4903 171000 0.0031
14.5327 171500 0.0033
14.5750 172000 0.0036
14.6174 172500 0.0038
14.6598 173000 0.0034
14.7021 173500 0.0034
14.7445 174000 0.0035
14.7869 174500 0.004
14.8293 175000 0.0042
14.8716 175500 0.0032
14.9140 176000 0.0029
14.9564 176500 0.004
14.9987 177000 0.0043
15.0411 177500 0.0033
15.0835 178000 0.003
15.1258 178500 0.0036
15.1682 179000 0.0035
15.2106 179500 0.0029
15.2529 180000 0.0028
15.2953 180500 0.0034
15.3377 181000 0.0024
15.3801 181500 0.0026
15.4224 182000 0.0032
15.4648 182500 0.0031
15.5072 183000 0.0038
15.5495 183500 0.0032
15.5919 184000 0.0029
15.6343 184500 0.003
15.6766 185000 0.0039
15.7190 185500 0.0034
15.7614 186000 0.0034
15.8037 186500 0.004
15.8461 187000 0.0029
15.8885 187500 0.0031
15.9309 188000 0.0025
15.9732 188500 0.0023
16.0156 189000 0.0025
16.0580 189500 0.0026
16.1003 190000 0.0028
16.1427 190500 0.003
16.1851 191000 0.0033
16.2274 191500 0.0022
16.2698 192000 0.0034
16.3122 192500 0.0029
16.3545 193000 0.0029
16.3969 193500 0.003
16.4393 194000 0.0029
16.4817 194500 0.0028
16.5240 195000 0.0026
16.5664 195500 0.003
16.6088 196000 0.0025
16.6511 196500 0.0023
16.6935 197000 0.0026
16.7359 197500 0.0031
16.7782 198000 0.0032
16.8206 198500 0.002
16.8630 199000 0.0022
16.9053 199500 0.0023
16.9477 200000 0.0027
16.9901 200500 0.0032
17.0325 201000 0.0026
17.0748 201500 0.0021
17.1172 202000 0.0028
17.1596 202500 0.0029
17.2019 203000 0.0021
17.2443 203500 0.0027
17.2867 204000 0.0023
17.3290 204500 0.0027
17.3714 205000 0.0029
17.4138 205500 0.0022
17.4561 206000 0.0026
17.4985 206500 0.0023
17.5409 207000 0.0025
17.5833 207500 0.0021
17.6256 208000 0.0022
17.6680 208500 0.0033
17.7104 209000 0.0027
17.7527 209500 0.0023
17.7951 210000 0.0026
17.8375 210500 0.0024
17.8798 211000 0.0023
17.9222 211500 0.0027
17.9646 212000 0.0037
18.0069 212500 0.0026
18.0493 213000 0.0024
18.0917 213500 0.0021
18.1341 214000 0.0022
18.1764 214500 0.0023
18.2188 215000 0.003
18.2612 215500 0.0018
18.3035 216000 0.0024
18.3459 216500 0.0031
18.3883 217000 0.0025
18.4306 217500 0.0035
18.4730 218000 0.0028
18.5154 218500 0.0027
18.5577 219000 0.002
18.6001 219500 0.0022
18.6425 220000 0.0022
18.6849 220500 0.002
18.7272 221000 0.0021
18.7696 221500 0.003
18.8120 222000 0.0023
18.8543 222500 0.0021
18.8967 223000 0.0026
18.9391 223500 0.0025
18.9814 224000 0.0031
19.0238 224500 0.0019
19.0662 225000 0.0021
19.1086 225500 0.0018
19.1509 226000 0.0019
19.1933 226500 0.0022
19.2357 227000 0.0023
19.2780 227500 0.0026
19.3204 228000 0.0029
19.3628 228500 0.0022
19.4051 229000 0.0022
19.4475 229500 0.0019
19.4899 230000 0.0019
19.5322 230500 0.0021
19.5746 231000 0.0017
19.6170 231500 0.0023
19.6594 232000 0.002
19.7017 232500 0.0023
19.7441 233000 0.0023
19.7865 233500 0.0016
19.8288 234000 0.0022
19.8712 234500 0.0018
19.9136 235000 0.002
19.9559 235500 0.0022
19.9983 236000 0.002
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Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.54.1
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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",
}

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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