iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.4_wseg-lr2e-05-1-epochs-bs-48

SentenceTransformer based on vinai/phobert-base-v2

This is a sentence-transformers model finetuned from vinai/phobert-base-v2 on the wiki-data 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: vinai/phobert-base-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • wiki-data

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': 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:

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("iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.4_wseg-lr2e-05-1-epochs-bs-48")
# Run inference
sentences = [
    'Giới_thiệu của Ommatius singlensis',
    'Ommatius singlensis là một loài ruồi trong họ Asilidae . Ommatius singlensis được Oldroyd miêu_tả năm 1972 . ( )',
    'Hoãn phát_sóng 2 tập vào ngày 8 và 9 tháng 2 để phát_sóng các chương_trình dịp Tết Giáp_Thìn 2024 của nhà đài . ( )',
]
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]

Training Details

Training Dataset

wiki-data

• Dataset: wiki-data at a37fed9
• Size: 2,129,192 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 9.83 tokens max: 24 tokens	min: 13 tokens mean: 89.06 tokens max: 256 tokens

• Samples:

  anchor	positive
  Giới_thiệu của Podascon haploopis	Podascon haploopis là một loài chân đều trong họ Podasconidae . Loài này được Giard & Bonnier miêu_tả khoa_học năm 1895 . ( Boyko , C. ( 2010 ) . Podascon haploopis Giard & Bonnier , 1895 . In : Schotte , M. , Boyko , C.B , Bruce , N.L. , Poore , G.C.B. , Taiti , S. , Wilson , G.D.F. ( Eds ) ( 2010 ) . World_Marine , Freshwater and Terrestrial_Isopod_Crustaceans database . Gebaseerd op informatie uit het Cơ_sở_dữ_liệu sinh_vật biển , te vinden op http://www.marinespecies.org/aphia.php?p=taxdetails&id=295733)
  Trung_đoàn 266 của Sư_đoàn 341 , Quân_đội nhân_dân Việt_Nam	Gọi tắt là E3 gồm các đơn_vị : - Tiểu_đoàn 7 nguyên là Tiểu_đoàn 53 bộ_đội_địa_phương tỉnh Thái_Bình . - Tiểu_đoàn 8 gồm C1 & C3 thuộc Tiểu_đoàn dự_nhiệm 210 huyện Quỳnh_Lưu , tỉnh Nghệ_An . - Tiểu_đoàn 9 gồm các đại_đội dự_nhiệm 272 , 274 và 2 đại_đội dự_nhiệm của Tiểu_đoàn 53 Quảng_Bình . - 5 đại_đội bộ_binh của E129 tỉnh_đội Hà_Tĩnh . Trung_đoàn thành_lập ngày 26/9/1973 . Gia_nhập đội_hình sư_đoàn từ tháng 9/1973 tại Quảng_Bình . Trung_đoàn trưởng : Trung_tá Trịnh_Đăng_Thế_Chính uỷ : Thượng_tá Mai_Thanh_Bình
  Giới_thiệu của Warlus , Somme	Warlus là một xã ở tỉnh Somme , vùng Hauts-de-France , Pháp .

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 48
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.4_wseg-lr2e-05-1-epochs-bs-48
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 48
• per_device_eval_batch_size: 8
• 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: 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: linear
• 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: False
• 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: True
• 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
• 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: True
• resume_from_checkpoint: None
• hub_model_id: iambestfeed/phobert-base-v2-wiki-data-filter_fasttext_0.4_wseg-lr2e-05-1-epochs-bs-48
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• 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
• 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
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0005	10	3.4297
0.0009	20	3.3577
0.0014	30	3.3025
0.0018	40	3.1419
0.0023	50	2.9045
0.0027	60	2.7514
0.0032	70	2.5959
0.0036	80	2.3981
0.0041	90	2.2227
0.0045	100	2.0816
0.0050	110	1.8545
0.0054	120	1.7712
0.0059	130	1.5548
0.0063	140	1.4381
0.0068	150	1.2668
0.0072	160	1.1221
0.0077	170	0.9117
0.0081	180	0.8491
0.0086	190	0.7116
0.0090	200	0.5499
0.0095	210	0.4855
0.0099	220	0.465
0.0104	230	0.4186
0.0108	240	0.3358
0.0113	250	0.34
0.0117	260	0.3116
0.0122	270	0.3472
0.0126	280	0.2875
0.0131	290	0.2416
0.0135	300	0.2374
0.0140	310	0.209
0.0144	320	0.2174
0.0149	330	0.2013
0.0153	340	0.2092
0.0158	350	0.169
0.0162	360	0.2011
0.0167	370	0.1779
0.0171	380	0.1604
0.0176	390	0.1839
0.0180	400	0.1724
0.0185	410	0.1651
0.0189	420	0.1458
0.0194	430	0.1503
0.0198	440	0.1843
0.0203	450	0.1832
0.0207	460	0.1434
0.0212	470	0.1687
0.0216	480	0.1338
0.0221	490	0.1171
0.0225	500	0.1166
0.0230	510	0.1409
0.0234	520	0.1145
0.0239	530	0.1409
0.0243	540	0.1202
0.0248	550	0.1091
0.0252	560	0.1496
0.0257	570	0.1157
0.0262	580	0.1344
0.0266	590	0.1176
0.0271	600	0.1066
0.0275	610	0.0734
0.0280	620	0.0838
0.0284	630	0.1262
0.0289	640	0.1111
0.0293	650	0.0809
0.0298	660	0.1006
0.0302	670	0.1265
0.0307	680	0.0762
0.0311	690	0.0935
0.0316	700	0.0835
0.0320	710	0.0887
0.0325	720	0.0891
0.0329	730	0.0756
0.0334	740	0.0855
0.0338	750	0.0828
0.0343	760	0.0846
0.0347	770	0.0909
0.0352	780	0.0716
0.0356	790	0.0922
0.0361	800	0.0947
0.0365	810	0.0699
0.0370	820	0.0875
0.0374	830	0.0845
0.0379	840	0.0778
0.0383	850	0.0818
0.0388	860	0.0503
0.0392	870	0.0782
0.0397	880	0.0459
0.0401	890	0.0867
0.0406	900	0.0683
0.0410	910	0.0946
0.0415	920	0.0531
0.0419	930	0.0479
0.0424	940	0.0808
0.0428	950	0.0779
0.0433	960	0.0797
0.0437	970	0.0757
0.0442	980	0.0592
0.0446	990	0.0714
0.0451	1000	0.0641
0.0455	1010	0.0584
0.0460	1020	0.048
0.0464	1030	0.0585
0.0469	1040	0.0834
0.0473	1050	0.0655
0.0478	1060	0.0789
0.0482	1070	0.0601
0.0487	1080	0.0648
0.0491	1090	0.0717
0.0496	1100	0.0806
0.0500	1110	0.0646
0.0505	1120	0.0831
0.0509	1130	0.0536
0.0514	1140	0.0673
0.0519	1150	0.0786
0.0523	1160	0.0698
0.0528	1170	0.0621
0.0532	1180	0.0619
0.0537	1190	0.0582
0.0541	1200	0.0579
0.0546	1210	0.0459
0.0550	1220	0.0474
0.0555	1230	0.0782
0.0559	1240	0.0436
0.0564	1250	0.0766
0.0568	1260	0.0438
0.0573	1270	0.0643
0.0577	1280	0.0476
0.0582	1290	0.0732
0.0586	1300	0.059
0.0591	1310	0.0527
0.0595	1320	0.0496
0.0600	1330	0.0495
0.0604	1340	0.0542
0.0609	1350	0.0591
0.0613	1360	0.0424
0.0618	1370	0.0525
0.0622	1380	0.0663
0.0627	1390	0.0547
0.0631	1400	0.062
0.0636	1410	0.0592
0.0640	1420	0.0569
0.0645	1430	0.0563
0.0649	1440	0.0561
0.0654	1450	0.0633
0.0658	1460	0.0563
0.0663	1470	0.0523
0.0667	1480	0.0675
0.0672	1490	0.0681
0.0676	1500	0.0437
0.0681	1510	0.0398
0.0685	1520	0.0495
0.0690	1530	0.0698
0.0694	1540	0.0527
0.0699	1550	0.0583
0.0703	1560	0.0417
0.0708	1570	0.0472
0.0712	1580	0.0555
0.0717	1590	0.0368
0.0721	1600	0.0527
0.0726	1610	0.0284
0.0730	1620	0.049
0.0735	1630	0.0509
0.0739	1640	0.0564
0.0744	1650	0.0416
0.0748	1660	0.0395
0.0753	1670	0.0498
0.0757	1680	0.0451
0.0762	1690	0.0339
0.0766	1700	0.0566
0.0771	1710	0.0554
0.0776	1720	0.058
0.0780	1730	0.0332
0.0785	1740	0.0376
0.0789	1750	0.0621
0.0794	1760	0.0498
0.0798	1770	0.034
0.0803	1780	0.0346
0.0807	1790	0.0218
0.0812	1800	0.0542
0.0816	1810	0.0503
0.0821	1820	0.0501
0.0825	1830	0.0588
0.0830	1840	0.0481
0.0834	1850	0.0389
0.0839	1860	0.0593
0.0843	1870	0.0313
0.0848	1880	0.0481
0.0852	1890	0.0436
0.0857	1900	0.0409
0.0861	1910	0.0628
0.0866	1920	0.0503
0.0870	1930	0.0486
0.0875	1940	0.0455
0.0879	1950	0.0477
0.0884	1960	0.0489
0.0888	1970	0.058
0.0893	1980	0.0357
0.0897	1990	0.0428
0.0902	2000	0.0358
0.0906	2010	0.042
0.0911	2020	0.0483
0.0915	2030	0.0493
0.0920	2040	0.0427
0.0924	2050	0.039
0.0929	2060	0.0409
0.0933	2070	0.0428
0.0938	2080	0.0415
0.0942	2090	0.0569
0.0947	2100	0.0473
0.0951	2110	0.0423
0.0956	2120	0.0571
0.0960	2130	0.0459
0.0965	2140	0.0383
0.0969	2150	0.0557
0.0974	2160	0.0433
0.0978	2170	0.041
0.0983	2180	0.035
0.0987	2190	0.035
0.0992	2200	0.0371
0.0996	2210	0.0293
0.1001	2220	0.0455
0.1005	2230	0.0342
0.1010	2240	0.0475
0.1014	2250	0.0455
0.1019	2260	0.0573
0.1023	2270	0.0443
0.1028	2280	0.0328
0.1033	2290	0.0287
0.1037	2300	0.0415
0.1042	2310	0.0294
0.1046	2320	0.0343
0.1051	2330	0.0298
0.1055	2340	0.0277
0.1060	2350	0.0346
0.1064	2360	0.041
0.1069	2370	0.0528
0.1073	2380	0.041
0.1078	2390	0.04
0.1082	2400	0.0249
0.1087	2410	0.0564
0.1091	2420	0.0478
0.1096	2430	0.0394
0.1100	2440	0.0442
0.1105	2450	0.0447
0.1109	2460	0.0347
0.1114	2470	0.0417
0.1118	2480	0.0374
0.1123	2490	0.037
0.1127	2500	0.0339
0.1132	2510	0.0458
0.1136	2520	0.048
0.1141	2530	0.0528
0.1145	2540	0.0647
0.1150	2550	0.0277
0.1154	2560	0.0515
0.1159	2570	0.0316
0.1163	2580	0.0523
0.1168	2590	0.0412
0.1172	2600	0.0294
0.1177	2610	0.0483
0.1181	2620	0.0437
0.1186	2630	0.0314
0.1190	2640	0.0369
0.1195	2650	0.0418
0.1199	2660	0.0417
0.1204	2670	0.051
0.1208	2680	0.0378
0.1213	2690	0.0329
0.1217	2700	0.0452
0.1222	2710	0.0288
0.1226	2720	0.0507
0.1231	2730	0.0347
0.1235	2740	0.0351
0.1240	2750	0.0472
0.1244	2760	0.042
0.1249	2770	0.0266
0.1253	2780	0.0357
0.1258	2790	0.0302
0.1262	2800	0.0416
0.1267	2810	0.0429
0.1271	2820	0.0351
0.1276	2830	0.0332
0.1280	2840	0.0418
0.1285	2850	0.0394
0.1290	2860	0.0275
0.1294	2870	0.0448
0.1299	2880	0.035
0.1303	2890	0.0546
0.1308	2900	0.037
0.1312	2910	0.0611
0.1317	2920	0.0515
0.1321	2930	0.0456
0.1326	2940	0.0368
0.1330	2950	0.0377
0.1335	2960	0.0212
0.1339	2970	0.0351
0.1344	2980	0.0339
0.1348	2990	0.043
0.1353	3000	0.0287
0.1357	3010	0.0363
0.1362	3020	0.0351
0.1366	3030	0.0515
0.1371	3040	0.0405
0.1375	3050	0.0326
0.1380	3060	0.0416
0.1384	3070	0.0364
0.1389	3080	0.0318
0.1393	3090	0.0291
0.1398	3100	0.0372
0.1402	3110	0.0339
0.1407	3120	0.0348
0.1411	3130	0.04
0.1416	3140	0.0515
0.1420	3150	0.0269
0.1425	3160	0.0213
0.1429	3170	0.0232
0.1434	3180	0.0402
0.1438	3190	0.0272
0.1443	3200	0.0442
0.1447	3210	0.0332
0.1452	3220	0.0362
0.1456	3230	0.0218
0.1461	3240	0.0419
0.1465	3250	0.0331
0.1470	3260	0.0259
0.1474	3270	0.0344
0.1479	3280	0.0295
0.1483	3290	0.0371
0.1488	3300	0.048
0.1492	3310	0.0334
0.1497	3320	0.0294
0.1501	3330	0.0343
0.1506	3340	0.0302
0.1510	3350	0.0364
0.1515	3360	0.03
0.1519	3370	0.0475
0.1524	3380	0.0303
0.1528	3390	0.0414
0.1533	3400	0.0251
0.1537	3410	0.0252
0.1542	3420	0.0293
0.1547	3430	0.0314
0.1551	3440	0.0313
0.1556	3450	0.0396
0.1560	3460	0.0362
0.1565	3470	0.0313
0.1569	3480	0.0433
0.1574	3490	0.0451
0.1578	3500	0.0395
0.1583	3510	0.0436
0.1587	3520	0.0396
0.1592	3530	0.0489
0.1596	3540	0.0268
0.1601	3550	0.0264
0.1605	3560	0.0265
0.1610	3570	0.0233
0.1614	3580	0.0265
0.1619	3590	0.0293
0.1623	3600	0.0378
0.1628	3610	0.0277
0.1632	3620	0.0403
0.1637	3630	0.0321
0.1641	3640	0.0268
0.1646	3650	0.023
0.1650	3660	0.0311
0.1655	3670	0.0228
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0.1970	4370	0.0199
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0.9617	21330	0.011
0.9622	21340	0.024
0.9626	21350	0.021
0.9631	21360	0.0096
0.9635	21370	0.0169
0.9640	21380	0.0186
0.9644	21390	0.0224
0.9649	21400	0.0138
0.9653	21410	0.0103
0.9658	21420	0.0086
0.9662	21430	0.006
0.9667	21440	0.0147
0.9671	21450	0.0059
0.9676	21460	0.0073
0.9680	21470	0.0066
0.9685	21480	0.0105
0.9689	21490	0.0113
0.9694	21500	0.0139
0.9698	21510	0.0162
0.9703	21520	0.015
0.9707	21530	0.0118
0.9712	21540	0.0129
0.9716	21550	0.0103
0.9721	21560	0.01
0.9725	21570	0.0153
0.9730	21580	0.017
0.9734	21590	0.0185
0.9739	21600	0.0176
0.9743	21610	0.0077
0.9748	21620	0.0077
0.9752	21630	0.0083
0.9757	21640	0.0124
0.9761	21650	0.017
0.9766	21660	0.0252
0.9771	21670	0.0127
0.9775	21680	0.02
0.9780	21690	0.0136
0.9784	21700	0.0186
0.9789	21710	0.0122
0.9793	21720	0.0146
0.9798	21730	0.0144
0.9802	21740	0.0159
0.9807	21750	0.0197
0.9811	21760	0.0152
0.9816	21770	0.0094
0.9820	21780	0.0134
0.9825	21790	0.0232
0.9829	21800	0.0039
0.9834	21810	0.0184
0.9838	21820	0.0139
0.9843	21830	0.0149
0.9847	21840	0.0147
0.9852	21850	0.026
0.9856	21860	0.0077
0.9861	21870	0.0092
0.9865	21880	0.0103
0.9870	21890	0.0196
0.9874	21900	0.017
0.9879	21910	0.009
0.9883	21920	0.0186
0.9888	21930	0.014
0.9892	21940	0.0124
0.9897	21950	0.0174
0.9901	21960	0.0166
0.9906	21970	0.0093
0.9910	21980	0.0107
0.9915	21990	0.0163
0.9919	22000	0.007
0.9924	22010	0.027
0.9928	22020	0.0093
0.9933	22030	0.0095
0.9937	22040	0.0143
0.9942	22050	0.0115
0.9946	22060	0.0104
0.9951	22070	0.0128
0.9955	22080	0.0037
0.9960	22090	0.015
0.9964	22100	0.012
0.9969	22110	0.016
0.9973	22120	0.0213
0.9978	22130	0.0104
0.9982	22140	0.0069
0.9987	22150	0.0087
0.9991	22160	0.0138
0.9996	22170	0.0153

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.47.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.3.1
• Tokenizers: 0.21.0

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}
}