ZeniZeni/trained_model

SentenceTransformer based on cl-nagoya/ruri-base

This is a sentence-transformers model finetuned from cl-nagoya/ruri-base. 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: cl-nagoya/ruri-base
• Maximum Sequence Length: 32 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

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': 32, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("ZeniZeni/trained_model")
# Run inference
sentences = [
    '聖職者の前で言うことではなかったのぅ。',
    'いえ、お気になさらず',
    'そうだ。',
]
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

Unnamed Dataset

• Size: 2,249,944 training samples
• Columns: sent1, sent2, and label
• Approximate statistics based on the first 1000 samples:

  	sent1	sent2	label
  type	string	string	int
  details	min: 4 tokens mean: 13.37 tokens max: 32 tokens	min: 3 tokens mean: 12.76 tokens max: 32 tokens	0: ~69.70% 1: ~30.30%

• Samples:

  sent1	sent2	label
  黒坂常陸がいなくなれば極北のロシアより美味しいアドリア海沿岸攻めも出来る	今、大日本合藩帝国が支配している国々を奪い取りたいのよ。	1
  きみ…	お前らより先に店にいたんだ	0
  入ってもよろしいですか！	アンナ！	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 11,363 evaluation samples
• Columns: sent1, sent2, and label
• Approximate statistics based on the first 1000 samples:

  	sent1	sent2	label
  type	string	string	int
  details	min: 4 tokens mean: 14.1 tokens max: 32 tokens	min: 3 tokens mean: 12.47 tokens max: 32 tokens	0: ~68.50% 1: ~31.50%

• Samples:

  sent1	sent2	label
  本当に生存者は…	さてな、それも分からん。	0
  それではこれより、殿と呼ばせていただきます	俺は新たな、九戸の家を興そう。	0
  くれぐれも、ご無事で…	だから…	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• num_train_epochs: 10
• remove_unused_columns: False
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• 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: 1.0
• num_train_epochs: 10
• 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: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: False
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0045	10	0.0297	-
0.0091	20	0.0268	-
0.0136	30	0.0249	-
0.0182	40	0.0242	-
0.0227	50	0.0239	-
0.0273	60	0.0239	-
0.0318	70	0.0235	-
0.0364	80	0.0235	-
0.0409	90	0.0233	-
0.0455	100	0.0233	0.024
0.0500	110	0.0233	-
0.0546	120	0.0231	-
0.0591	130	0.0229	-
0.0637	140	0.0225	-
0.0682	150	0.0229	-
0.0728	160	0.0227	-
0.0773	170	0.0228	-
0.0819	180	0.0229	-
0.0864	190	0.0223	-
0.0910	200	0.0227	0.0236
0.0955	210	0.0228	-
0.1001	220	0.0225	-
0.1046	230	0.0225	-
0.1092	240	0.0224	-
0.1137	250	0.0225	-
0.1183	260	0.0229	-
0.1228	270	0.022	-
0.1274	280	0.0226	-
0.1319	290	0.0224	-
0.1365	300	0.0222	0.0232
0.1410	310	0.0222	-
0.1456	320	0.022	-
0.1501	330	0.0223	-
0.1547	340	0.0223	-
0.1592	350	0.0222	-
0.1638	360	0.0223	-
0.1683	370	0.0219	-
0.1729	380	0.0223	-
0.1774	390	0.0219	-
0.1820	400	0.0218	0.0225
0.1865	410	0.0216	-
0.1911	420	0.0219	-
0.1956	430	0.0216	-
0.2002	440	0.022	-
0.2047	450	0.0214	-
0.2093	460	0.0219	-
0.2138	470	0.0216	-
0.2184	480	0.0215	-
0.2229	490	0.0218	-
0.2275	500	0.0214	0.0224
0.2320	510	0.0218	-
0.2366	520	0.0216	-
0.2411	530	0.0213	-
0.2457	540	0.0216	-
0.2502	550	0.0216	-
0.2548	560	0.0219	-
0.2593	570	0.0218	-
0.2639	580	0.0219	-
0.2684	590	0.022	-
0.2730	600	0.0215	0.0219
0.2775	610	0.0216	-
0.2821	620	0.0215	-
0.2866	630	0.0213	-
0.2912	640	0.0211	-
0.2957	650	0.0211	-
0.3003	660	0.0214	-
0.3048	670	0.0212	-
0.3094	680	0.0215	-
0.3139	690	0.0212	-
0.3185	700	0.0212	0.0218
0.3230	710	0.0214	-
0.3276	720	0.021	-
0.3321	730	0.0211	-
0.3367	740	0.0213	-
0.3412	750	0.0213	-
0.3458	760	0.0213	-
0.3503	770	0.0212	-
0.3549	780	0.0213	-
0.3594	790	0.0212	-
0.3640	800	0.0212	0.0220
0.3685	810	0.0216	-
0.3731	820	0.0213	-
0.3776	830	0.0216	-
0.3822	840	0.021	-
0.3867	850	0.0211	-
0.3913	860	0.0214	-
0.3958	870	0.0213	-
0.4004	880	0.0211	-
0.4049	890	0.0213	-
0.4095	900	0.0212	0.0218
0.4140	910	0.0207	-
0.4186	920	0.0209	-
0.4231	930	0.0208	-
0.4277	940	0.0206	-
0.4322	950	0.021	-
0.4368	960	0.0214	-
0.4413	970	0.021	-
0.4459	980	0.0212	-
0.4504	990	0.0211	-
0.4550	1000	0.0209	0.0214
0.4595	1010	0.0209	-
0.4641	1020	0.0206	-
0.4686	1030	0.0204	-
0.4732	1040	0.021	-
0.4777	1050	0.0202	-
0.4823	1060	0.0203	-
0.4868	1070	0.0211	-
0.4914	1080	0.0208	-
0.4959	1090	0.021	-
0.5005	1100	0.0208	0.0214
0.5050	1110	0.0206	-
0.5096	1120	0.0207	-
0.5141	1130	0.0209	-
0.5187	1140	0.0203	-
0.5232	1150	0.0206	-
0.5278	1160	0.0206	-
0.5323	1170	0.0208	-
0.5369	1180	0.0206	-
0.5414	1190	0.0204	-
0.5460	1200	0.021	0.0213
0.5505	1210	0.0206	-
0.5551	1220	0.0206	-
0.5596	1230	0.0208	-
0.5641	1240	0.0213	-
0.5687	1250	0.0206	-
0.5732	1260	0.0211	-
0.5778	1270	0.021	-
0.5823	1280	0.0208	-
0.5869	1290	0.0207	-
0.5914	1300	0.0204	0.0213
0.5960	1310	0.0202	-
0.6005	1320	0.0207	-
0.6051	1330	0.0209	-
0.6096	1340	0.02	-
0.6142	1350	0.0204	-
0.6187	1360	0.0205	-
0.6233	1370	0.0201	-
0.6278	1380	0.0202	-
0.6324	1390	0.021	-
0.6369	1400	0.0202	0.0210
0.6415	1410	0.0205	-
0.6460	1420	0.0205	-
0.6506	1430	0.0208	-
0.6551	1440	0.0205	-
0.6597	1450	0.0209	-
0.6642	1460	0.0205	-
0.6688	1470	0.0204	-
0.6733	1480	0.0207	-
0.6779	1490	0.0199	-
0.6824	1500	0.0205	0.0208
0.6870	1510	0.0201	-
0.6915	1520	0.0205	-
0.6961	1530	0.0205	-
0.7006	1540	0.0209	-
0.7052	1550	0.0206	-
0.7097	1560	0.0205	-
0.7143	1570	0.0205	-
0.7188	1580	0.0203	-
0.7234	1590	0.0199	-
0.7279	1600	0.0205	0.0208
0.7325	1610	0.0201	-
0.7370	1620	0.0202	-
0.7416	1630	0.0206	-
0.7461	1640	0.0207	-
0.7507	1650	0.0203	-
0.7552	1660	0.02	-
0.7598	1670	0.0204	-
0.7643	1680	0.0205	-
0.7689	1690	0.0199	-
0.7734	1700	0.0206	0.0206
0.7780	1710	0.0204	-
0.7825	1720	0.0205	-
0.7871	1730	0.0202	-
0.7916	1740	0.0204	-
0.7962	1750	0.0205	-
0.8007	1760	0.0202	-
0.8053	1770	0.0202	-
0.8098	1780	0.02	-
0.8144	1790	0.0203	-
0.8189	1800	0.0206	0.0205
0.8235	1810	0.0201	-
0.8280	1820	0.0207	-
0.8326	1830	0.02	-
0.8371	1840	0.0201	-
0.8417	1850	0.0205	-
0.8462	1860	0.02	-
0.8508	1870	0.0201	-
0.8553	1880	0.0199	-
0.8599	1890	0.0198	-
0.8644	1900	0.0197	0.0205
0.8690	1910	0.0201	-
0.8735	1920	0.0198	-
0.8781	1930	0.02	-
0.8826	1940	0.0198	-
0.8872	1950	0.0196	-
0.8917	1960	0.02	-
0.8963	1970	0.0204	-
0.9008	1980	0.0199	-
0.9054	1990	0.02	-
0.9099	2000	0.0199	0.0204
0.9145	2010	0.0204	-
0.9190	2020	0.0202	-
0.9236	2030	0.02	-
0.9281	2040	0.0198	-
0.9327	2050	0.0198	-
0.9372	2060	0.0197	-
0.9418	2070	0.0198	-
0.9463	2080	0.0202	-
0.9509	2090	0.0198	-
0.9554	2100	0.0201	0.0203
0.9600	2110	0.0201	-
0.9645	2120	0.02	-
0.9691	2130	0.0196	-
0.9736	2140	0.0201	-
0.9782	2150	0.0199	-
0.9827	2160	0.0198	-
0.9873	2170	0.02	-
0.9918	2180	0.0199	-
0.9964	2190	0.0194	-
1.0009	2200	0.0195	0.0206
1.0055	2210	0.0191	-
1.0100	2220	0.019	-
1.0146	2230	0.019	-
1.0191	2240	0.0189	-
1.0237	2250	0.019	-
1.0282	2260	0.0192	-
1.0328	2270	0.0193	-
1.0373	2280	0.0194	-
1.0419	2290	0.0184	-
1.0464	2300	0.0183	0.0201
1.0510	2310	0.0191	-
1.0555	2320	0.0189	-
1.0601	2330	0.0185	-
1.0646	2340	0.0186	-
1.0692	2350	0.0187	-
1.0737	2360	0.0196	-
1.0783	2370	0.0189	-
1.0828	2380	0.0188	-
1.0874	2390	0.0186	-
1.0919	2400	0.0191	0.0200
1.0965	2410	0.0188	-
1.1010	2420	0.0193	-
1.1056	2430	0.019	-
1.1101	2440	0.0187	-
1.1146	2450	0.0187	-
1.1192	2460	0.0192	-
1.1237	2470	0.0188	-
1.1283	2480	0.0188	-
1.1328	2490	0.0187	-
1.1374	2500	0.0183	0.0201
1.1419	2510	0.0185	-
1.1465	2520	0.0188	-
1.1510	2530	0.019	-
1.1556	2540	0.0186	-
1.1601	2550	0.0187	-
1.1647	2560	0.0185	-
1.1692	2570	0.0186	-
1.1738	2580	0.0188	-
1.1783	2590	0.0182	-
1.1829	2600	0.0185	0.0200
1.1874	2610	0.0186	-
1.1920	2620	0.0192	-
1.1965	2630	0.0186	-
1.2011	2640	0.0188	-
1.2056	2650	0.0185	-
1.2102	2660	0.0188	-
1.2147	2670	0.0188	-
1.2193	2680	0.0184	-
1.2238	2690	0.0186	-
1.2284	2700	0.0191	0.0199
1.2329	2710	0.0189	-
1.2375	2720	0.0191	-
1.2420	2730	0.0186	-
1.2466	2740	0.0183	-
1.2511	2750	0.0189	-
1.2557	2760	0.019	-
1.2602	2770	0.019	-
1.2648	2780	0.0183	-
1.2693	2790	0.0184	-
1.2739	2800	0.0186	0.0198
1.2784	2810	0.0193	-
1.2830	2820	0.0187	-
1.2875	2830	0.019	-
1.2921	2840	0.0189	-
1.2966	2850	0.0189	-
1.3012	2860	0.0187	-
1.3057	2870	0.0187	-
1.3103	2880	0.0186	-
1.3148	2890	0.0187	-
1.3194	2900	0.0187	0.0197
1.3239	2910	0.0185	-
1.3285	2920	0.0187	-
1.3330	2930	0.0187	-
1.3376	2940	0.018	-
1.3421	2950	0.0188	-
1.3467	2960	0.0184	-
1.3512	2970	0.0183	-
1.3558	2980	0.0185	-
1.3603	2990	0.0181	-
1.3649	3000	0.0186	0.0196
1.3694	3010	0.0184	-
1.3740	3020	0.0184	-
1.3785	3030	0.0187	-
1.3831	3040	0.0184	-
1.3876	3050	0.0185	-
1.3922	3060	0.0184	-
1.3967	3070	0.0188	-
1.4013	3080	0.0185	-
1.4058	3090	0.0185	-
1.4104	3100	0.0187	0.0196
1.4149	3110	0.0191	-
1.4195	3120	0.0185	-
1.4240	3130	0.0184	-
1.4286	3140	0.0187	-
1.4331	3150	0.0187	-
1.4377	3160	0.0182	-
1.4422	3170	0.0182	-
1.4468	3180	0.0184	-
1.4513	3190	0.0183	-
1.4559	3200	0.0182	0.0196
1.4604	3210	0.0183	-
1.4650	3220	0.0189	-
1.4695	3230	0.0188	-
1.4741	3240	0.0185	-
1.4786	3250	0.0184	-
1.4832	3260	0.0186	-
1.4877	3270	0.018	-
1.4923	3280	0.0183	-
1.4968	3290	0.019	-
1.5014	3300	0.018	0.0196
1.5059	3310	0.0181	-
1.5105	3320	0.0188	-
1.5150	3330	0.0182	-
1.5196	3340	0.0182	-
1.5241	3350	0.0184	-
1.5287	3360	0.0185	-
1.5332	3370	0.018	-
1.5378	3380	0.0179	-
1.5423	3390	0.0186	-
1.5469	3400	0.0178	0.0194
1.5514	3410	0.018	-
1.5560	3420	0.0183	-
1.5605	3430	0.0179	-
1.5651	3440	0.018	-
1.5696	3450	0.0181	-
1.5742	3460	0.0182	-
1.5787	3470	0.0186	-
1.5833	3480	0.0183	-
1.5878	3490	0.0186	-
1.5924	3500	0.0181	0.0193
1.5969	3510	0.0185	-
1.6015	3520	0.0178	-
1.6060	3530	0.0182	-
1.6106	3540	0.0186	-
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1.6197	3560	0.0183	-
1.6242	3570	0.0181	-
1.6288	3580	0.0183	-
1.6333	3590	0.0179	-
1.6379	3600	0.0187	0.0193
1.6424	3610	0.018	-
1.6470	3620	0.0182	-
1.6515	3630	0.0184	-
1.6561	3640	0.018	-
1.6606	3650	0.018	-
1.6652	3660	0.0181	-
1.6697	3670	0.0179	-
1.6742	3680	0.0181	-
1.6788	3690	0.0185	-
1.6833	3700	0.0179	0.0193
1.6879	3710	0.0186	-
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1.6970	3730	0.0183	-
1.7015	3740	0.018	-
1.7061	3750	0.0186	-
1.7106	3760	0.018	-
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1.7288	3800	0.0179	0.0191
1.7334	3810	0.018	-
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1.7425	3830	0.0179	-
1.7470	3840	0.0179	-
1.7516	3850	0.0178	-
1.7561	3860	0.0174	-
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1.7743	3900	0.0179	0.0191
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1.8198	4000	0.0179	0.0189
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1.8926	4160	0.018	-
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1.9063	4190	0.0179	-
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1.9427	4270	0.0187	-
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1.9518	4290	0.0174	-
1.9563	4300	0.0176	0.0186
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1.9700	4330	0.018	-
1.9745	4340	0.0179	-
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1.9836	4360	0.0179	-
1.9882	4370	0.018	-
1.9927	4380	0.0179	-
1.9973	4390	0.0178	-
2.0018	4400	0.0175	0.0185
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2.0382	4480	0.0161	-
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2.0473	4500	0.0164	0.0184
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2.1110	4640	0.0161	-
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2.1292	4680	0.016	-
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2.1838	4800	0.0161	0.0183
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2.2202	4880	0.016	-
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2.2293	4900	0.0161	0.0181
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2.2702	4990	0.0163	-
2.2748	5000	0.0161	0.0182
2.2793	5010	0.0163	-
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2.2884	5030	0.016	-
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2.2975	5050	0.0161	-
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2.3112	5080	0.0163	-
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2.3203	5100	0.0161	0.0180
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2.3703	5210	0.0164	-
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2.3794	5230	0.016	-
2.3840	5240	0.0159	-
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2.4022	5280	0.0163	-
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2.4431	5370	0.016	-
2.4477	5380	0.0162	-
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2.4568	5400	0.0162	0.0180
2.4613	5410	0.0159	-
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2.4841	5460	0.016	-
2.4886	5470	0.016	-
2.4932	5480	0.0164	-
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2.5023	5500	0.016	0.0180
2.5068	5510	0.0161	-
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2.5205	5540	0.0166	-
2.5250	5550	0.0163	-
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2.5705	5650	0.0162	-
2.5751	5660	0.0159	-
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2.5887	5690	0.0158	-
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2.5978	5710	0.0161	-
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2.6297	5780	0.016	-
2.6342	5790	0.0156	-
2.6388	5800	0.0161	0.0175
2.6433	5810	0.0164	-
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2.6615	5850	0.0165	-
2.6661	5860	0.016	-
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2.6843	5900	0.0159	0.0176
2.6888	5910	0.0156	-
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2.7025	5940	0.0163	-
2.7070	5950	0.0159	-
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2.7298	6000	0.016	0.0174
2.7343	6010	0.0161	-
2.7389	6020	0.0159	-
2.7434	6030	0.0161	-
2.7480	6040	0.016	-
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2.7571	6060	0.0159	-
2.7616	6070	0.0162	-
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2.7753	6100	0.0159	0.0176
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2.7934	6140	0.0156	-
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2.8025	6160	0.016	-
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2.8116	6180	0.0156	-
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2.8207	6200	0.016	0.0174
2.8253	6210	0.0159	-
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2.8435	6250	0.0156	-
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2.8526	6270	0.016	-
2.8571	6280	0.0156	-
2.8617	6290	0.0154	-
2.8662	6300	0.016	0.0173
2.8708	6310	0.016	-
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2.8799	6330	0.0158	-
2.8844	6340	0.0156	-
2.8890	6350	0.0161	-
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2.9026	6380	0.0157	-
2.9072	6390	0.0158	-
2.9117	6400	0.016	0.0174
2.9163	6410	0.0157	-
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2.9299	6440	0.0156	-
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2.9436	6470	0.0156	-
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2.9527	6490	0.0155	-
2.9572	6500	0.0159	0.0172
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2.9800	6550	0.0156	-
2.9845	6560	0.0153	-
2.9891	6570	0.0159	-
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2.9982	6590	0.0157	-
3.0027	6600	0.0149	0.0173
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3.0118	6620	0.0143	-
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3.0209	6640	0.0139	-
3.0255	6650	0.0145	-
3.0300	6660	0.0145	-
3.0346	6670	0.0142	-
3.0391	6680	0.0141	-
3.0437	6690	0.0139	-
3.0482	6700	0.0143	0.0173
3.0528	6710	0.0144	-
3.0573	6720	0.0145	-
3.0619	6730	0.014	-
3.0664	6740	0.0144	-
3.0710	6750	0.0141	-
3.0755	6760	0.0141	-
3.0801	6770	0.0145	-
3.0846	6780	0.014	-
3.0892	6790	0.0144	-
3.0937	6800	0.014	0.0174
3.0983	6810	0.0145	-
3.1028	6820	0.0141	-
3.1074	6830	0.0142	-
3.1119	6840	0.0138	-
3.1165	6850	0.0142	-
3.1210	6860	0.0143	-
3.1256	6870	0.0144	-
3.1301	6880	0.0143	-
3.1347	6890	0.0145	-
3.1392	6900	0.0144	0.0171
3.1438	6910	0.014	-
3.1483	6920	0.0142	-
3.1529	6930	0.0144	-
3.1574	6940	0.0141	-
3.1620	6950	0.0142	-
3.1665	6960	0.0145	-
3.1711	6970	0.0139	-
3.1756	6980	0.0145	-
3.1802	6990	0.0145	-
3.1847	7000	0.0137	0.0170
3.1893	7010	0.0144	-
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3.1984	7030	0.0139	-
3.2029	7040	0.014	-
3.2075	7050	0.0147	-
3.2120	7060	0.0141	-
3.2166	7070	0.014	-
3.2211	7080	0.0146	-
3.2257	7090	0.0143	-
3.2302	7100	0.0145	0.0169
3.2348	7110	0.0145	-
3.2393	7120	0.0141	-
3.2439	7130	0.014	-
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3.2530	7150	0.014	-
3.2575	7160	0.0143	-
3.2621	7170	0.0145	-
3.2666	7180	0.0143	-
3.2712	7190	0.0142	-
3.2757	7200	0.0144	0.0168
3.2803	7210	0.0141	-
3.2848	7220	0.014	-
3.2894	7230	0.0142	-
3.2939	7240	0.0144	-
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3.3030	7260	0.0143	-
3.3076	7270	0.0141	-
3.3121	7280	0.014	-
3.3167	7290	0.0136	-
3.3212	7300	0.0144	0.0167
3.3258	7310	0.014	-
3.3303	7320	0.014	-
3.3348	7330	0.0143	-
3.3394	7340	0.0142	-
3.3439	7350	0.0141	-
3.3485	7360	0.0142	-
3.3530	7370	0.0142	-
3.3576	7380	0.0142	-
3.3621	7390	0.0142	-
3.3667	7400	0.0143	0.0168
3.3712	7410	0.0142	-
3.3758	7420	0.0141	-
3.3803	7430	0.0142	-
3.3849	7440	0.0141	-
3.3894	7450	0.0142	-
3.3940	7460	0.0142	-
3.3985	7470	0.0143	-
3.4031	7480	0.0139	-
3.4076	7490	0.0141	-
3.4122	7500	0.0142	0.0166
3.4167	7510	0.014	-
3.4213	7520	0.0143	-
3.4258	7530	0.0139	-
3.4304	7540	0.0141	-
3.4349	7550	0.0145	-
3.4395	7560	0.0141	-
3.4440	7570	0.0144	-
3.4486	7580	0.0141	-
3.4531	7590	0.0139	-
3.4577	7600	0.0139	0.0166
3.4622	7610	0.014	-
3.4668	7620	0.0139	-
3.4713	7630	0.0144	-
3.4759	7640	0.0141	-
3.4804	7650	0.0145	-
3.4850	7660	0.0142	-
3.4895	7670	0.0142	-
3.4941	7680	0.0141	-
3.4986	7690	0.014	-
3.5032	7700	0.0138	0.0166
3.5077	7710	0.014	-
3.5123	7720	0.0147	-
3.5168	7730	0.0143	-
3.5214	7740	0.0136	-
3.5259	7750	0.0141	-
3.5305	7760	0.0144	-
3.5350	7770	0.0143	-
3.5396	7780	0.0145	-
3.5441	7790	0.0141	-
3.5487	7800	0.0138	0.0164
3.5532	7810	0.0145	-
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3.5669	7840	0.0142	-
3.5714	7850	0.0141	-
3.5760	7860	0.0138	-
3.5805	7870	0.0141	-
3.5851	7880	0.0137	-
3.5896	7890	0.0143	-
3.5942	7900	0.0139	0.0165
3.5987	7910	0.0141	-
3.6033	7920	0.0143	-
3.6078	7930	0.0141	-
3.6124	7940	0.0144	-
3.6169	7950	0.0141	-
3.6215	7960	0.0144	-
3.6260	7970	0.0137	-
3.6306	7980	0.0139	-
3.6351	7990	0.014	-
3.6397	8000	0.0142	0.0164
3.6442	8010	0.0142	-
3.6488	8020	0.0138	-
3.6533	8030	0.0138	-
3.6579	8040	0.0136	-
3.6624	8050	0.0138	-
3.6670	8060	0.0143	-
3.6715	8070	0.0142	-
3.6761	8080	0.014	-
3.6806	8090	0.0141	-
3.6852	8100	0.0139	0.0164
3.6897	8110	0.014	-
3.6943	8120	0.0146	-
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3.7034	8140	0.0138	-
3.7079	8150	0.0139	-
3.7125	8160	0.0148	-
3.7170	8170	0.0143	-
3.7216	8180	0.014	-
3.7261	8190	0.0138	-
3.7307	8200	0.0137	0.0163
3.7352	8210	0.014	-
3.7398	8220	0.0144	-
3.7443	8230	0.0141	-
3.7489	8240	0.0144	-
3.7534	8250	0.0144	-
3.7580	8260	0.0141	-
3.7625	8270	0.0138	-
3.7671	8280	0.014	-
3.7716	8290	0.0138	-
3.7762	8300	0.0141	0.0161
3.7807	8310	0.0141	-
3.7853	8320	0.0141	-
3.7898	8330	0.0141	-
3.7944	8340	0.0143	-
3.7989	8350	0.0137	-
3.8035	8360	0.014	-
3.8080	8370	0.0142	-
3.8126	8380	0.0146	-
3.8171	8390	0.0139	-
3.8217	8400	0.0139	0.0160
3.8262	8410	0.0144	-
3.8308	8420	0.014	-
3.8353	8430	0.0141	-
3.8399	8440	0.0144	-
3.8444	8450	0.014	-
3.8490	8460	0.0144	-
3.8535	8470	0.0139	-
3.8581	8480	0.014	-
3.8626	8490	0.0138	-
3.8672	8500	0.0136	0.0160
3.8717	8510	0.014	-
3.8763	8520	0.0144	-
3.8808	8530	0.0139	-
3.8854	8540	0.0137	-
3.8899	8550	0.0139	-
3.8944	8560	0.0142	-
3.8990	8570	0.0139	-
3.9035	8580	0.0137	-
3.9081	8590	0.0137	-
3.9126	8600	0.0139	0.0160
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3.9217	8620	0.0135	-
3.9263	8630	0.014	-
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3.9399	8660	0.0137	-
3.9445	8670	0.0141	-
3.9490	8680	0.014	-
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3.9581	8700	0.0144	0.0161
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4.0036	8800	0.0129	0.0162
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4.0673	8940	0.0123	-
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4.0764	8960	0.0125	-
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4.0946	9000	0.0134	0.0159
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4.1401	9100	0.0128	0.0161
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4.1902	9210	0.0123	-
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4.2766	9400	0.0128	0.0160
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4.3221	9500	0.0125	0.0158
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4.3540	9570	0.0123	-
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4.3676	9600	0.0125	0.0158
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4.3995	9670	0.0123	-
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4.4586	9800	0.0124	0.0158
4.4631	9810	0.0125	-
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4.5041	9900	0.0125	0.0156
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4.5496	10000	0.0128	0.0157
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4.5769	10060	0.0123	-
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4.6224	10160	0.0123	-
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5.0	10990	0.0123	-
5.0045	11000	0.0115	0.0153
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5.1410	11300	0.0114	0.0151
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5.1683	11360	0.0115	-
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5.3048	11660	0.0115	-
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5.3276	11710	0.0115	-
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5.4595	12000	0.0115	0.0149
5.4641	12010	0.0113	-
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5.5005	12090	0.0115	-
5.5050	12100	0.0115	0.0149
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5.5869	12280	0.0115	-
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5.5960	12300	0.0114	0.0148
5.6005	12310	0.0115	-
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5.6415	12400	0.0112	0.0149
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5.6870	12500	0.0113	0.0148
5.6915	12510	0.0115	-
5.6961	12520	0.0112	-
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5.7143	12560	0.0115	-
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5.7325	12600	0.0115	0.0147
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5.7780	12700	0.0117	0.0146
5.7825	12710	0.0114	-
5.7871	12720	0.0115	-
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5.7962	12740	0.0111	-
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5.8189	12790	0.0115	-
5.8235	12800	0.0114	0.0145
5.8280	12810	0.0117	-
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5.8599	12880	0.0112	-
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5.8690	12900	0.0116	0.0144
5.8735	12910	0.0114	-
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5.8917	12950	0.0114	-
5.8963	12960	0.0117	-
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5.9099	12990	0.0113	-
5.9145	13000	0.0113	0.0144
5.9190	13010	0.0115	-
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5.9418	13060	0.0115	-
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5.9554	13090	0.0114	-
5.9600	13100	0.0116	0.0145
5.9645	13110	0.0114	-
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5.9827	13150	0.0115	-
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5.9964	13180	0.0114	-
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6.0373	13270	0.0103	-
6.0419	13280	0.0106	-
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6.0510	13300	0.0104	0.0145
6.0555	13310	0.0106	-
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6.0965	13400	0.0099	0.0146
6.1010	13410	0.0102	-
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6.1237	13460	0.0104	-
6.1283	13470	0.0103	-
6.1328	13480	0.0107	-
6.1374	13490	0.0105	-
6.1419	13500	0.0102	0.0143
6.1465	13510	0.0102	-
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6.1692	13560	0.01	-
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6.1874	13600	0.0104	0.0145
6.1920	13610	0.0106	-
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6.2011	13630	0.0103	-
6.2056	13640	0.0104	-
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8.4986	18680	0.0088	-
8.5032	18690	0.0094	-
8.5077	18700	0.0094	0.0135
8.5123	18710	0.0092	-
8.5168	18720	0.0094	-
8.5214	18730	0.0094	-
8.5259	18740	0.0089	-
8.5305	18750	0.0094	-
8.5350	18760	0.0093	-
8.5396	18770	0.0093	-
8.5441	18780	0.0091	-
8.5487	18790	0.009	-
8.5532	18800	0.0089	0.0135
8.5578	18810	0.0087	-
8.5623	18820	0.009	-
8.5669	18830	0.009	-
8.5714	18840	0.009	-
8.5760	18850	0.0091	-
8.5805	18860	0.0091	-
8.5851	18870	0.0089	-
8.5896	18880	0.0095	-
8.5942	18890	0.0092	-
8.5987	18900	0.0093	0.0135
8.6033	18910	0.0095	-
8.6078	18920	0.0095	-
8.6124	18930	0.0094	-
8.6169	18940	0.0093	-
8.6215	18950	0.0089	-
8.6260	18960	0.0089	-
8.6306	18970	0.0094	-
8.6351	18980	0.0096	-
8.6397	18990	0.0092	-
8.6442	19000	0.0091	0.0136
8.6488	19010	0.0092	-
8.6533	19020	0.0091	-
8.6579	19030	0.0089	-
8.6624	19040	0.0088	-
8.6670	19050	0.009	-
8.6715	19060	0.0093	-
8.6761	19070	0.0092	-
8.6806	19080	0.0091	-
8.6852	19090	0.0091	-
8.6897	19100	0.0092	0.0135
8.6943	19110	0.0089	-
8.6988	19120	0.0091	-
8.7034	19130	0.0092	-
8.7079	19140	0.009	-
8.7125	19150	0.009	-
8.7170	19160	0.0093	-
8.7216	19170	0.0089	-
8.7261	19180	0.0091	-
8.7307	19190	0.009	-
8.7352	19200	0.0093	0.0135
8.7398	19210	0.0091	-
8.7443	19220	0.0096	-
8.7489	19230	0.009	-
8.7534	19240	0.0091	-
8.7580	19250	0.009	-
8.7625	19260	0.0094	-
8.7671	19270	0.0087	-
8.7716	19280	0.0093	-
8.7762	19290	0.0086	-
8.7807	19300	0.0092	0.0135
8.7853	19310	0.0092	-
8.7898	19320	0.0091	-
8.7944	19330	0.0091	-
8.7989	19340	0.0089	-
8.8035	19350	0.0092	-
8.8080	19360	0.0091	-
8.8126	19370	0.0088	-
8.8171	19380	0.0096	-
8.8217	19390	0.0089	-
8.8262	19400	0.0091	0.0134
8.8308	19410	0.0093	-
8.8353	19420	0.0091	-
8.8399	19430	0.0092	-
8.8444	19440	0.009	-
8.8490	19450	0.0091	-
8.8535	19460	0.0089	-
8.8581	19470	0.0089	-
8.8626	19480	0.0093	-
8.8672	19490	0.0092	-
8.8717	19500	0.0091	0.0135
8.8763	19510	0.0092	-
8.8808	19520	0.009	-
8.8854	19530	0.0092	-
8.8899	19540	0.009	-
8.8944	19550	0.0089	-
8.8990	19560	0.0092	-
8.9035	19570	0.0089	-
8.9081	19580	0.0094	-
8.9126	19590	0.009	-
8.9172	19600	0.009	0.0134
8.9217	19610	0.0089	-
8.9263	19620	0.0094	-
8.9308	19630	0.0092	-
8.9354	19640	0.0093	-
8.9399	19650	0.0087	-
8.9445	19660	0.0091	-
8.9490	19670	0.0088	-
8.9536	19680	0.0093	-
8.9581	19690	0.0091	-
8.9627	19700	0.0093	0.0134
8.9672	19710	0.0089	-
8.9718	19720	0.0094	-
8.9763	19730	0.009	-
8.9809	19740	0.0091	-
8.9854	19750	0.009	-
8.9900	19760	0.0089	-
8.9945	19770	0.0089	-
8.9991	19780	0.0093	-
9.0036	19790	0.0089	-
9.0082	19800	0.0089	0.0133
9.0127	19810	0.009	-
9.0173	19820	0.009	-
9.0218	19830	0.0086	-
9.0264	19840	0.0089	-
9.0309	19850	0.0091	-
9.0355	19860	0.0089	-
9.0400	19870	0.0085	-
9.0446	19880	0.0085	-
9.0491	19890	0.0088	-
9.0537	19900	0.0083	0.0135
9.0582	19910	0.0089	-
9.0628	19920	0.0087	-
9.0673	19930	0.0089	-
9.0719	19940	0.0086	-
9.0764	19950	0.0088	-
9.0810	19960	0.0089	-
9.0855	19970	0.0089	-
9.0901	19980	0.0086	-
9.0946	19990	0.0087	-
9.0992	20000	0.0091	0.0134
9.1037	20010	0.0086	-
9.1083	20020	0.0083	-
9.1128	20030	0.0086	-
9.1174	20040	0.0087	-
9.1219	20050	0.0089	-
9.1265	20060	0.0085	-
9.1310	20070	0.0089	-
9.1356	20080	0.0087	-
9.1401	20090	0.0088	-
9.1447	20100	0.0087	0.0134
9.1492	20110	0.0089	-
9.1538	20120	0.0088	-
9.1583	20130	0.0089	-
9.1629	20140	0.0083	-
9.1674	20150	0.0092	-
9.1720	20160	0.0089	-
9.1765	20170	0.0087	-
9.1811	20180	0.0085	-
9.1856	20190	0.0089	-
9.1902	20200	0.0087	0.0134
9.1947	20210	0.0088	-
9.1993	20220	0.0088	-
9.2038	20230	0.0084	-
9.2084	20240	0.0088	-
9.2129	20250	0.0087	-
9.2175	20260	0.009	-
9.2220	20270	0.0087	-
9.2266	20280	0.0087	-
9.2311	20290	0.0087	-
9.2357	20300	0.0086	0.0135
9.2402	20310	0.0086	-
9.2448	20320	0.0088	-
9.2493	20330	0.0085	-
9.2539	20340	0.0088	-
9.2584	20350	0.0089	-
9.2630	20360	0.0088	-
9.2675	20370	0.009	-
9.2721	20380	0.0087	-
9.2766	20390	0.0086	-
9.2812	20400	0.0089	0.0134
9.2857	20410	0.0083	-
9.2903	20420	0.0086	-
9.2948	20430	0.0088	-
9.2994	20440	0.0084	-
9.3039	20450	0.0085	-
9.3085	20460	0.0086	-
9.3130	20470	0.0089	-
9.3176	20480	0.0089	-
9.3221	20490	0.0089	-
9.3267	20500	0.0088	0.0134
9.3312	20510	0.0088	-
9.3358	20520	0.0086	-
9.3403	20530	0.0088	-
9.3449	20540	0.0086	-
9.3494	20550	0.0087	-
9.3540	20560	0.0089	-
9.3585	20570	0.009	-
9.3631	20580	0.0089	-
9.3676	20590	0.0086	-
9.3722	20600	0.0085	0.0133
9.3767	20610	0.0089	-
9.3813	20620	0.0086	-
9.3858	20630	0.0086	-
9.3904	20640	0.0088	-
9.3949	20650	0.0087	-
9.3995	20660	0.0086	-
9.4040	20670	0.0087	-
9.4086	20680	0.0086	-
9.4131	20690	0.0086	-
9.4177	20700	0.009	0.0134
9.4222	20710	0.0088	-
9.4268	20720	0.0085	-
9.4313	20730	0.0089	-
9.4359	20740	0.0084	-
9.4404	20750	0.0085	-
9.4449	20760	0.0088	-
9.4495	20770	0.0086	-
9.4540	20780	0.0085	-
9.4586	20790	0.0089	-
9.4631	20800	0.0086	0.0133
9.4677	20810	0.0087	-
9.4722	20820	0.0083	-
9.4768	20830	0.0089	-
9.4813	20840	0.0087	-
9.4859	20850	0.0084	-
9.4904	20860	0.0083	-
9.4950	20870	0.0086	-
9.4995	20880	0.0087	-
9.5041	20890	0.0088	-
9.5086	20900	0.0084	0.0134
9.5132	20910	0.0085	-
9.5177	20920	0.0087	-
9.5223	20930	0.009	-
9.5268	20940	0.0089	-
9.5314	20950	0.0085	-
9.5359	20960	0.0085	-
9.5405	20970	0.0085	-
9.5450	20980	0.0087	-
9.5496	20990	0.0084	-
9.5541	21000	0.0089	0.0133
9.5587	21010	0.0088	-
9.5632	21020	0.0083	-
9.5678	21030	0.0088	-
9.5723	21040	0.0087	-
9.5769	21050	0.0085	-
9.5814	21060	0.0086	-
9.5860	21070	0.0087	-
9.5905	21080	0.0089	-
9.5951	21090	0.0086	-
9.5996	21100	0.0087	0.0133
9.6042	21110	0.009	-
9.6087	21120	0.0091	-
9.6133	21130	0.0092	-
9.6178	21140	0.0088	-
9.6224	21150	0.0085	-
9.6269	21160	0.0087	-
9.6315	21170	0.0086	-
9.6360	21180	0.0089	-
9.6406	21190	0.0086	-
9.6451	21200	0.0084	0.0133
9.6497	21210	0.0091	-
9.6542	21220	0.0088	-
9.6588	21230	0.0091	-
9.6633	21240	0.0089	-
9.6679	21250	0.0086	-
9.6724	21260	0.0087	-
9.6770	21270	0.0086	-
9.6815	21280	0.0089	-
9.6861	21290	0.0084	-
9.6906	21300	0.0087	0.0134
9.6952	21310	0.0084	-
9.6997	21320	0.0087	-
9.7043	21330	0.0087	-
9.7088	21340	0.0085	-
9.7134	21350	0.0089	-
9.7179	21360	0.0089	-
9.7225	21370	0.0084	-
9.7270	21380	0.0088	-
9.7316	21390	0.0086	-
9.7361	21400	0.0087	0.0134
9.7407	21410	0.0086	-
9.7452	21420	0.0089	-
9.7498	21430	0.0087	-
9.7543	21440	0.0083	-
9.7589	21450	0.0086	-
9.7634	21460	0.0085	-
9.7680	21470	0.0084	-
9.7725	21480	0.009	-
9.7771	21490	0.0086	-
9.7816	21500	0.0086	0.0134
9.7862	21510	0.0088	-
9.7907	21520	0.0085	-
9.7953	21530	0.0088	-
9.7998	21540	0.0087	-
9.8044	21550	0.0085	-
9.8089	21560	0.0085	-
9.8135	21570	0.0086	-
9.8180	21580	0.0085	-
9.8226	21590	0.0089	-
9.8271	21600	0.0083	0.0133
9.8317	21610	0.0085	-
9.8362	21620	0.0088	-
9.8408	21630	0.0087	-
9.8453	21640	0.0086	-
9.8499	21650	0.0089	-
9.8544	21660	0.0085	-
9.8590	21670	0.009	-
9.8635	21680	0.0092	-
9.8681	21690	0.009	-
9.8726	21700	0.0089	0.0133
9.8772	21710	0.009	-
9.8817	21720	0.0089	-
9.8863	21730	0.0091	-
9.8908	21740	0.0085	-
9.8954	21750	0.0086	-
9.8999	21760	0.0089	-
9.9045	21770	0.0083	-
9.9090	21780	0.009	-
9.9136	21790	0.0084	-
9.9181	21800	0.0089	0.0133
9.9227	21810	0.0089	-
9.9272	21820	0.0084	-
9.9318	21830	0.0086	-
9.9363	21840	0.0088	-
9.9409	21850	0.0087	-
9.9454	21860	0.0087	-
9.9500	21870	0.0086	-
9.9545	21880	0.0085	-
9.9591	21890	0.0086	-
9.9636	21900	0.0087	0.0133
9.9682	21910	0.0088	-
9.9727	21920	0.0087	-
9.9773	21930	0.0084	-
9.9818	21940	0.0089	-
9.9864	21950	0.0086	-
9.9909	21960	0.009	-
9.9955	21970	0.0089	-
10.0	21980	0.0086	-

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 3.2.0
• Transformers: 4.45.2
• PyTorch: 2.4.1+cu121
• Accelerate: 1.0.1
• Datasets: 3.0.1
• Tokenizers: 0.20.1

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}