Albertdebeauvais/all-MiniLM-L6-v2_cotes

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("Albertdebeauvais/all-MiniLM-L6-v2_cotes")
# Run inference
sentences = [
    'PRAGUE [Czech Republic] Národní knihovna Cod. hist. 17036',
    'Národní knihovna, Cod. hist. 17036 (PRAGUE)',
    'FLORENCE [Italy] Biblioteca Medicea Laurenziana, Département des manuscrits, Cod. 7698',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9921, 0.2995],
#         [0.9921, 1.0000, 0.3039],
#         [0.2995, 0.3039, 1.0000]])

Evaluation

Metrics

Binary Classification

• Datasets: eval and test
• Evaluated with BinaryClassificationEvaluator

Metric	eval	test
cosine_accuracy	0.9936	0.9919
cosine_accuracy_threshold	0.858	0.8689
cosine_f1	0.9934	0.9919
cosine_f1_threshold	0.8505	0.8689
cosine_precision	0.9928	0.9946
cosine_recall	0.9941	0.9892
cosine_ap	0.9994	0.9994
cosine_mcc	0.9872	0.9839

Training Details

Training Dataset

Unnamed Dataset

• Size: 183,668 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 6 tokens mean: 16.43 tokens max: 34 tokens	min: 7 tokens mean: 16.67 tokens max: 39 tokens	0: ~51.20% 1: ~48.80%

• Samples:

  text1	text2	label
  Vatican. Biblioteca apostolica vaticana, Vat.lat.1664	Vatican. Biblioteca apostolica vaticana, Vat.lat.1664	1
  Royal Library of Belgium (Brussels, Belgium), Voss. lat. 69542-73	STOCKHOLM [Sweden] Kungliga biblioteket Cod. 69542-73	0
  KB, ms. Ott. 34088	Staatsbibliothek zu Berlin, ms. nouv. acq. 34088	0

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 4,833 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 7 tokens mean: 16.21 tokens max: 39 tokens	min: 6 tokens mean: 16.65 tokens max: 41 tokens	0: ~52.00% 1: ~48.00%

• Samples:

  text1	text2	label
  NYPL, ms. EGERTON 44378	New York Public Library, Egerton 44378	1
  BRUSSELS [Belgium] KBR lat. 39761	BRUSSELS [Belgium] Bibliothèque royale de Belgique lat. 39761	1
  Stockholm, Royal Library Sweden, lat. 21045-64	Royal Library of Sweden, lat. 21045-64	1

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 160
• per_device_eval_batch_size: 160
• learning_rate: 3e-05
• warmup_ratio: 0.03

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 160
• per_device_eval_batch_size: 160
• 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: 3e-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: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.03
• 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: 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}
• parallelism_config: 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: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	eval_cosine_ap	test_cosine_ap
-1	-1	-	-	0.7380	-
0.0052	6	11.0534	-	-	-
0.0105	12	11.3792	-	-	-
0.0157	18	8.799	-	-	-
0.0209	24	7.7743	-	-	-
0.0261	30	5.8982	-	-	-
0.0314	36	4.9405	-	-	-
0.0366	42	3.2519	-	-	-
0.0418	48	2.195	-	-	-
0.0470	54	2.9317	-	-	-
0.0523	60	1.6287	-	-	-
0.0575	66	1.39	-	-	-
0.0627	72	1.6894	-	-	-
0.0679	78	1.7984	-	-	-
0.0732	84	1.4432	-	-	-
0.0784	90	1.4062	-	-	-
0.0836	96	1.5969	-	-	-
0.0889	102	1.3597	-	-	-
0.0941	108	1.1465	-	-	-
0.0993	114	1.1614	-	-	-
0.1045	120	1.116	-	-	-
0.1098	126	1.1741	-	-	-
0.1150	132	0.9491	-	-	-
0.1202	138	0.7978	-	-	-
0.1254	144	0.9691	-	-	-
0.1307	150	0.8838	-	-	-
0.1359	156	0.9894	-	-	-
0.1411	162	1.0388	-	-	-
0.1463	168	0.9774	-	-	-
0.1516	174	0.8315	-	-	-
0.1568	180	0.747	-	-	-
0.1620	186	0.7621	-	-	-
0.1672	192	0.8285	-	-	-
0.1725	198	0.8893	-	-	-
0.1777	204	0.8845	-	-	-
0.1829	210	0.7866	-	-	-
0.1882	216	0.8586	-	-	-
0.1934	222	0.8521	-	-	-
0.1986	228	0.9021	-	-	-
0.2038	234	0.5791	-	-	-
0.2091	240	0.5312	-	-	-
0.2143	246	0.6911	-	-	-
0.2195	252	0.543	-	-	-
0.2247	258	0.7933	-	-	-
0.2300	264	0.6489	-	-	-
0.2352	270	0.6369	-	-	-
0.2404	276	0.6113	-	-	-
0.2456	282	0.647	-	-	-
0.2509	288	0.6253	-	-	-
0.2561	294	0.7232	-	-	-
0.2613	300	0.5919	-	-	-
0.2666	306	0.5326	-	-	-
0.2718	312	0.7215	-	-	-
0.2770	318	0.7516	-	-	-
0.2822	324	0.5459	-	-	-
0.2875	330	0.3956	-	-	-
0.2927	336	0.6712	-	-	-
0.2979	342	1.1014	-	-	-
0.3031	348	0.7373	-	-	-
0.3084	354	0.6435	-	-	-
0.3136	360	0.726	-	-	-
0.3188	366	0.6514	-	-	-
0.3240	372	0.9203	-	-	-
0.3293	378	0.4015	-	-	-
0.3345	384	0.4945	-	-	-
0.3397	390	0.5672	-	-	-
0.3449	396	0.5229	-	-	-
0.3502	402	0.6377	-	-	-
0.3554	408	0.7667	-	-	-
0.3606	414	0.8402	-	-	-
0.3659	420	0.5398	-	-	-
0.3711	426	1.017	-	-	-
0.3763	432	0.6365	-	-	-
0.3815	438	0.2821	-	-	-
0.3868	444	0.7511	-	-	-
0.3920	450	0.5463	-	-	-
0.3972	456	0.4772	-	-	-
0.4024	462	0.6965	-	-	-
0.4077	468	0.646	-	-	-
0.4129	474	0.4739	-	-	-
0.4181	480	0.3673	-	-	-
0.4233	486	0.5785	-	-	-
0.4286	492	0.227	-	-	-
0.4338	498	0.7576	-	-	-
0.4390	504	0.8942	-	-	-
0.4443	510	0.4486	-	-	-
0.4495	516	0.3542	-	-	-
0.4547	522	0.4259	-	-	-
0.4599	528	0.5376	-	-	-
0.4652	534	0.7009	-	-	-
0.4704	540	0.5248	-	-	-
0.4756	546	0.577	-	-	-
0.4808	552	0.3948	-	-	-
0.4861	558	0.4447	-	-	-
0.4913	564	0.7539	-	-	-
0.4965	570	0.2763	-	-	-
0.5017	576	0.3015	-	-	-
0.5070	582	0.4449	-	-	-
0.5122	588	0.6351	-	-	-
0.5174	594	0.5846	-	-	-
0.5226	600	0.576	-	-	-
0.5279	606	0.3576	-	-	-
0.5331	612	0.2414	-	-	-
0.5383	618	0.5904	-	-	-
0.5436	624	0.5306	-	-	-
0.5488	630	0.474	-	-	-
0.5540	636	0.6083	-	-	-
0.5592	642	0.6796	-	-	-
0.5645	648	0.5498	-	-	-
0.5697	654	0.6603	-	-	-
0.5749	660	0.3717	-	-	-
0.5801	666	0.8338	-	-	-
0.5854	672	0.4483	-	-	-
0.5906	678	0.3904	-	-	-
0.5958	684	0.3456	-	-	-
0.6010	690	0.3724	-	-	-
0.6063	696	0.8648	-	-	-
0.6115	702	0.6776	-	-	-
0.6167	708	0.3602	-	-	-
0.6220	714	0.405	-	-	-
0.6272	720	0.5042	-	-	-
0.6324	726	0.458	-	-	-
0.6376	732	0.3791	-	-	-
0.6429	738	0.4746	-	-	-
0.6481	744	0.6014	-	-	-
0.6533	750	0.4847	-	-	-
0.6585	756	0.3132	-	-	-
0.6638	762	0.4689	-	-	-
0.6690	768	0.3886	-	-	-
0.6742	774	0.6002	-	-	-
0.6794	780	0.2	-	-	-
0.6847	786	0.4584	-	-	-
0.6899	792	0.5236	-	-	-
0.6951	798	0.2428	-	-	-
0.7003	804	0.7477	-	-	-
0.7056	810	0.5688	-	-	-
0.7108	816	0.2678	-	-	-
0.7160	822	0.2852	-	-	-
0.7213	828	0.3194	-	-	-
0.7265	834	0.6157	-	-	-
0.7317	840	0.2916	-	-	-
0.7369	846	0.5354	-	-	-
0.7422	852	0.5441	-	-	-
0.7474	858	0.3386	-	-	-
0.7526	864	0.2868	-	-	-
0.7578	870	0.8884	-	-	-
0.7631	876	0.1933	-	-	-
0.7683	882	0.5702	-	-	-
0.7735	888	0.4724	-	-	-
0.7787	894	0.4239	-	-	-
0.7840	900	0.5211	-	-	-
0.7892	906	0.5454	-	-	-
0.7944	912	0.3982	-	-	-
0.7997	918	0.6393	-	-	-
0.8049	924	0.2553	-	-	-
0.8101	930	0.2186	-	-	-
0.8153	936	0.3652	-	-	-
0.8206	942	0.4922	-	-	-
0.8258	948	0.4043	-	-	-
0.8310	954	0.5297	-	-	-
0.8362	960	0.4649	-	-	-
0.8415	966	0.4515	-	-	-
0.8467	972	0.3466	-	-	-
0.8519	978	0.4999	-	-	-
0.8571	984	0.4356	-	-	-
0.8624	990	0.4066	-	-	-
0.8676	996	0.1665	-	-	-
0.8728	1002	0.4078	-	-	-
0.8780	1008	0.2811	-	-	-
0.8833	1014	0.351	-	-	-
0.8885	1020	0.2498	-	-	-
0.8937	1026	0.3684	-	-	-
0.8990	1032	0.3429	-	-	-
0.9042	1038	0.3797	-	-	-
0.9094	1044	0.3756	-	-	-
0.9146	1050	0.1628	-	-	-
0.9199	1056	0.6396	-	-	-
0.9251	1062	0.4486	-	-	-
0.9303	1068	0.4347	-	-	-
0.9355	1074	0.3849	-	-	-
0.9408	1080	0.6004	-	-	-
0.9460	1086	0.5233	-	-	-
0.9512	1092	0.3776	-	-	-
0.9564	1098	0.5516	-	-	-
0.9617	1104	0.3355	-	-	-
0.9669	1110	0.3031	-	-	-
0.9721	1116	0.409	-	-	-
0.9774	1122	0.5007	-	-	-
0.9826	1128	0.4215	-	-	-
0.9878	1134	0.4394	-	-	-
0.9930	1140	0.273	-	-	-
0.9983	1146	0.365	-	-	-
1.0	1148	-	0.2877	0.9987	-
1.0035	1152	0.3812	-	-	-
1.0087	1158	0.2444	-	-	-
1.0139	1164	0.3097	-	-	-
1.0192	1170	0.4659	-	-	-
1.0244	1176	0.1669	-	-	-
1.0296	1182	0.1825	-	-	-
1.0348	1188	0.4532	-	-	-
1.0401	1194	0.5368	-	-	-
1.0453	1200	0.2084	-	-	-
1.0505	1206	0.1953	-	-	-
1.0557	1212	0.2187	-	-	-
1.0610	1218	0.3887	-	-	-
1.0662	1224	0.366	-	-	-
1.0714	1230	0.1054	-	-	-
1.0767	1236	0.5161	-	-	-
1.0819	1242	0.2891	-	-	-
1.0871	1248	0.2111	-	-	-
1.0923	1254	0.2921	-	-	-
1.0976	1260	0.2873	-	-	-
1.1028	1266	0.3253	-	-	-
1.1080	1272	0.3026	-	-	-
1.1132	1278	0.0585	-	-	-
1.1185	1284	0.2984	-	-	-
1.1237	1290	0.1654	-	-	-
1.1289	1296	0.6225	-	-	-
1.1341	1302	0.3749	-	-	-
1.1394	1308	0.3727	-	-	-
1.1446	1314	0.2266	-	-	-
1.1498	1320	0.2619	-	-	-
1.1551	1326	0.2534	-	-	-
1.1603	1332	0.3271	-	-	-
1.1655	1338	0.1328	-	-	-
1.1707	1344	0.4029	-	-	-
1.1760	1350	0.0869	-	-	-
1.1812	1356	0.6434	-	-	-
1.1864	1362	0.2033	-	-	-
1.1916	1368	0.2266	-	-	-
1.1969	1374	0.2547	-	-	-
1.2021	1380	0.2199	-	-	-
1.2073	1386	0.3716	-	-	-
1.2125	1392	0.4646	-	-	-
1.2178	1398	0.2163	-	-	-
1.2230	1404	0.1998	-	-	-
1.2282	1410	0.41	-	-	-
1.2334	1416	0.2859	-	-	-
1.2387	1422	0.2039	-	-	-
1.2439	1428	0.4095	-	-	-
1.2491	1434	0.0924	-	-	-
1.2544	1440	0.3192	-	-	-
1.2596	1446	0.4833	-	-	-
1.2648	1452	0.4927	-	-	-
1.2700	1458	0.2107	-	-	-
1.2753	1464	0.1869	-	-	-
1.2805	1470	0.188	-	-	-
1.2857	1476	0.0841	-	-	-
1.2909	1482	0.4332	-	-	-
1.2962	1488	0.3164	-	-	-
1.3014	1494	0.1526	-	-	-
1.3066	1500	0.0848	-	-	-
1.3118	1506	0.1896	-	-	-
1.3171	1512	0.2525	-	-	-
1.3223	1518	0.1776	-	-	-
1.3275	1524	0.2098	-	-	-
1.3328	1530	0.2494	-	-	-
1.3380	1536	0.1643	-	-	-
1.3432	1542	0.2588	-	-	-
1.3484	1548	0.2429	-	-	-
1.3537	1554	0.2195	-	-	-
1.3589	1560	0.138	-	-	-
1.3641	1566	0.285	-	-	-
1.3693	1572	0.7177	-	-	-
1.3746	1578	0.5872	-	-	-
1.3798	1584	0.1981	-	-	-
1.3850	1590	0.34	-	-	-
1.3902	1596	0.3694	-	-	-
1.3955	1602	0.0795	-	-	-
1.4007	1608	0.4017	-	-	-
1.4059	1614	0.364	-	-	-
1.4111	1620	0.2462	-	-	-
1.4164	1626	0.0681	-	-	-
1.4216	1632	0.2719	-	-	-
1.4268	1638	0.1616	-	-	-
1.4321	1644	0.3847	-	-	-
1.4373	1650	0.3032	-	-	-
1.4425	1656	0.2087	-	-	-
1.4477	1662	0.2143	-	-	-
1.4530	1668	0.2841	-	-	-
1.4582	1674	0.0644	-	-	-
1.4634	1680	0.1469	-	-	-
1.4686	1686	0.1478	-	-	-
1.4739	1692	0.1094	-	-	-
1.4791	1698	0.1843	-	-	-
1.4843	1704	0.4574	-	-	-
1.4895	1710	0.4318	-	-	-
1.4948	1716	0.1449	-	-	-
1.5	1722	0.2713	-	-	-
1.5052	1728	0.3274	-	-	-
1.5105	1734	0.2755	-	-	-
1.5157	1740	0.3226	-	-	-
1.5209	1746	0.2007	-	-	-
1.5261	1752	0.4237	-	-	-
1.5314	1758	0.2821	-	-	-
1.5366	1764	0.4661	-	-	-
1.5418	1770	0.2417	-	-	-
1.5470	1776	0.1035	-	-	-
1.5523	1782	0.238	-	-	-
1.5575	1788	0.1845	-	-	-
1.5627	1794	0.1721	-	-	-
1.5679	1800	0.04	-	-	-
1.5732	1806	0.0548	-	-	-
1.5784	1812	0.427	-	-	-
1.5836	1818	0.0614	-	-	-
1.5889	1824	0.1521	-	-	-
1.5941	1830	0.3391	-	-	-
1.5993	1836	0.2717	-	-	-
1.6045	1842	0.2131	-	-	-
1.6098	1848	0.3416	-	-	-
1.6150	1854	0.218	-	-	-
1.6202	1860	0.148	-	-	-
1.6254	1866	0.3309	-	-	-
1.6307	1872	0.0677	-	-	-
1.6359	1878	0.1093	-	-	-
1.6411	1884	0.4192	-	-	-
1.6463	1890	0.165	-	-	-
1.6516	1896	0.1975	-	-	-
1.6568	1902	0.2514	-	-	-
1.6620	1908	0.3038	-	-	-
1.6672	1914	0.4222	-	-	-
1.6725	1920	0.2399	-	-	-
1.6777	1926	0.1679	-	-	-
1.6829	1932	0.3724	-	-	-
1.6882	1938	0.1685	-	-	-
1.6934	1944	0.4037	-	-	-
1.6986	1950	0.3649	-	-	-
1.7038	1956	0.3041	-	-	-
1.7091	1962	0.29	-	-	-
1.7143	1968	0.2204	-	-	-
1.7195	1974	0.3762	-	-	-
1.7247	1980	0.3857	-	-	-
1.7300	1986	0.2591	-	-	-
1.7352	1992	0.1436	-	-	-
1.7404	1998	0.3725	-	-	-
1.7456	2004	0.272	-	-	-
1.7509	2010	0.2497	-	-	-
1.7561	2016	0.211	-	-	-
1.7613	2022	0.339	-	-	-
1.7666	2028	0.3218	-	-	-
1.7718	2034	0.28	-	-	-
1.7770	2040	0.2353	-	-	-
1.7822	2046	0.1672	-	-	-
1.7875	2052	0.1992	-	-	-
1.7927	2058	0.1121	-	-	-
1.7979	2064	0.2625	-	-	-
1.8031	2070	0.3866	-	-	-
1.8084	2076	0.35	-	-	-
1.8136	2082	0.1784	-	-	-
1.8188	2088	0.2353	-	-	-
1.8240	2094	0.2156	-	-	-
1.8293	2100	0.1825	-	-	-
1.8345	2106	0.2695	-	-	-
1.8397	2112	0.2211	-	-	-
1.8449	2118	0.3734	-	-	-
1.8502	2124	0.4629	-	-	-
1.8554	2130	0.1376	-	-	-
1.8606	2136	0.2899	-	-	-
1.8659	2142	0.2706	-	-	-
1.8711	2148	0.3565	-	-	-
1.8763	2154	0.1231	-	-	-
1.8815	2160	0.3058	-	-	-
1.8868	2166	0.1174	-	-	-
1.8920	2172	0.2687	-	-	-
1.8972	2178	0.1954	-	-	-
1.9024	2184	0.1452	-	-	-
1.9077	2190	0.2603	-	-	-
1.9129	2196	0.2607	-	-	-
1.9181	2202	0.2368	-	-	-
1.9233	2208	0.3415	-	-	-
1.9286	2214	0.1312	-	-	-
1.9338	2220	0.1627	-	-	-
1.9390	2226	0.1815	-	-	-
1.9443	2232	0.089	-	-	-
1.9495	2238	0.1868	-	-	-
1.9547	2244	0.1073	-	-	-
1.9599	2250	0.341	-	-	-
1.9652	2256	0.2377	-	-	-
1.9704	2262	0.2618	-	-	-
1.9756	2268	0.24	-	-	-
1.9808	2274	0.2164	-	-	-
1.9861	2280	0.1535	-	-	-
1.9913	2286	0.3638	-	-	-
1.9965	2292	0.2644	-	-	-
2.0	2296	-	0.1788	0.9993	-
2.0017	2298	0.1214	-	-	-
2.0070	2304	0.1739	-	-	-
2.0122	2310	0.143	-	-	-
2.0174	2316	0.2787	-	-	-
2.0226	2322	0.0949	-	-	-
2.0279	2328	0.1275	-	-	-
2.0331	2334	0.1298	-	-	-
2.0383	2340	0.1309	-	-	-
2.0436	2346	0.2705	-	-	-
2.0488	2352	0.1701	-	-	-
2.0540	2358	0.0701	-	-	-
2.0592	2364	0.1964	-	-	-
2.0645	2370	0.3012	-	-	-
2.0697	2376	0.0734	-	-	-
2.0749	2382	0.1742	-	-	-
2.0801	2388	0.0627	-	-	-
2.0854	2394	0.1086	-	-	-
2.0906	2400	0.0505	-	-	-
2.0958	2406	0.1698	-	-	-
2.1010	2412	0.2281	-	-	-
2.1063	2418	0.2046	-	-	-
2.1115	2424	0.2343	-	-	-
2.1167	2430	0.1266	-	-	-
2.1220	2436	0.0939	-	-	-
2.1272	2442	0.2014	-	-	-
2.1324	2448	0.0345	-	-	-
2.1376	2454	0.164	-	-	-
2.1429	2460	0.1983	-	-	-
2.1481	2466	0.3191	-	-	-
2.1533	2472	0.2991	-	-	-
2.1585	2478	0.089	-	-	-
2.1638	2484	0.136	-	-	-
2.1690	2490	0.0	-	-	-
2.1742	2496	0.1122	-	-	-
2.1794	2502	0.1697	-	-	-
2.1847	2508	0.0866	-	-	-
2.1899	2514	0.1509	-	-	-
2.1951	2520	0.3506	-	-	-
2.2003	2526	0.0	-	-	-
2.2056	2532	0.1797	-	-	-
2.2108	2538	0.0762	-	-	-
2.2160	2544	0.1793	-	-	-
2.2213	2550	0.0303	-	-	-
2.2265	2556	0.3666	-	-	-
2.2317	2562	0.0903	-	-	-
2.2369	2568	0.2618	-	-	-
2.2422	2574	0.0372	-	-	-
2.2474	2580	0.1638	-	-	-
2.2526	2586	0.1492	-	-	-
2.2578	2592	0.128	-	-	-
2.2631	2598	0.2282	-	-	-
2.2683	2604	0.14	-	-	-
2.2735	2610	0.2016	-	-	-
2.2787	2616	0.2329	-	-	-
2.2840	2622	0.0231	-	-	-
2.2892	2628	0.1221	-	-	-
2.2944	2634	0.2853	-	-	-
2.2997	2640	0.1054	-	-	-
2.3049	2646	0.1585	-	-	-
2.3101	2652	0.1773	-	-	-
2.3153	2658	0.0978	-	-	-
2.3206	2664	0.1096	-	-	-
2.3258	2670	0.0669	-	-	-
2.3310	2676	0.0727	-	-	-
2.3362	2682	0.329	-	-	-
2.3415	2688	0.2567	-	-	-
2.3467	2694	0.0864	-	-	-
2.3519	2700	0.1642	-	-	-
2.3571	2706	0.1138	-	-	-
2.3624	2712	0.3575	-	-	-
2.3676	2718	0.1272	-	-	-
2.3728	2724	0.0902	-	-	-
2.3780	2730	0.211	-	-	-
2.3833	2736	0.1225	-	-	-
2.3885	2742	0.1512	-	-	-
2.3937	2748	0.2895	-	-	-
2.3990	2754	0.1159	-	-	-
2.4042	2760	0.0993	-	-	-
2.4094	2766	0.109	-	-	-
2.4146	2772	0.1609	-	-	-
2.4199	2778	0.2196	-	-	-
2.4251	2784	0.0618	-	-	-
2.4303	2790	0.1255	-	-	-
2.4355	2796	0.2316	-	-	-
2.4408	2802	0.1175	-	-	-
2.4460	2808	0.2023	-	-	-
2.4512	2814	0.2239	-	-	-
2.4564	2820	0.2202	-	-	-
2.4617	2826	0.2029	-	-	-
2.4669	2832	0.2325	-	-	-
2.4721	2838	0.1697	-	-	-
2.4774	2844	0.3761	-	-	-
2.4826	2850	0.1319	-	-	-
2.4878	2856	0.0234	-	-	-
2.4930	2862	0.3063	-	-	-
2.4983	2868	0.0783	-	-	-
2.5035	2874	0.1579	-	-	-
2.5087	2880	0.3283	-	-	-
2.5139	2886	0.217	-	-	-
2.5192	2892	0.0555	-	-	-
2.5244	2898	0.1873	-	-	-
2.5296	2904	0.1958	-	-	-
2.5348	2910	0.2545	-	-	-
2.5401	2916	0.1208	-	-	-
2.5453	2922	0.212	-	-	-
2.5505	2928	0.2414	-	-	-
2.5557	2934	0.1004	-	-	-
2.5610	2940	0.0302	-	-	-
2.5662	2946	0.1554	-	-	-
2.5714	2952	0.1421	-	-	-
2.5767	2958	0.1493	-	-	-
2.5819	2964	0.2166	-	-	-
2.5871	2970	0.4662	-	-	-
2.5923	2976	0.0	-	-	-
2.5976	2982	0.3535	-	-	-
2.6028	2988	0.1567	-	-	-
2.6080	2994	0.3008	-	-	-
2.6132	3000	0.1711	-	-	-
2.6185	3006	0.1507	-	-	-
2.6237	3012	0.2314	-	-	-
2.6289	3018	0.1814	-	-	-
2.6341	3024	0.1327	-	-	-
2.6394	3030	0.1694	-	-	-
2.6446	3036	0.1623	-	-	-
2.6498	3042	0.1089	-	-	-
2.6551	3048	0.1668	-	-	-
2.6603	3054	0.0577	-	-	-
2.6655	3060	0.1246	-	-	-
2.6707	3066	0.0771	-	-	-
2.6760	3072	0.2558	-	-	-
2.6812	3078	0.1282	-	-	-
2.6864	3084	0.2405	-	-	-
2.6916	3090	0.2521	-	-	-
2.6969	3096	0.2159	-	-	-
2.7021	3102	0.3155	-	-	-
2.7073	3108	0.0728	-	-	-
2.7125	3114	0.1084	-	-	-
2.7178	3120	0.108	-	-	-
2.7230	3126	0.2431	-	-	-
2.7282	3132	0.075	-	-	-
2.7334	3138	0.2153	-	-	-
2.7387	3144	0.2256	-	-	-
2.7439	3150	0.153	-	-	-
2.7491	3156	0.1715	-	-	-
2.7544	3162	0.2082	-	-	-
2.7596	3168	0.1098	-	-	-
2.7648	3174	0.1658	-	-	-
2.7700	3180	0.0779	-	-	-
2.7753	3186	0.2188	-	-	-
2.7805	3192	0.1308	-	-	-
2.7857	3198	0.1662	-	-	-
2.7909	3204	0.1753	-	-	-
2.7962	3210	0.1767	-	-	-
2.8014	3216	0.1818	-	-	-
2.8066	3222	0.207	-	-	-
2.8118	3228	0.3599	-	-	-
2.8171	3234	0.1318	-	-	-
2.8223	3240	0.1923	-	-	-
2.8275	3246	0.1841	-	-	-
2.8328	3252	0.1179	-	-	-
2.8380	3258	0.2105	-	-	-
2.8432	3264	0.1522	-	-	-
2.8484	3270	0.1161	-	-	-
2.8537	3276	0.134	-	-	-
2.8589	3282	0.0355	-	-	-
2.8641	3288	0.1747	-	-	-
2.8693	3294	0.101	-	-	-
2.8746	3300	0.1603	-	-	-
2.8798	3306	0.1461	-	-	-
2.8850	3312	0.0955	-	-	-
2.8902	3318	0.1072	-	-	-
2.8955	3324	0.0749	-	-	-
2.9007	3330	0.3698	-	-	-
2.9059	3336	0.1146	-	-	-
2.9111	3342	0.0699	-	-	-
2.9164	3348	0.2239	-	-	-
2.9216	3354	0.2916	-	-	-
2.9268	3360	0.1078	-	-	-
2.9321	3366	0.2395	-	-	-
2.9373	3372	0.3056	-	-	-
2.9425	3378	0.0643	-	-	-
2.9477	3384	0.1579	-	-	-
2.9530	3390	0.2721	-	-	-
2.9582	3396	0.1975	-	-	-
2.9634	3402	0.1886	-	-	-
2.9686	3408	0.1968	-	-	-
2.9739	3414	0.0632	-	-	-
2.9791	3420	0.1413	-	-	-
2.9843	3426	0.1126	-	-	-
2.9895	3432	0.1712	-	-	-
2.9948	3438	0.042	-	-	-
3.0	3444	0.236	0.1238	0.9994	-
-1	-1	-	-	-	0.9994

Framework Versions

• Python: 3.9.21
• Sentence Transformers: 5.1.0
• Transformers: 4.56.1
• PyTorch: 2.8.0+cu129
• Accelerate: 1.10.1
• Datasets: 4.1.0
• Tokenizers: 0.22.0

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",
}