basaryilmaz/e5-multilingual-large-finetuned-movielens

SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large. 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
• Base model: intfloat/multilingual-e5-large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Cold Comfort Farm (1995)',
    'Daylight (1996)',
    'Heavenly Creatures (1994)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 571,688 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	float
  details	min: 4 tokens mean: 7.56 tokens max: 26 tokens	min: 4 tokens mean: 8.18 tokens max: 26 tokens	min: 0.0 mean: 0.25 max: 0.9

• Samples:

  text1	text2	label
  Patton (1970)	Shining, The (1980)	0.15
  Davy Crockett, King of the Wild Frontier (1955)	Double Happiness (1994)	0.15
  Birdcage, The (1996)	Phantom, The (1996)	0.25

• Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 71,461 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	float
  details	min: 4 tokens mean: 7.7 tokens max: 26 tokens	min: 4 tokens mean: 8.06 tokens max: 26 tokens	min: 0.0 mean: 0.25 max: 0.75

• Samples:

  text1	text2	label
  Chamber, The (1996)	Cutthroat Island (1995)	0.25
  Drop Dead Fred (1991)	Madness of King George, The (1994)	0.15
  Singin' in the Rain (1952)	With Honors (1994)	0.15

• Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• num_train_epochs: 100
• warmup_ratio: 0.05
• ddp_find_unused_parameters: False

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• 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: 100
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• 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: 1
• 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}
• tp_size: 0
• 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: False
• 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
• 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0009	1	10.0822	-
0.0896	100	10.0689	-
0.1792	200	10.0617	-
0.2688	300	10.0466	-
0.3584	400	10.0214	-
0.4480	500	9.9876	9.9658
0.5376	600	9.9544	-
0.6272	700	9.9268	-
0.7168	800	9.9006	-
0.8065	900	9.8907	-
0.8961	1000	9.8696	9.8545
0.9857	1100	9.8625	-
1.0753	1200	9.8474	-
1.1649	1300	9.8339	-
1.2545	1400	9.8158	-
1.3441	1500	9.81	9.7777
1.4337	1600	9.7977	-
1.5233	1700	9.7895	-
1.6129	1800	9.7749	-
1.7025	1900	9.7695	-
1.7921	2000	9.7578	9.7107
1.8817	2100	9.7528	-
1.9713	2200	9.7449	-
2.0609	2300	9.7334	-
2.1505	2400	9.7261	-
2.2401	2500	9.7132	9.6617
2.3297	2600	9.7142	-
2.4194	2700	9.7024	-
2.5090	2800	9.696	-
2.5986	2900	9.6863	-
2.6882	3000	9.6733	9.6197
2.7778	3100	9.6772	-
2.8674	3200	9.6637	-
2.9570	3300	9.6647	-
3.0466	3400	9.6576	-
3.1362	3500	9.6563	9.5798
3.2258	3600	9.6396	-
3.3154	3700	9.6329	-
3.4050	3800	9.6175	-
3.4946	3900	9.6163	-
3.5842	4000	9.6306	9.5412
3.6738	4100	9.6197	-
3.7634	4200	9.6053	-
3.8530	4300	9.6015	-
3.9427	4400	9.5932	-
4.0323	4500	9.5936	9.4967
4.1219	4600	9.5797	-
4.2115	4700	9.5775	-
4.3011	4800	9.572	-
4.3907	4900	9.5639	-
4.4803	5000	9.5652	9.4773
4.5699	5100	9.5603	-
4.6595	5200	9.5561	-
4.7491	5300	9.5403	-
4.8387	5400	9.5464	-
4.9283	5500	9.5473	9.4351
5.0179	5600	9.5339	-
5.1075	5700	9.5303	-
5.1971	5800	9.5236	-
5.2867	5900	9.5164	-
5.3763	6000	9.5074	9.4207
5.4659	6100	9.5112	-
5.5556	6200	9.5008	-
5.6452	6300	9.5073	-
5.7348	6400	9.4836	-
5.8244	6500	9.4963	9.3733
5.9140	6600	9.4811	-
6.0036	6700	9.4743	-
6.0932	6800	9.4683	-
6.1828	6900	9.4686	-
6.2724	7000	9.475	9.3494
6.3620	7100	9.4632	-
6.4516	7200	9.4612	-
6.5412	7300	9.4474	-
6.6308	7400	9.4503	-
6.7204	7500	9.4461	9.3347
6.8100	7600	9.4475	-
6.8996	7700	9.4397	-
6.9892	7800	9.444	-
7.0789	7900	9.4325	-
7.1685	8000	9.4419	9.3222
7.2581	8100	9.4273	-
7.3477	8200	9.4244	-
7.4373	8300	9.4231	-
7.5269	8400	9.4246	-
7.6165	8500	9.4238	9.3011
7.7061	8600	9.4053	-
7.7957	8700	9.3979	-
7.8853	8800	9.4097	-
7.9749	8900	9.4051	-
8.0645	9000	9.416	9.2830
8.1541	9100	9.3876	-
8.2437	9200	9.3931	-
8.3333	9300	9.3924	-
8.4229	9400	9.3994	-
8.5125	9500	9.3835	9.2748
8.6022	9600	9.3751	-
8.6918	9700	9.3939	-
8.7814	9800	9.3924	-
8.8710	9900	9.3686	-
8.9606	10000	9.3837	9.2636
9.0502	10100	9.3759	-
9.1398	10200	9.3729	-
9.2294	10300	9.3717	-
9.3190	10400	9.3724	-
9.4086	10500	9.3625	9.2673
9.4982	10600	9.3636	-
9.5878	10700	9.3609	-
9.6774	10800	9.3623	-
9.7670	10900	9.3516	-
9.8566	11000	9.3604	9.2450
9.9462	11100	9.3571	-
10.0358	11200	9.3394	-
10.1254	11300	9.3424	-
10.2151	11400	9.3396	-
10.3047	11500	9.3485	9.2305
10.3943	11600	9.3396	-
10.4839	11700	9.3315	-
10.5735	11800	9.3427	-
10.6631	11900	9.338	-
10.7527	12000	9.3387	9.2277
10.8423	12100	9.3373	-
10.9319	12200	9.3406	-
11.0215	12300	9.3356	-
11.1111	12400	9.3265	-
11.2007	12500	9.3325	9.2159
11.2903	12600	9.323	-
11.3799	12700	9.3253	-
11.4695	12800	9.3148	-
11.5591	12900	9.3178	-
11.6487	13000	9.3226	9.2007
11.7384	13100	9.3178	-
11.8280	13200	9.3114	-
11.9176	13300	9.3142	-
12.0072	13400	9.312	-
12.0968	13500	9.2996	9.2003
12.1864	13600	9.3114	-
12.2760	13700	9.2992	-
12.3656	13800	9.3057	-
12.4552	13900	9.3013	-
12.5448	14000	9.2896	9.1900
12.6344	14100	9.2926	-
12.7240	14200	9.298	-
12.8136	14300	9.3083	-
12.9032	14400	9.2866	-
12.9928	14500	9.3009	9.1835
13.0824	14600	9.2923	-
13.1720	14700	9.2874	-
13.2616	14800	9.2806	-
13.3513	14900	9.2815	-
13.4409	15000	9.2925	9.1725
13.5305	15100	9.2797	-
13.6201	15200	9.27	-
13.7097	15300	9.2808	-
13.7993	15400	9.2907	-
13.8889	15500	9.2826	9.1618
13.9785	15600	9.2765	-
14.0681	15700	9.2724	-
14.1577	15800	9.2754	-
14.2473	15900	9.2688	-
14.3369	16000	9.2645	9.1686
14.4265	16100	9.277	-
14.5161	16200	9.2692	-
14.6057	16300	9.2736	-
14.6953	16400	9.2568	-
14.7849	16500	9.2795	9.1639
14.8746	16600	9.2669	-
14.9642	16700	9.2627	-
15.0538	16800	9.2572	-
15.1434	16900	9.2479	-
15.2330	17000	9.2496	9.1483
15.3226	17100	9.2528	-
15.4122	17200	9.2451	-
15.5018	17300	9.2406	-
15.5914	17400	9.2516	-
15.6810	17500	9.26	9.1459
15.7706	17600	9.2603	-
15.8602	17700	9.2528	-
15.9498	17800	9.2507	-
16.0394	17900	9.2459	-
16.1290	18000	9.2427	9.1456
16.2186	18100	9.2391	-
16.3082	18200	9.2332	-
16.3978	18300	9.2367	-
16.4875	18400	9.2442	-
16.5771	18500	9.2381	9.1286
16.6667	18600	9.2414	-
16.7563	18700	9.2344	-
16.8459	18800	9.2424	-
16.9355	18900	9.2364	-
17.0251	19000	9.2355	9.1264
17.1147	19100	9.2166	-
17.2043	19200	9.2186	-
17.2939	19300	9.2311	-
17.3835	19400	9.2253	-
17.4731	19500	9.2298	9.1125
17.5627	19600	9.2265	-
17.6523	19700	9.2203	-
17.7419	19800	9.2256	-
17.8315	19900	9.2234	-
17.9211	20000	9.2301	9.1168
18.0108	20100	9.2121	-
18.1004	20200	9.2094	-
18.1900	20300	9.2065	-
18.2796	20400	9.2172	-
18.3692	20500	9.2085	9.1133
18.4588	20600	9.2045	-
18.5484	20700	9.1981	-
18.6380	20800	9.2089	-
18.7276	20900	9.2031	-
18.8172	21000	9.2249	9.0949
18.9068	21100	9.2157	-
18.9964	21200	9.2131	-
19.0860	21300	9.1946	-
19.1756	21400	9.2064	-
19.2652	21500	9.2009	9.0984
19.3548	21600	9.2076	-
19.4444	21700	9.2003	-
19.5341	21800	9.1955	-
19.6237	21900	9.1939	-
19.7133	22000	9.207	9.0893
19.8029	22100	9.1947	-
19.8925	22200	9.2024	-
19.9821	22300	9.2044	-
20.0717	22400	9.1939	-
20.1613	22500	9.1844	9.0940
20.2509	22600	9.187	-
20.3405	22700	9.1857	-
20.4301	22800	9.192	-
20.5197	22900	9.1839	-
20.6093	23000	9.1843	9.0802
20.6989	23100	9.2059	-
20.7885	23200	9.1863	-
20.8781	23300	9.1908	-
20.9677	23400	9.1881	-
21.0573	23500	9.1774	9.0814
21.1470	23600	9.1844	-
21.2366	23700	9.1867	-
21.3262	23800	9.1839	-
21.4158	23900	9.1855	-
21.5054	24000	9.1768	9.0747
21.5950	24100	9.1764	-
21.6846	24200	9.1893	-
21.7742	24300	9.1632	-
21.8638	24400	9.1743	-
21.9534	24500	9.1747	9.0654
22.0430	24600	9.1688	-
22.1326	24700	9.1724	-
22.2222	24800	9.1694	-
22.3118	24900	9.1644	-
22.4014	25000	9.1668	9.0716
22.4910	25100	9.1722	-
22.5806	25200	9.18	-
22.6703	25300	9.1725	-
22.7599	25400	9.1624	-
22.8495	25500	9.1834	9.0573
22.9391	25600	9.1682	-
23.0287	25700	9.1757	-
23.1183	25800	9.1578	-
23.2079	25900	9.1534	-
23.2975	26000	9.1744	9.0573
23.3871	26100	9.1629	-
23.4767	26200	9.1601	-
23.5663	26300	9.162	-
23.6559	26400	9.1515	-
23.7455	26500	9.1567	9.0569
23.8351	26600	9.1539	-
23.9247	26700	9.1593	-
24.0143	26800	9.1575	-
24.1039	26900	9.1418	-
24.1935	27000	9.1617	9.0526
24.2832	27100	9.1457	-
24.3728	27200	9.151	-
24.4624	27300	9.1637	-
24.5520	27400	9.155	-
24.6416	27500	9.1616	9.0507
24.7312	27600	9.1536	-
24.8208	27700	9.1408	-
24.9104	27800	9.1515	-
25.0	27900	9.1483	-
25.0896	28000	9.1436	9.0452
25.1792	28100	9.155	-
25.2688	28200	9.1427	-
25.3584	28300	9.1516	-
25.4480	28400	9.1354	-
25.5376	28500	9.1456	9.0398
25.6272	28600	9.1452	-
25.7168	28700	9.1402	-
25.8065	28800	9.1423	-
25.8961	28900	9.1459	-
25.9857	29000	9.1308	9.0391
26.0753	29100	9.1277	-
26.1649	29200	9.1342	-
26.2545	29300	9.1307	-
26.3441	29400	9.1267	-
26.4337	29500	9.1439	9.0355
26.5233	29600	9.1267	-
26.6129	29700	9.142	-
26.7025	29800	9.153	-
26.7921	29900	9.1329	-
26.8817	30000	9.1404	9.0341
26.9713	30100	9.1321	-
27.0609	30200	9.1357	-
27.1505	30300	9.1299	-
27.2401	30400	9.1195	-
27.3297	30500	9.1239	9.0367
27.4194	30600	9.1337	-
27.5090	30700	9.1209	-
27.5986	30800	9.1303	-
27.6882	30900	9.1225	-
27.7778	31000	9.131	9.0234
27.8674	31100	9.1347	-
27.9570	31200	9.1219	-
28.0466	31300	9.1203	-
28.1362	31400	9.1246	-
28.2258	31500	9.1201	9.0205
28.3154	31600	9.1261	-
28.4050	31700	9.1185	-
28.4946	31800	9.1192	-
28.5842	31900	9.1226	-
28.6738	32000	9.1207	9.0187
28.7634	32100	9.1254	-
28.8530	32200	9.1193	-
28.9427	32300	9.117	-
29.0323	32400	9.123	-
29.1219	32500	9.1156	9.0212
29.2115	32600	9.1198	-
29.3011	32700	9.0997	-
29.3907	32800	9.1146	-
29.4803	32900	9.1117	-
29.5699	33000	9.127	9.0187
29.6595	33100	9.1137	-
29.7491	33200	9.1061	-
29.8387	33300	9.1201	-
29.9283	33400	9.1155	-
30.0179	33500	9.1206	9.0119
30.1075	33600	9.1121	-
30.1971	33700	9.0974	-
30.2867	33800	9.1108	-
30.3763	33900	9.11	-
30.4659	34000	9.1028	9.0113
30.5556	34100	9.1102	-
30.6452	34200	9.1187	-
30.7348	34300	9.1081	-
30.8244	34400	9.1052	-
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31.3620	35000	9.0992	9.0172
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31.5412	35200	9.0957	-
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32.0789	35800	9.0988	-
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33.6022	37500	9.0922	8.9982
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33.7814	37700	9.0924	-
33.8710	37800	9.0851	-
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34.0502	38000	9.0784	8.9986
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40.3226	45000	9.0489	8.9737
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52.5986	58700	9.0061	-
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52.9570	59100	9.0154	-
53.0466	59200	8.9976	-
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53.3154	59500	9.0091	8.9370
53.4050	59600	9.0197	-
53.4946	59700	9.0037	-
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54.0323	60300	9.0076	-
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54.2115	60500	9.0036	8.9419
54.3011	60600	8.9983	-
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54.5699	60900	9.0008	-
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54.7491	61100	9.0034	-
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58.8710	65700	8.9853	-
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59.0502	65900	8.9928	-
59.1398	66000	8.9904	8.9302
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59.3190	66200	8.9911	-
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59.7670	66700	8.9858	-
59.8566	66800	8.9897	-
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62.9032	70200	9.0037	-
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66.8459	74600	8.9951	-
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72.9391	81400	8.9591	-
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73.2079	81700	8.9575	-
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73.6559	82200	8.9741	-
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73.8351	82400	8.9589	-
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74.7312	83400	8.9534	-
74.8208	83500	8.972	8.9100
74.9104	83600	8.967	-
75.0	83700	8.9563	-
75.0896	83800	8.9696	-
75.1792	83900	8.9455	-
75.2688	84000	8.957	8.9132
75.3584	84100	8.9627	-
75.4480	84200	8.965	-
75.5376	84300	8.9616	-
75.6272	84400	8.9594	-
75.7168	84500	8.9626	8.9110
75.8065	84600	8.9751	-
75.8961	84700	8.9649	-
75.9857	84800	8.9629	-
76.0753	84900	8.9681	-
76.1649	85000	8.9523	8.9109
76.2545	85100	8.9456	-
76.3441	85200	8.9707	-
76.4337	85300	8.9617	-
76.5233	85400	8.9653	-
76.6129	85500	8.9777	8.9074
76.7025	85600	8.9561	-
76.7921	85700	8.956	-
76.8817	85800	8.9536	-
76.9713	85900	8.966	-
77.0609	86000	8.9655	8.9106
77.1505	86100	8.9613	-
77.2401	86200	8.952	-
77.3297	86300	8.957	-
77.4194	86400	8.9766	-
77.5090	86500	8.9616	8.9101
77.5986	86600	8.9579	-
77.6882	86700	8.9464	-
77.7778	86800	8.9555	-
77.8674	86900	8.9501	-
77.9570	87000	8.9598	8.9062
78.0466	87100	8.9479	-
78.1362	87200	8.9656	-
78.2258	87300	8.9513	-
78.3154	87400	8.9789	-
78.4050	87500	8.9622	8.9064
78.4946	87600	8.9609	-
78.5842	87700	8.9554	-
78.6738	87800	8.9579	-
78.7634	87900	8.962	-
78.8530	88000	8.9627	8.9099
78.9427	88100	8.953	-
79.0323	88200	8.9457	-
79.1219	88300	8.9497	-
79.2115	88400	8.9439	-
79.3011	88500	8.9513	8.9127
79.3907	88600	8.965	-
79.4803	88700	8.9461	-
79.5699	88800	8.9685	-
79.6595	88900	8.9781	-
79.7491	89000	8.9579	8.9073
79.8387	89100	8.9627	-
79.9283	89200	8.9536	-
80.0179	89300	8.935	-
80.1075	89400	8.9562	-
80.1971	89500	8.9561	8.9101
80.2867	89600	8.9587	-
80.3763	89700	8.9693	-
80.4659	89800	8.9527	-
80.5556	89900	8.9557	-
80.6452	90000	8.9632	8.9039
80.7348	90100	8.9614	-
80.8244	90200	8.9464	-
80.9140	90300	8.9394	-
81.0036	90400	8.9535	-
81.0932	90500	8.9466	8.9096
81.1828	90600	8.9465	-
81.2724	90700	8.9619	-
81.3620	90800	8.9553	-
81.4516	90900	8.9498	-
81.5412	91000	8.9572	8.9052
81.6308	91100	8.9595	-
81.7204	91200	8.951	-
81.8100	91300	8.9669	-
81.8996	91400	8.9602	-
81.9892	91500	8.9562	8.9082
82.0789	91600	8.9479	-
82.1685	91700	8.9481	-
82.2581	91800	8.9583	-
82.3477	91900	8.9571	-
82.4373	92000	8.9497	8.9083
82.5269	92100	8.9645	-
82.6165	92200	8.9539	-
82.7061	92300	8.96	-
82.7957	92400	8.954	-
82.8853	92500	8.9469	8.9080
82.9749	92600	8.9531	-
83.0645	92700	8.9491	-
83.1541	92800	8.9553	-
83.2437	92900	8.9401	-
83.3333	93000	8.9475	8.9096
83.4229	93100	8.957	-
83.5125	93200	8.9611	-
83.6022	93300	8.9527	-
83.6918	93400	8.9674	-
83.7814	93500	8.9473	8.9054
83.8710	93600	8.9389	-
83.9606	93700	8.9558	-
84.0502	93800	8.9534	-
84.1398	93900	8.9386	-
84.2294	94000	8.9506	8.9050
84.3190	94100	8.9452	-
84.4086	94200	8.9416	-
84.4982	94300	8.9506	-
84.5878	94400	8.9613	-
84.6774	94500	8.9625	8.9060
84.7670	94600	8.9614	-
84.8566	94700	8.9418	-
84.9462	94800	8.9597	-
85.0358	94900	8.9511	-
85.1254	95000	8.9424	8.9064
85.2151	95100	8.9607	-
85.3047	95200	8.9469	-
85.3943	95300	8.9539	-
85.4839	95400	8.9442	-
85.5735	95500	8.9522	8.9037
85.6631	95600	8.9416	-
85.7527	95700	8.9448	-
85.8423	95800	8.9405	-
85.9319	95900	8.9658	-
86.0215	96000	8.9539	8.9015
86.1111	96100	8.9412	-
86.2007	96200	8.9368	-
86.2903	96300	8.9577	-
86.3799	96400	8.9473	-
86.4695	96500	8.9569	8.9043
86.5591	96600	8.9651	-
86.6487	96700	8.9493	-
86.7384	96800	8.9479	-
86.8280	96900	8.9522	-
86.9176	97000	8.9539	8.9030
87.0072	97100	8.951	-
87.0968	97200	8.9538	-
87.1864	97300	8.9382	-
87.2760	97400	8.9568	-
87.3656	97500	8.9471	8.9019
87.4552	97600	8.9425	-
87.5448	97700	8.9518	-
87.6344	97800	8.939	-
87.7240	97900	8.9487	-
87.8136	98000	8.9504	8.9039
87.9032	98100	8.9481	-
87.9928	98200	8.9536	-
88.0824	98300	8.9402	-
88.1720	98400	8.939	-
88.2616	98500	8.9406	8.9032
88.3513	98600	8.9436	-
88.4409	98700	8.9438	-
88.5305	98800	8.9527	-
88.6201	98900	8.9531	-
88.7097	99000	8.958	8.9019
88.7993	99100	8.9429	-
88.8889	99200	8.9435	-
88.9785	99300	8.9597	-
89.0681	99400	8.952	-
89.1577	99500	8.9432	8.9021
89.2473	99600	8.9438	-
89.3369	99700	8.9555	-
89.4265	99800	8.936	-
89.5161	99900	8.9416	-
89.6057	100000	8.958	8.9026
89.6953	100100	8.9527	-
89.7849	100200	8.9399	-
89.8746	100300	8.9449	-
89.9642	100400	8.9447	-
90.0538	100500	8.9498	8.9019
90.1434	100600	8.9416	-
90.2330	100700	8.9409	-
90.3226	100800	8.9562	-
90.4122	100900	8.9515	-
90.5018	101000	8.946	8.9003
90.5914	101100	8.9404	-
90.6810	101200	8.9536	-
90.7706	101300	8.9475	-
90.8602	101400	8.9424	-
90.9498	101500	8.9391	8.9022
91.0394	101600	8.9407	-
91.1290	101700	8.9539	-
91.2186	101800	8.9448	-
91.3082	101900	8.9328	-
91.3978	102000	8.941	8.9019
91.4875	102100	8.9407	-
91.5771	102200	8.941	-
91.6667	102300	8.9489	-
91.7563	102400	8.943	-
91.8459	102500	8.9571	8.9005
91.9355	102600	8.9515	-
92.0251	102700	8.9495	-
92.1147	102800	8.9452	-
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92.2939	103000	8.9414	8.9005
92.3835	103100	8.9441	-
92.4731	103200	8.9296	-
92.5627	103300	8.9398	-
92.6523	103400	8.9455	-
92.7419	103500	8.9598	8.9005
92.8315	103600	8.9447	-
92.9211	103700	8.9431	-
93.0108	103800	8.9504	-
93.1004	103900	8.9383	-
93.1900	104000	8.9397	8.9024
93.2796	104100	8.9421	-
93.3692	104200	8.9299	-
93.4588	104300	8.9388	-
93.5484	104400	8.9516	-
93.6380	104500	8.9499	8.9002
93.7276	104600	8.9494	-
93.8172	104700	8.9429	-
93.9068	104800	8.9603	-
93.9964	104900	8.9329	-
94.0860	105000	8.9276	8.9027
94.1756	105100	8.9405	-
94.2652	105200	8.9443	-
94.3548	105300	8.9327	-
94.4444	105400	8.943	-
94.5341	105500	8.9391	8.9027
94.6237	105600	8.9516	-
94.7133	105700	8.9461	-
94.8029	105800	8.9365	-
94.8925	105900	8.9509	-
94.9821	106000	8.9475	8.9004
95.0717	106100	8.9494	-
95.1613	106200	8.9391	-
95.2509	106300	8.9413	-
95.3405	106400	8.9472	-
95.4301	106500	8.9407	8.9006
95.5197	106600	8.9386	-
95.6093	106700	8.9408	-
95.6989	106800	8.9464	-
95.7885	106900	8.9333	-
95.8781	107000	8.9364	8.9010
95.9677	107100	8.948	-
96.0573	107200	8.9316	-
96.1470	107300	8.9466	-
96.2366	107400	8.9462	-
96.3262	107500	8.9476	8.9006
96.4158	107600	8.9505	-
96.5054	107700	8.9384	-
96.5950	107800	8.9505	-
96.6846	107900	8.943	-
96.7742	108000	8.9334	8.9012
96.8638	108100	8.9232	-
96.9534	108200	8.9421	-
97.0430	108300	8.9428	-
97.1326	108400	8.9505	-
97.2222	108500	8.9579	8.9002
97.3118	108600	8.9196	-
97.4014	108700	8.9409	-
97.4910	108800	8.9329	-
97.5806	108900	8.9428	-
97.6703	109000	8.9406	8.9002
97.7599	109100	8.9333	-
97.8495	109200	8.9588	-
97.9391	109300	8.9325	-
98.0287	109400	8.9477	-
98.1183	109500	8.9393	8.9001
98.2079	109600	8.9399	-
98.2975	109700	8.935	-
98.3871	109800	8.9365	-
98.4767	109900	8.9471	-
98.5663	110000	8.927	8.9004
98.6559	110100	8.9364	-
98.7455	110200	8.9392	-
98.8351	110300	8.941	-
98.9247	110400	8.9443	-
99.0143	110500	8.9502	8.9004
99.1039	110600	8.9455	-
99.1935	110700	8.9236	-
99.2832	110800	8.9275	-
99.3728	110900	8.9575	-
99.4624	111000	8.9531	8.9001
99.5520	111100	8.9302	-
99.6416	111200	8.9412	-
99.7312	111300	8.9438	-
99.8208	111400	8.955	-
99.9104	111500	8.9411	8.9003
100.0	111600	8.9361	-

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.7.0+cu126
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}