Mihaiii/test34

SentenceTransformer based on Mihaiii/Venusaur

This is a sentence-transformers model finetuned from Mihaiii/Venusaur on the Mihaiii/qa-assistant-2 dataset. 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: Mihaiii/Venusaur
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • Mihaiii/qa-assistant-2
• Language: en

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: 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})
)

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 = [
    'What are the social structures of ants?',
    'The social hierarchy of ants is a complex system that ensures the survival and efficiency of the colony.',
    'In a parliamentary system, the executive branch derives its legitimacy from and is accountable to the legislature; the executive and legislative branches are thus interconnected.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8226
spearman_cosine	0.8216
pearson_manhattan	0.8141
spearman_manhattan	0.819
pearson_euclidean	0.8165
spearman_euclidean	0.8213
pearson_dot	0.8076
spearman_dot	0.8113
pearson_max	0.8226
spearman_max	0.8216

Training Details

Training Dataset

Mihaiii/qa-assistant-2

• Dataset: Mihaiii/qa-assistant-2 at 9650e69
• Size: 16,011 training samples
• Columns: question, answer, and score
• Approximate statistics based on the first 1000 samples:

  	question	answer	score
  type	string	string	float
  details	min: 6 tokens mean: 12.73 tokens max: 27 tokens	min: 10 tokens mean: 22.42 tokens max: 65 tokens	min: 0.02 mean: 0.53 max: 1.0

• Samples:

  question	answer	score
  Can you describe the process of robot path planning?	Robots can be programmed to perform a variety of tasks, from simple repetitive actions to complex decision-making processes.	0.27999999999999997
  Can humans live on Mars?	Mars is the fourth planet from the Sun and is often called the Red Planet due to its reddish appearance.	0.16
  What are the key elements of composition in abstract art?	The history of abstract art dates back to the early 20th century, with pioneers like Wassily Kandinsky and Piet Mondrian.	0.36

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

Mihaiii/qa-assistant-2

• Dataset: Mihaiii/qa-assistant-2 at 9650e69
• Size: 3,879 evaluation samples
• Columns: question, answer, and score
• Approximate statistics based on the first 1000 samples:

  	question	answer	score
  type	string	string	float
  details	min: 7 tokens mean: 12.71 tokens max: 31 tokens	min: 10 tokens mean: 22.63 tokens max: 51 tokens	min: 0.02 mean: 0.53 max: 1.0

• Samples:

  question	answer	score
  What is the concept of social stratification?	The study of social stratification involves examining the inequalities and divisions within a society.	0.6799999999999999
  How does J.K. Rowling develop the character of Hermione Granger throughout the 'Harry Potter' series?	The 'Harry Potter' series consists of seven books, starting with 'Harry Potter and the Philosopher's Stone' and ending with 'Harry Potter and the Deathly Hallows'.	0.22000000000000003
  What is the parliamentary system and how does it function?	In a parliamentary system, the government can be dissolved by a vote of no confidence, which can lead to new elections.	0.6799999999999999

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 100
• warmup_ratio: 0.1

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_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: 0.1
• 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}
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
0.0999	100	0.0612	0.0583	0.5424
0.1998	200	0.0596	0.0574	0.5480
0.2997	300	0.0557	0.0565	0.5567
0.3996	400	0.0583	0.0553	0.5704
0.4995	500	0.0568	0.0537	0.5881
0.5994	600	0.0542	0.0522	0.6046
0.6993	700	0.0525	0.0507	0.6209
0.7992	800	0.0498	0.0495	0.6362
0.8991	900	0.0502	0.0476	0.6518
0.9990	1000	0.0467	0.0465	0.6630
1.0989	1100	0.0437	0.0455	0.6739
1.1988	1200	0.0462	0.0442	0.6860
1.2987	1300	0.0438	0.0433	0.6971
1.3986	1400	0.0459	0.0424	0.7049
1.4985	1500	0.0416	0.0411	0.7156
1.5984	1600	0.0416	0.0402	0.7238
1.6983	1700	0.0385	0.0395	0.7318
1.7982	1800	0.0397	0.0386	0.7416
1.8981	1900	0.0358	0.0378	0.7452
1.9980	2000	0.0374	0.0371	0.7527
2.0979	2100	0.0368	0.0371	0.7569
2.1978	2200	0.0349	0.0363	0.7624
2.2977	2300	0.035	0.0352	0.7687
2.3976	2400	0.0315	0.0346	0.7726
2.4975	2500	0.033	0.0339	0.7775
2.5974	2600	0.0328	0.0330	0.7824
2.6973	2700	0.0327	0.0326	0.7856
2.7972	2800	0.0301	0.0322	0.7897
2.8971	2900	0.031	0.0325	0.7910
2.9970	3000	0.0296	0.0315	0.7918
3.0969	3100	0.0273	0.0313	0.7946
3.1968	3200	0.0288	0.0311	0.7994
3.2967	3300	0.027	0.0318	0.7986
3.3966	3400	0.0279	0.0301	0.8028
3.4965	3500	0.0261	0.0298	0.8040
3.5964	3600	0.027	0.0299	0.8045
3.6963	3700	0.0268	0.0303	0.8024
3.7962	3800	0.028	0.0290	0.8090
3.8961	3900	0.0285	0.0291	0.8079
3.9960	4000	0.0268	0.0291	0.8098
4.0959	4100	0.0244	0.0290	0.8085
4.1958	4200	0.0269	0.0291	0.8086
4.2957	4300	0.0243	0.0287	0.8114
4.3956	4400	0.0244	0.0312	0.8105
4.4955	4500	0.0237	0.0280	0.8148
4.5954	4600	0.0228	0.0285	0.8164
4.6953	4700	0.0228	0.0278	0.8173
4.7952	4800	0.0231	0.0280	0.8151
4.8951	4900	0.0233	0.0284	0.8203
4.9950	5000	0.0242	0.0269	0.8229
5.0949	5100	0.0212	0.0271	0.8214
5.1948	5200	0.0199	0.0276	0.8208
5.2947	5300	0.02	0.0276	0.8191
5.3946	5400	0.021	0.0286	0.8182
5.4945	5500	0.0213	0.0270	0.8215
5.5944	5600	0.0203	0.0271	0.8210
5.6943	5700	0.0203	0.0266	0.8234
5.7942	5800	0.0216	0.0271	0.8245
5.8941	5900	0.0215	0.0271	0.8223
5.9940	6000	0.0204	0.0271	0.8218
6.0939	6100	0.0177	0.0263	0.8256
6.1938	6200	0.0181	0.0267	0.8232
6.2937	6300	0.0187	0.0264	0.8246
6.3936	6400	0.0179	0.0265	0.8237
6.4935	6500	0.0176	0.0272	0.8232
6.5934	6600	0.0185	0.0263	0.8258
6.6933	6700	0.0188	0.0261	0.8289
6.7932	6800	0.0184	0.0264	0.8272
6.8931	6900	0.0192	0.0264	0.8264
6.9930	7000	0.0169	0.0265	0.8248
7.0929	7100	0.0137	0.0266	0.8259
7.1928	7200	0.0153	0.0262	0.8269
7.2927	7300	0.0141	0.0266	0.8238
7.3926	7400	0.0158	0.0266	0.8247
7.4925	7500	0.0162	0.0264	0.8262
7.5924	7600	0.0142	0.0264	0.8253
7.6923	7700	0.0168	0.0263	0.8261
7.7922	7800	0.0149	0.0260	0.8276
7.8921	7900	0.0158	0.0270	0.8230
7.9920	8000	0.0171	0.0268	0.8267
8.0919	8100	0.0131	0.0261	0.8288
8.1918	8200	0.012	0.0263	0.8282
8.2917	8300	0.0128	0.0267	0.8239
8.3916	8400	0.0131	0.0262	0.8250
8.4915	8500	0.0133	0.0258	0.8290
8.5914	8600	0.0139	0.0259	0.8285
8.6913	8700	0.0139	0.0266	0.8275
8.7912	8800	0.0128	0.0256	0.8306
8.8911	8900	0.0135	0.0257	0.8299
8.9910	9000	0.0132	0.0264	0.8248
9.0909	9100	0.0104	0.0257	0.8287
9.1908	9200	0.0102	0.0265	0.8262
9.2907	9300	0.0106	0.0263	0.8263
9.3906	9400	0.0108	0.0263	0.8239
9.4905	9500	0.0108	0.0271	0.8223
9.5904	9600	0.0107	0.0263	0.8237
9.6903	9700	0.0115	0.0264	0.8253
9.7902	9800	0.0121	0.0263	0.8261
9.8901	9900	0.012	0.0266	0.8240
9.9900	10000	0.0117	0.0266	0.8234
10.0899	10100	0.0091	0.0275	0.8222
10.1898	10200	0.0086	0.0263	0.8250
10.2897	10300	0.0089	0.0266	0.8237
10.3896	10400	0.0088	0.0265	0.8233
10.4895	10500	0.009	0.0261	0.8269
10.5894	10600	0.0097	0.0280	0.8214
10.6893	10700	0.0101	0.0268	0.8245
10.7892	10800	0.0096	0.0265	0.8230
10.8891	10900	0.0092	0.0268	0.8234
10.9890	11000	0.0099	0.0269	0.8212
11.0889	11100	0.0081	0.0273	0.8232
11.1888	11200	0.0069	0.0263	0.8256
11.2887	11300	0.0072	0.0263	0.8254
11.3886	11400	0.0078	0.0265	0.8249
11.4885	11500	0.0077	0.0264	0.8250
11.5884	11600	0.0075	0.0266	0.8247
11.6883	11700	0.0078	0.0270	0.8199
11.7882	11800	0.0085	0.0270	0.8214
11.8881	11900	0.0079	0.0272	0.8206
11.9880	12000	0.008	0.0270	0.8213
12.0879	12100	0.0059	0.0267	0.8243
12.1878	12200	0.0058	0.0267	0.8223
12.2877	12300	0.0063	0.0269	0.8219
12.3876	12400	0.0064	0.0264	0.8255
12.4875	12500	0.0062	0.0269	0.8225
12.5874	12600	0.0066	0.0271	0.8211
12.6873	12700	0.0067	0.0271	0.8220
12.7872	12800	0.0072	0.0272	0.8218
12.8871	12900	0.0071	0.0269	0.8225
12.9870	13000	0.0065	0.0265	0.8231
13.0869	13100	0.0052	0.0270	0.8244
13.1868	13200	0.0052	0.0266	0.8223
13.2867	13300	0.0054	0.0265	0.8244
13.3866	13400	0.0057	0.0269	0.8228
13.4865	13500	0.0055	0.0278	0.8227
13.5864	13600	0.0061	0.0270	0.8231
13.6863	13700	0.0056	0.0272	0.8197
13.7862	13800	0.0053	0.0269	0.8231
13.8861	13900	0.0059	0.0271	0.8238
13.9860	14000	0.006	0.0272	0.8208
14.0859	14100	0.0047	0.0267	0.8242
14.1858	14200	0.0044	0.0270	0.8231
14.2857	14300	0.0046	0.0272	0.8219
14.3856	14400	0.0048	0.0272	0.8223
14.4855	14500	0.0049	0.0269	0.8204
14.5854	14600	0.0046	0.0272	0.8209
14.6853	14700	0.0049	0.0274	0.8200
14.7852	14800	0.0048	0.0276	0.8211
14.8851	14900	0.005	0.0271	0.8211
14.9850	15000	0.0055	0.0270	0.8204
15.0849	15100	0.0039	0.0270	0.8215
15.1848	15200	0.0039	0.0272	0.8217
15.2847	15300	0.0039	0.0269	0.8212
15.3846	15400	0.0043	0.0268	0.8228
15.4845	15500	0.0043	0.0272	0.8210
15.5844	15600	0.0043	0.0274	0.8212
15.6843	15700	0.0045	0.0271	0.8211
15.7842	15800	0.0048	0.0274	0.8183
15.8841	15900	0.0049	0.0272	0.8219
15.9840	16000	0.0045	0.0273	0.8215
16.0839	16100	0.0035	0.0268	0.8223
16.1838	16200	0.0039	0.0269	0.8227
16.2837	16300	0.0038	0.0269	0.8208
16.3836	16400	0.0034	0.0271	0.8204
16.4835	16500	0.004	0.0272	0.8189
16.5834	16600	0.004	0.0270	0.8224
16.6833	16700	0.0041	0.0272	0.8210
16.7832	16800	0.0046	0.0279	0.8204
16.8831	16900	0.0041	0.0275	0.8202
16.9830	17000	0.0041	0.0275	0.8199
17.0829	17100	0.0035	0.0272	0.8208
17.1828	17200	0.0036	0.0272	0.8198
17.2827	17300	0.0035	0.0274	0.8181
17.3826	17400	0.0033	0.0273	0.8196
17.4825	17500	0.0038	0.0274	0.8201
17.5824	17600	0.0034	0.0274	0.8184
17.6823	17700	0.004	0.0276	0.8192
17.7822	17800	0.0034	0.0274	0.8201
17.8821	17900	0.0038	0.0273	0.8193
17.9820	18000	0.0037	0.0275	0.8189
18.0819	18100	0.0032	0.0273	0.8189
18.1818	18200	0.0031	0.0273	0.8187
18.2817	18300	0.0031	0.0273	0.8199
18.3816	18400	0.0033	0.0280	0.8167
18.4815	18500	0.0035	0.0280	0.8187
18.5814	18600	0.0033	0.0277	0.8191
18.6813	18700	0.0033	0.0275	0.8184
18.7812	18800	0.0033	0.0277	0.8187
18.8811	18900	0.0034	0.0274	0.8176
18.9810	19000	0.0034	0.0273	0.8183
19.0809	19100	0.0028	0.0273	0.8192
19.1808	19200	0.003	0.0270	0.8213
19.2807	19300	0.003	0.0272	0.8185
19.3806	19400	0.0032	0.0277	0.8187
19.4805	19500	0.0031	0.0274	0.8208
19.5804	19600	0.0029	0.0276	0.8218
19.6803	19700	0.003	0.0276	0.8169
19.7802	19800	0.0032	0.0278	0.8152
19.8801	19900	0.0029	0.0280	0.8154
19.9800	20000	0.003	0.0274	0.8183
20.0799	20100	0.0029	0.0271	0.8193
20.1798	20200	0.0027	0.0272	0.8196
20.2797	20300	0.0027	0.0272	0.8190
20.3796	20400	0.0027	0.0276	0.8176
20.4795	20500	0.0031	0.0274	0.8186
20.5794	20600	0.003	0.0277	0.8180
20.6793	20700	0.0027	0.0279	0.8158
20.7792	20800	0.0029	0.0276	0.8174
20.8791	20900	0.0028	0.0273	0.8186
20.9790	21000	0.003	0.0270	0.8203
21.0789	21100	0.0026	0.0275	0.8192
21.1788	21200	0.0026	0.0275	0.8202
21.2787	21300	0.0027	0.0273	0.8174
21.3786	21400	0.0026	0.0270	0.8217
21.4785	21500	0.0028	0.0275	0.8185
21.5784	21600	0.0028	0.0279	0.8175
21.6783	21700	0.0027	0.0276	0.8175
21.7782	21800	0.0028	0.0276	0.8188
21.8781	21900	0.0028	0.0274	0.8190
21.9780	22000	0.0027	0.0275	0.8184
22.0779	22100	0.0023	0.0271	0.8210
22.1778	22200	0.0024	0.0273	0.8191
22.2777	22300	0.0025	0.0276	0.8167
22.3776	22400	0.0025	0.0272	0.8189
22.4775	22500	0.0025	0.0273	0.8190
22.5774	22600	0.0024	0.0272	0.8192
22.6773	22700	0.0025	0.0274	0.8181
22.7772	22800	0.0026	0.0277	0.8176
22.8771	22900	0.0025	0.0275	0.8200
22.9770	23000	0.0027	0.0274	0.8173
23.0769	23100	0.0025	0.0272	0.8200
23.1768	23200	0.0024	0.0277	0.8153
23.2767	23300	0.0022	0.0274	0.8170
23.3766	23400	0.0023	0.0274	0.8178
23.4765	23500	0.0023	0.0274	0.8188
23.5764	23600	0.0024	0.0275	0.8178
23.6763	23700	0.0024	0.0276	0.8167
23.7762	23800	0.0022	0.0279	0.8179
23.8761	23900	0.0023	0.0278	0.8154
23.9760	24000	0.0022	0.0278	0.8172
24.0759	24100	0.0023	0.0272	0.8185
24.1758	24200	0.0021	0.0271	0.8195
24.2757	24300	0.0023	0.0275	0.8183
24.3756	24400	0.0021	0.0277	0.8164
24.4755	24500	0.0023	0.0274	0.8169
24.5754	24600	0.0023	0.0273	0.8175
24.6753	24700	0.0024	0.0274	0.8199
24.7752	24800	0.0022	0.0277	0.8161
24.8751	24900	0.0025	0.0274	0.8173
24.9750	25000	0.0025	0.0274	0.8185
25.0749	25100	0.0021	0.0270	0.8191
25.1748	25200	0.0021	0.0272	0.8180
25.2747	25300	0.002	0.0273	0.8187
25.3746	25400	0.002	0.0271	0.8211
25.4745	25500	0.002	0.0272	0.8181
25.5744	25600	0.0024	0.0274	0.8176
25.6743	25700	0.002	0.0270	0.8197
25.7742	25800	0.0022	0.0271	0.8201
25.8741	25900	0.0022	0.0276	0.8171
25.9740	26000	0.0024	0.0274	0.8188
26.0739	26100	0.0019	0.0276	0.8156
26.1738	26200	0.0019	0.0274	0.8177
26.2737	26300	0.002	0.0273	0.8190
26.3736	26400	0.0019	0.0270	0.8204
26.4735	26500	0.002	0.0270	0.8207
26.5734	26600	0.002	0.0271	0.8196
26.6733	26700	0.0021	0.0272	0.8198
26.7732	26800	0.0022	0.0273	0.8207
26.8731	26900	0.0022	0.0272	0.8220
26.9730	27000	0.0022	0.0270	0.8212
27.0729	27100	0.0019	0.0270	0.8212
27.1728	27200	0.0018	0.0270	0.8209
27.2727	27300	0.0018	0.0268	0.8208
27.3726	27400	0.0019	0.0271	0.8200
27.4725	27500	0.0019	0.0272	0.8191
27.5724	27600	0.0018	0.0271	0.8192
27.6723	27700	0.0019	0.0274	0.8190
27.7722	27800	0.0019	0.0274	0.8180
27.8721	27900	0.002	0.0277	0.8168
27.9720	28000	0.0021	0.0272	0.8196
28.0719	28100	0.002	0.0269	0.8214
28.1718	28200	0.0017	0.0270	0.8212
28.2717	28300	0.0018	0.0270	0.8198
28.3716	28400	0.0019	0.0274	0.8187
28.4715	28500	0.0017	0.0273	0.8188
28.5714	28600	0.0018	0.0271	0.8199
28.6713	28700	0.0018	0.0270	0.8202
28.7712	28800	0.002	0.0270	0.8210
28.8711	28900	0.0019	0.0271	0.8202
28.9710	29000	0.002	0.0268	0.8213
29.0709	29100	0.0017	0.0267	0.8226
29.1708	29200	0.0017	0.0268	0.8228
29.2707	29300	0.0018	0.0269	0.8212
29.3706	29400	0.0019	0.0270	0.8206
29.4705	29500	0.0018	0.0270	0.8201
29.5704	29600	0.0018	0.0269	0.8212
29.6703	29700	0.0018	0.0269	0.8206
29.7702	29800	0.0018	0.0271	0.8207
29.8701	29900	0.0019	0.0269	0.8199
29.9700	30000	0.0018	0.0269	0.8205
30.0699	30100	0.0017	0.0269	0.8206
30.1698	30200	0.0017	0.0272	0.8192
30.2697	30300	0.0018	0.0272	0.8208
30.3696	30400	0.0016	0.0267	0.8219
30.4695	30500	0.0017	0.0270	0.8205
30.5694	30600	0.0017	0.0273	0.8172
30.6693	30700	0.0017	0.0272	0.8200
30.7692	30800	0.002	0.0271	0.8190
30.8691	30900	0.0018	0.0272	0.8185
30.9690	31000	0.0017	0.0268	0.8202
31.0689	31100	0.0016	0.0270	0.8200
31.1688	31200	0.0017	0.0268	0.8213
31.2687	31300	0.0016	0.0270	0.8198
31.3686	31400	0.0016	0.0269	0.8211
31.4685	31500	0.0017	0.0269	0.8212
31.5684	31600	0.0016	0.0270	0.8209
31.6683	31700	0.0017	0.0269	0.8209
31.7682	31800	0.0019	0.0270	0.8195
31.8681	31900	0.0017	0.0271	0.8197
31.9680	32000	0.0017	0.0270	0.8191
32.0679	32100	0.0014	0.0271	0.8189
32.1678	32200	0.0014	0.0271	0.8200
32.2677	32300	0.0015	0.0270	0.8199
32.3676	32400	0.0015	0.0271	0.8187
32.4675	32500	0.0016	0.0275	0.8181
32.5674	32600	0.0017	0.0273	0.8188
32.6673	32700	0.0016	0.0272	0.8193
32.7672	32800	0.0014	0.0273	0.8189
32.8671	32900	0.0016	0.0276	0.8166
32.9670	33000	0.0018	0.0274	0.8181
33.0669	33100	0.0017	0.0271	0.8189
33.1668	33200	0.0016	0.0273	0.8181
33.2667	33300	0.0015	0.0271	0.8186
33.3666	33400	0.0016	0.0271	0.8193
33.4665	33500	0.0014	0.0271	0.8187
33.5664	33600	0.0017	0.0270	0.8190
33.6663	33700	0.0015	0.0270	0.8190
33.7662	33800	0.0015	0.0270	0.8196
33.8661	33900	0.0015	0.0271	0.8209
33.9660	34000	0.0016	0.0270	0.8201
34.0659	34100	0.0017	0.0269	0.8199
34.1658	34200	0.0014	0.0269	0.8198
34.2657	34300	0.0014	0.0271	0.8207
34.3656	34400	0.0015	0.0271	0.8198
34.4655	34500	0.0014	0.0273	0.8188
34.5654	34600	0.0016	0.0271	0.8193
34.6653	34700	0.0016	0.0268	0.8216
34.7652	34800	0.0014	0.0272	0.8186
34.8651	34900	0.0016	0.0271	0.8180
34.9650	35000	0.0015	0.0272	0.8185
35.0649	35100	0.0014	0.0271	0.8191
35.1648	35200	0.0015	0.0270	0.8197
35.2647	35300	0.0013	0.0269	0.8204
35.3646	35400	0.0014	0.0267	0.8215
35.4645	35500	0.0013	0.0270	0.8196
35.5644	35600	0.0015	0.0271	0.8190
35.6643	35700	0.0015	0.0270	0.8200
35.7642	35800	0.0014	0.0267	0.8204
35.8641	35900	0.0014	0.0269	0.8206
35.9640	36000	0.0014	0.0270	0.8194
36.0639	36100	0.0014	0.0270	0.8196
36.1638	36200	0.0013	0.0268	0.8203
36.2637	36300	0.0014	0.0269	0.8207
36.3636	36400	0.0013	0.0271	0.8188
36.4635	36500	0.0014	0.0272	0.8183
36.5634	36600	0.0014	0.0271	0.8199
36.6633	36700	0.0014	0.0270	0.8191
36.7632	36800	0.0013	0.0268	0.8205
36.8631	36900	0.0016	0.0271	0.8192
36.9630	37000	0.0013	0.0269	0.8207
37.0629	37100	0.0013	0.0268	0.8208
37.1628	37200	0.0013	0.0267	0.8217
37.2627	37300	0.0013	0.0268	0.8211
37.3626	37400	0.0013	0.0268	0.8223
37.4625	37500	0.0013	0.0267	0.8215
37.5624	37600	0.0015	0.0268	0.8214
37.6623	37700	0.0013	0.0269	0.8199
37.7622	37800	0.0013	0.0271	0.8192
37.8621	37900	0.0015	0.0274	0.8184
37.9620	38000	0.0013	0.0271	0.8192
38.0619	38100	0.0013	0.0268	0.8201
38.1618	38200	0.0012	0.0269	0.8197
38.2617	38300	0.0013	0.0267	0.8211
38.3616	38400	0.0012	0.0272	0.8177
38.4615	38500	0.0013	0.0270	0.8192
38.5614	38600	0.0014	0.0268	0.8214
38.6613	38700	0.0013	0.0266	0.8214
38.7612	38800	0.0014	0.0268	0.8207
38.8611	38900	0.0015	0.0271	0.8195
38.9610	39000	0.0014	0.0269	0.8204
39.0609	39100	0.0013	0.0270	0.8204
39.1608	39200	0.0014	0.0271	0.8195
39.2607	39300	0.0012	0.0270	0.8202
39.3606	39400	0.0012	0.0272	0.8196
39.4605	39500	0.0011	0.0270	0.8187
39.5604	39600	0.0013	0.0269	0.8194
39.6603	39700	0.0012	0.0271	0.8188
39.7602	39800	0.0013	0.0269	0.8199
39.8601	39900	0.0014	0.0270	0.8206
39.9600	40000	0.0013	0.0269	0.8200
40.0599	40100	0.0013	0.0268	0.8209
40.1598	40200	0.0013	0.0269	0.8207
40.2597	40300	0.0012	0.0271	0.8194
40.3596	40400	0.0013	0.0272	0.8194
40.4595	40500	0.0012	0.0271	0.8189
40.5594	40600	0.0013	0.0273	0.8178
40.6593	40700	0.0013	0.0272	0.8177
40.7592	40800	0.0012	0.0272	0.8174
40.8591	40900	0.0013	0.0271	0.8188
40.9590	41000	0.0012	0.0273	0.8186
41.0589	41100	0.0013	0.0272	0.8178
41.1588	41200	0.0011	0.0269	0.8199
41.2587	41300	0.0012	0.0267	0.8204
41.3586	41400	0.0012	0.0269	0.8204
41.4585	41500	0.0011	0.0271	0.8188
41.5584	41600	0.0012	0.0269	0.8207
41.6583	41700	0.0013	0.0271	0.8179
41.7582	41800	0.0011	0.0270	0.8200
41.8581	41900	0.0013	0.0273	0.8184
41.9580	42000	0.0014	0.0272	0.8182
42.0579	42100	0.0012	0.0269	0.8210
42.1578	42200	0.0011	0.0269	0.8208
42.2577	42300	0.0013	0.0268	0.8220
42.3576	42400	0.0013	0.0267	0.8208
42.4575	42500	0.0012	0.0265	0.8228
42.5574	42600	0.0011	0.0267	0.8207
42.6573	42700	0.0011	0.0268	0.8208
42.7572	42800	0.0011	0.0267	0.8215
42.8571	42900	0.0012	0.0267	0.8217
42.9570	43000	0.0011	0.0269	0.8208
43.0569	43100	0.0011	0.0268	0.8208
43.1568	43200	0.0011	0.0267	0.8212
43.2567	43300	0.0011	0.0266	0.8214
43.3566	43400	0.0011	0.0268	0.8213
43.4565	43500	0.0012	0.0269	0.8206
43.5564	43600	0.0011	0.0268	0.8207
43.6563	43700	0.0012	0.0269	0.8199
43.7562	43800	0.0011	0.0270	0.8201
43.8561	43900	0.0011	0.0268	0.8201
43.9560	44000	0.0013	0.0269	0.8194
44.0559	44100	0.0011	0.0271	0.8181
44.1558	44200	0.0011	0.0269	0.8196
44.2557	44300	0.0011	0.0270	0.8189
44.3556	44400	0.0012	0.0270	0.8194
44.4555	44500	0.0011	0.0270	0.8189
44.5554	44600	0.0011	0.0269	0.8199
44.6553	44700	0.0011	0.0270	0.8200
44.7552	44800	0.0012	0.0270	0.8193
44.8551	44900	0.0011	0.0267	0.8203
44.9550	45000	0.0011	0.0269	0.8201
45.0549	45100	0.0011	0.0268	0.8204
45.1548	45200	0.001	0.0267	0.8215
45.2547	45300	0.0012	0.0265	0.8229
45.3546	45400	0.0011	0.0266	0.8218
45.4545	45500	0.0013	0.0266	0.8229
45.5544	45600	0.0011	0.0267	0.8220
45.6543	45700	0.0011	0.0266	0.8212
45.7542	45800	0.0011	0.0268	0.8213
45.8541	45900	0.0012	0.0265	0.8227
45.9540	46000	0.001	0.0266	0.8223
46.0539	46100	0.001	0.0266	0.8211
46.1538	46200	0.001	0.0266	0.8209
46.2537	46300	0.001	0.0268	0.8212
46.3536	46400	0.001	0.0268	0.8202
46.4535	46500	0.001	0.0267	0.8211
46.5534	46600	0.0011	0.0268	0.8213
46.6533	46700	0.001	0.0268	0.8211
46.7532	46800	0.0011	0.0269	0.8198
46.8531	46900	0.001	0.0267	0.8213
46.9530	47000	0.0013	0.0266	0.8213
47.0529	47100	0.001	0.0265	0.8221
47.1528	47200	0.001	0.0266	0.8222
47.2527	47300	0.0012	0.0267	0.8222
47.3526	47400	0.0011	0.0266	0.8224
47.4525	47500	0.0011	0.0267	0.8212
47.5524	47600	0.001	0.0268	0.8212
47.6523	47700	0.001	0.0268	0.8209
47.7522	47800	0.0011	0.0268	0.8199
47.8521	47900	0.0011	0.0267	0.8200
47.9520	48000	0.001	0.0268	0.8208
48.0519	48100	0.0011	0.0269	0.8201
48.1518	48200	0.0011	0.0267	0.8208
48.2517	48300	0.0011	0.0268	0.8207
48.3516	48400	0.001	0.0266	0.8220
48.4515	48500	0.001	0.0267	0.8214
48.5514	48600	0.0011	0.0266	0.8215
48.6513	48700	0.0011	0.0267	0.8212
48.7512	48800	0.0011	0.0267	0.8212
48.8511	48900	0.001	0.0266	0.8218
48.9510	49000	0.001	0.0266	0.8228
49.0509	49100	0.001	0.0266	0.8224
49.1508	49200	0.001	0.0266	0.8216
49.2507	49300	0.001	0.0267	0.8210
49.3506	49400	0.001	0.0267	0.8219
49.4505	49500	0.001	0.0270	0.8192
49.5504	49600	0.001	0.0270	0.8201
49.6503	49700	0.001	0.0269	0.8200
49.7502	49800	0.0009	0.0268	0.8207
49.8501	49900	0.001	0.0268	0.8204
49.9500	50000	0.001	0.0270	0.8199
50.0500	50100	0.001	0.0269	0.8197
50.1499	50200	0.0009	0.0269	0.8199
50.2498	50300	0.001	0.0268	0.8205
50.3497	50400	0.0009	0.0269	0.8201
50.4496	50500	0.001	0.0269	0.8195
50.5495	50600	0.001	0.0267	0.8203
50.6494	50700	0.001	0.0268	0.8211
50.7493	50800	0.001	0.0267	0.8204
50.8492	50900	0.001	0.0268	0.8214
50.9491	51000	0.001	0.0267	0.8207
51.0490	51100	0.001	0.0267	0.8211
51.1489	51200	0.0009	0.0267	0.8208
51.2488	51300	0.001	0.0266	0.8213
51.3487	51400	0.001	0.0268	0.8208
51.4486	51500	0.001	0.0266	0.8216
51.5485	51600	0.001	0.0267	0.8200
51.6484	51700	0.001	0.0267	0.8208
51.7483	51800	0.001	0.0268	0.8208
51.8482	51900	0.001	0.0268	0.8212
51.9481	52000	0.001	0.0266	0.8216
52.0480	52100	0.0009	0.0267	0.8208
52.1479	52200	0.0009	0.0266	0.8215
52.2478	52300	0.001	0.0264	0.8222
52.3477	52400	0.0009	0.0265	0.8217
52.4476	52500	0.0011	0.0266	0.8214
52.5475	52600	0.0009	0.0267	0.8210
52.6474	52700	0.0009	0.0267	0.8214
52.7473	52800	0.0009	0.0267	0.8210
52.8472	52900	0.0009	0.0268	0.8203
52.9471	53000	0.0009	0.0269	0.8199
53.0470	53100	0.0009	0.0267	0.8206
53.1469	53200	0.001	0.0269	0.8198
53.2468	53300	0.0009	0.0268	0.8205
53.3467	53400	0.001	0.0267	0.8210
53.4466	53500	0.001	0.0267	0.8211
53.5465	53600	0.0009	0.0267	0.8210
53.6464	53700	0.0009	0.0265	0.8218
53.7463	53800	0.0009	0.0267	0.8204
53.8462	53900	0.0009	0.0266	0.8219
53.9461	54000	0.001	0.0268	0.8196
54.0460	54100	0.0008	0.0269	0.8196
54.1459	54200	0.0009	0.0268	0.8210
54.2458	54300	0.0009	0.0268	0.8201
54.3457	54400	0.0009	0.0267	0.8210
54.4456	54500	0.0009	0.0267	0.8206
54.5455	54600	0.0009	0.0268	0.8199
54.6454	54700	0.0008	0.0268	0.8202
54.7453	54800	0.001	0.0269	0.8195
54.8452	54900	0.0009	0.0269	0.8188
54.9451	55000	0.0009	0.0269	0.8195
55.0450	55100	0.001	0.0266	0.8210
55.1449	55200	0.0009	0.0268	0.8203
55.2448	55300	0.0009	0.0268	0.8209
55.3447	55400	0.0008	0.0270	0.8196
55.4446	55500	0.0009	0.0268	0.8206
55.5445	55600	0.0009	0.0268	0.8203
55.6444	55700	0.0009	0.0268	0.8205
55.7443	55800	0.0008	0.0267	0.8215
55.8442	55900	0.0009	0.0267	0.8208
55.9441	56000	0.0009	0.0269	0.8203
56.0440	56100	0.0009	0.0268	0.8201
56.1439	56200	0.0008	0.0269	0.8192
56.2438	56300	0.0008	0.0268	0.8203
56.3437	56400	0.0008	0.0268	0.8201
56.4436	56500	0.0008	0.0267	0.8207
56.5435	56600	0.0008	0.0267	0.8212
56.6434	56700	0.0009	0.0268	0.8192
56.7433	56800	0.0009	0.0268	0.8196
56.8432	56900	0.0009	0.0268	0.8207
56.9431	57000	0.0008	0.0268	0.8201
57.0430	57100	0.0008	0.0268	0.8197
57.1429	57200	0.0009	0.0268	0.8205
57.2428	57300	0.0008	0.0268	0.8207
57.3427	57400	0.0009	0.0268	0.8196
57.4426	57500	0.0008	0.0268	0.8201
57.5425	57600	0.0009	0.0268	0.8191
57.6424	57700	0.0008	0.0267	0.8204
57.7423	57800	0.0008	0.0270	0.8189
57.8422	57900	0.0008	0.0268	0.8198
57.9421	58000	0.0008	0.0269	0.8193
58.0420	58100	0.0008	0.0268	0.8201
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93.0070	93100	0.0005	0.0265	0.8216
93.1069	93200	0.0005	0.0265	0.8216
93.2068	93300	0.0005	0.0265	0.8218
93.3067	93400	0.0005	0.0265	0.8221
93.4066	93500	0.0005	0.0265	0.8218
93.5065	93600	0.0005	0.0265	0.8219
93.6064	93700	0.0006	0.0265	0.8220
93.7063	93800	0.0005	0.0265	0.8217
93.8062	93900	0.0005	0.0265	0.8216
93.9061	94000	0.0005	0.0265	0.8215
94.0060	94100	0.0005	0.0265	0.8215
94.1059	94200	0.0005	0.0265	0.8215
94.2058	94300	0.0005	0.0265	0.8217
94.3057	94400	0.0005	0.0265	0.8214
94.4056	94500	0.0006	0.0265	0.8216
94.5055	94600	0.0005	0.0265	0.8217
94.6054	94700	0.0005	0.0265	0.8214
94.7053	94800	0.0005	0.0265	0.8216
94.8052	94900	0.0005	0.0265	0.8219
94.9051	95000	0.0005	0.0265	0.8216
95.0050	95100	0.0005	0.0265	0.8217
95.1049	95200	0.0006	0.0265	0.8217
95.2048	95300	0.0005	0.0265	0.8218
95.3047	95400	0.0005	0.0265	0.8220
95.4046	95500	0.0005	0.0265	0.8220
95.5045	95600	0.0005	0.0265	0.8220
95.6044	95700	0.0005	0.0265	0.8219
95.7043	95800	0.0005	0.0265	0.8220
95.8042	95900	0.0004	0.0265	0.8218
95.9041	96000	0.0004	0.0265	0.8219
96.0040	96100	0.0005	0.0265	0.8218
96.1039	96200	0.0005	0.0265	0.8219
96.2038	96300	0.0005	0.0265	0.8219
96.3037	96400	0.0006	0.0265	0.8218
96.4036	96500	0.0005	0.0265	0.8219
96.5035	96600	0.0005	0.0265	0.8215
96.6034	96700	0.0005	0.0265	0.8215
96.7033	96800	0.0005	0.0265	0.8215
96.8032	96900	0.0005	0.0265	0.8214
96.9031	97000	0.0005	0.0265	0.8216
97.0030	97100	0.0005	0.0265	0.8217
97.1029	97200	0.0004	0.0265	0.8216
97.2028	97300	0.0005	0.0265	0.8218
97.3027	97400	0.0005	0.0265	0.8218
97.4026	97500	0.0005	0.0265	0.8218
97.5025	97600	0.0005	0.0265	0.8217
97.6024	97700	0.0005	0.0265	0.8217
97.7023	97800	0.0005	0.0265	0.8216
97.8022	97900	0.0005	0.0265	0.8217
97.9021	98000	0.0005	0.0265	0.8217
98.0020	98100	0.0006	0.0265	0.8215
98.1019	98200	0.0005	0.0265	0.8216
98.2018	98300	0.0005	0.0265	0.8217
98.3017	98400	0.0005	0.0265	0.8216
98.4016	98500	0.0005	0.0265	0.8216
98.5015	98600	0.0005	0.0265	0.8216
98.6014	98700	0.0005	0.0265	0.8216
98.7013	98800	0.0005	0.0265	0.8215
98.8012	98900	0.0005	0.0265	0.8216
98.9011	99000	0.0005	0.0265	0.8216
99.0010	99100	0.0005	0.0265	0.8216
99.1009	99200	0.0005	0.0265	0.8217
99.2008	99300	0.0005	0.0265	0.8217
99.3007	99400	0.0004	0.0265	0.8217
99.4006	99500	0.0005	0.0265	0.8217
99.5005	99600	0.0004	0.0265	0.8216
99.6004	99700	0.0005	0.0265	0.8216
99.7003	99800	0.0005	0.0265	0.8216
99.8002	99900	0.0005	0.0265	0.8216
99.9001	100000	0.0005	0.0265	0.8216
100.0	100100	0.0005	0.0265	0.8216

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.0.1+cu118
• Accelerate: 0.31.0
• Datasets: 2.20.0
• Tokenizers: 0.19.1

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",
}