sobamchan/st5-base-mean-3000

SentenceTransformer based on google-t5/t5-base

This is a sentence-transformers model finetuned from google-t5/t5-base on the all-nli dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: google-t5/t5-base
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • all-nli
• 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': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (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})
  (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 = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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

all-nli

• Dataset: all-nli at d482672
• Size: 557,850 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.96 tokens max: 52 tokens	min: 5 tokens mean: 12.79 tokens max: 44 tokens	min: 4 tokens mean: 14.02 tokens max: 57 tokens

• Samples:

  anchor	positive	negative
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	A person is at a diner, ordering an omelette.
  Children smiling and waving at camera	There are children present	The kids are frowning
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.	The boy skates down the sidewalk.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

all-nli

• Dataset: all-nli at d482672
• Size: 6,584 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 19.41 tokens max: 79 tokens	min: 4 tokens mean: 9.69 tokens max: 35 tokens	min: 4 tokens mean: 10.35 tokens max: 30 tokens

• Samples:

  anchor	positive	negative
  Two women are embracing while holding to go packages.	Two woman are holding packages.	The men are fighting outside a deli.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.	Two kids in jackets walk to school.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.	A woman drinks her coffee in a small cafe.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 1e-05
• warmup_ratio: 0.1
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: 1e-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: 0.1
• 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: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0011	10	-	1.8733
0.0023	20	-	1.8726
0.0034	30	-	1.8714
0.0046	40	-	1.8697
0.0057	50	-	1.8675
0.0069	60	-	1.8649
0.0080	70	-	1.8619
0.0092	80	-	1.8584
0.0103	90	-	1.8544
0.0115	100	3.1046	1.8499
0.0126	110	-	1.8451
0.0138	120	-	1.8399
0.0149	130	-	1.8343
0.0161	140	-	1.8283
0.0172	150	-	1.8223
0.0184	160	-	1.8159
0.0195	170	-	1.8091
0.0206	180	-	1.8016
0.0218	190	-	1.7938
0.0229	200	3.0303	1.7858
0.0241	210	-	1.7775
0.0252	220	-	1.7693
0.0264	230	-	1.7605
0.0275	240	-	1.7514
0.0287	250	-	1.7417
0.0298	260	-	1.7320
0.0310	270	-	1.7227
0.0321	280	-	1.7134
0.0333	290	-	1.7040
0.0344	300	2.9459	1.6941
0.0356	310	-	1.6833
0.0367	320	-	1.6725
0.0379	330	-	1.6614
0.0390	340	-	1.6510
0.0402	350	-	1.6402
0.0413	360	-	1.6296
0.0424	370	-	1.6187
0.0436	380	-	1.6073
0.0447	390	-	1.5962
0.0459	400	2.7813	1.5848
0.0470	410	-	1.5735
0.0482	420	-	1.5620
0.0493	430	-	1.5495
0.0505	440	-	1.5375
0.0516	450	-	1.5256
0.0528	460	-	1.5133
0.0539	470	-	1.5012
0.0551	480	-	1.4892
0.0562	490	-	1.4769
0.0574	500	2.6308	1.4640
0.0585	510	-	1.4513
0.0597	520	-	1.4391
0.0608	530	-	1.4262
0.0619	540	-	1.4130
0.0631	550	-	1.3998
0.0642	560	-	1.3874
0.0654	570	-	1.3752
0.0665	580	-	1.3620
0.0677	590	-	1.3485
0.0688	600	2.4452	1.3350
0.0700	610	-	1.3213
0.0711	620	-	1.3088
0.0723	630	-	1.2965
0.0734	640	-	1.2839
0.0746	650	-	1.2713
0.0757	660	-	1.2592
0.0769	670	-	1.2466
0.0780	680	-	1.2332
0.0792	690	-	1.2203
0.0803	700	2.2626	1.2077
0.0815	710	-	1.1959
0.0826	720	-	1.1841
0.0837	730	-	1.1725
0.0849	740	-	1.1619
0.0860	750	-	1.1516
0.0872	760	-	1.1416
0.0883	770	-	1.1320
0.0895	780	-	1.1227
0.0906	790	-	1.1138
0.0918	800	2.0044	1.1053
0.0929	810	-	1.0965
0.0941	820	-	1.0879
0.0952	830	-	1.0796
0.0964	840	-	1.0718
0.0975	850	-	1.0644
0.0987	860	-	1.0564
0.0998	870	-	1.0490
0.1010	880	-	1.0417
0.1021	890	-	1.0354
0.1032	900	1.8763	1.0296
0.1044	910	-	1.0239
0.1055	920	-	1.0180
0.1067	930	-	1.0123
0.1078	940	-	1.0065
0.1090	950	-	1.0008
0.1101	960	-	0.9950
0.1113	970	-	0.9894
0.1124	980	-	0.9840
0.1136	990	-	0.9793
0.1147	1000	1.7287	0.9752
0.1159	1010	-	0.9706
0.1170	1020	-	0.9659
0.1182	1030	-	0.9615
0.1193	1040	-	0.9572
0.1205	1050	-	0.9531
0.1216	1060	-	0.9494
0.1227	1070	-	0.9456
0.1239	1080	-	0.9415
0.1250	1090	-	0.9377
0.1262	1100	1.6312	0.9339
0.1273	1110	-	0.9303
0.1285	1120	-	0.9267
0.1296	1130	-	0.9232
0.1308	1140	-	0.9197
0.1319	1150	-	0.9162
0.1331	1160	-	0.9128
0.1342	1170	-	0.9097
0.1354	1180	-	0.9069
0.1365	1190	-	0.9040
0.1377	1200	1.5316	0.9010
0.1388	1210	-	0.8979
0.1400	1220	-	0.8947
0.1411	1230	-	0.8915
0.1423	1240	-	0.8888
0.1434	1250	-	0.8861
0.1445	1260	-	0.8833
0.1457	1270	-	0.8806
0.1468	1280	-	0.8779
0.1480	1290	-	0.8748
0.1491	1300	1.4961	0.8718
0.1503	1310	-	0.8690
0.1514	1320	-	0.8664
0.1526	1330	-	0.8635
0.1537	1340	-	0.8603
0.1549	1350	-	0.8574
0.1560	1360	-	0.8545
0.1572	1370	-	0.8521
0.1583	1380	-	0.8497
0.1595	1390	-	0.8474
0.1606	1400	1.451	0.8453
0.1618	1410	-	0.8429
0.1629	1420	-	0.8404
0.1640	1430	-	0.8380
0.1652	1440	-	0.8357
0.1663	1450	-	0.8336
0.1675	1460	-	0.8312
0.1686	1470	-	0.8289
0.1698	1480	-	0.8262
0.1709	1490	-	0.8236
0.1721	1500	1.4177	0.8213
0.1732	1510	-	0.8189
0.1744	1520	-	0.8168
0.1755	1530	-	0.8147
0.1767	1540	-	0.8127
0.1778	1550	-	0.8107
0.1790	1560	-	0.8082
0.1801	1570	-	0.8059
0.1813	1580	-	0.8036
0.1824	1590	-	0.8015
0.1835	1600	1.3734	0.7993
0.1847	1610	-	0.7970
0.1858	1620	-	0.7948
0.1870	1630	-	0.7922
0.1881	1640	-	0.7900
0.1893	1650	-	0.7877
0.1904	1660	-	0.7852
0.1916	1670	-	0.7829
0.1927	1680	-	0.7804
0.1939	1690	-	0.7779
0.1950	1700	1.3327	0.7757
0.1962	1710	-	0.7738
0.1973	1720	-	0.7719
0.1985	1730	-	0.7700
0.1996	1740	-	0.7679
0.2008	1750	-	0.7658
0.2019	1760	-	0.7641
0.2031	1770	-	0.7621
0.2042	1780	-	0.7601
0.2053	1790	-	0.7580
0.2065	1800	1.2804	0.7558
0.2076	1810	-	0.7536
0.2088	1820	-	0.7514
0.2099	1830	-	0.7493
0.2111	1840	-	0.7473
0.2122	1850	-	0.7451
0.2134	1860	-	0.7429
0.2145	1870	-	0.7408
0.2157	1880	-	0.7389
0.2168	1890	-	0.7368
0.2180	1900	1.2255	0.7349
0.2191	1910	-	0.7328
0.2203	1920	-	0.7310
0.2214	1930	-	0.7293
0.2226	1940	-	0.7277
0.2237	1950	-	0.7259
0.2248	1960	-	0.7240
0.2260	1970	-	0.7221
0.2271	1980	-	0.7203
0.2283	1990	-	0.7184
0.2294	2000	1.2635	0.7165
0.2306	2010	-	0.7150
0.2317	2020	-	0.7135
0.2329	2030	-	0.7117
0.2340	2040	-	0.7099
0.2352	2050	-	0.7084
0.2363	2060	-	0.7068
0.2375	2070	-	0.7054
0.2386	2080	-	0.7037
0.2398	2090	-	0.7023
0.2409	2100	1.1912	0.7009
0.2421	2110	-	0.6991
0.2432	2120	-	0.6974
0.2444	2130	-	0.6962
0.2455	2140	-	0.6950
0.2466	2150	-	0.6938
0.2478	2160	-	0.6922
0.2489	2170	-	0.6909
0.2501	2180	-	0.6897
0.2512	2190	-	0.6884
0.2524	2200	1.2144	0.6868
0.2535	2210	-	0.6856
0.2547	2220	-	0.6843
0.2558	2230	-	0.6829
0.2570	2240	-	0.6817
0.2581	2250	-	0.6804
0.2593	2260	-	0.6789
0.2604	2270	-	0.6775
0.2616	2280	-	0.6763
0.2627	2290	-	0.6751
0.2639	2300	1.1498	0.6739
0.2650	2310	-	0.6725
0.2661	2320	-	0.6711
0.2673	2330	-	0.6698
0.2684	2340	-	0.6684
0.2696	2350	-	0.6666
0.2707	2360	-	0.6653
0.2719	2370	-	0.6638
0.2730	2380	-	0.6621
0.2742	2390	-	0.6609
0.2753	2400	1.1446	0.6596
0.2765	2410	-	0.6582
0.2776	2420	-	0.6568
0.2788	2430	-	0.6553
0.2799	2440	-	0.6541
0.2811	2450	-	0.6527
0.2822	2460	-	0.6513
0.2834	2470	-	0.6496
0.2845	2480	-	0.6483
0.2856	2490	-	0.6475
0.2868	2500	1.1309	0.6465
0.2879	2510	-	0.6455
0.2891	2520	-	0.6447
0.2902	2530	-	0.6437
0.2914	2540	-	0.6428
0.2925	2550	-	0.6415
0.2937	2560	-	0.6403
0.2948	2570	-	0.6392
0.2960	2580	-	0.6381
0.2971	2590	-	0.6371
0.2983	2600	1.1006	0.6358
0.2994	2610	-	0.6348
0.3006	2620	-	0.6340
0.3017	2630	-	0.6330
0.3029	2640	-	0.6319
0.3040	2650	-	0.6308
0.3052	2660	-	0.6300
0.3063	2670	-	0.6291
0.3074	2680	-	0.6280
0.3086	2690	-	0.6268
0.3097	2700	1.0772	0.6254
0.3109	2710	-	0.6243
0.3120	2720	-	0.6232
0.3132	2730	-	0.6224
0.3143	2740	-	0.6215
0.3155	2750	-	0.6205
0.3166	2760	-	0.6194
0.3178	2770	-	0.6183
0.3189	2780	-	0.6171
0.3201	2790	-	0.6160
0.3212	2800	1.0648	0.6153
0.3224	2810	-	0.6141
0.3235	2820	-	0.6129
0.3247	2830	-	0.6119
0.3258	2840	-	0.6109
0.3269	2850	-	0.6099
0.3281	2860	-	0.6088
0.3292	2870	-	0.6079
0.3304	2880	-	0.6073
0.3315	2890	-	0.6063
0.3327	2900	1.0398	0.6054
0.3338	2910	-	0.6044
0.3350	2920	-	0.6033
0.3361	2930	-	0.6022
0.3373	2940	-	0.6012
0.3384	2950	-	0.6003
0.3396	2960	-	0.5993
0.3407	2970	-	0.5986
0.3419	2980	-	0.5978
0.3430	2990	-	0.5967
0.3442	3000	1.0256	0.5959

Framework Versions

• Python: 3.12.8
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.2.0+cu121
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}