bwang0911/bge-int8

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the trivia 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: BAAI/bge-base-en-v1.5
• Maximum Sequence Length: 196 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • trivia
• 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': 196, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("bwang0911/bge-int8")
# Run inference
sentences = [
    'Where in Australia was swashbuckling Errol Flynn born?',
    'Errol Flynn Blood" (1935), Major Geoffrey Vickers in "The Charge of the Light Brigade" (1936), as well as a number of Westerns, such as "Dodge City" (1939), "Santa Fe Trail" (1940), and "San Antonio" (1945). Errol Leslie Flynn was born on 20 June 1909 in Battery Point, a suburb of Hobart, Tasmania, Australia. His father, Theodore Thomson Flynn, was a lecturer (1909) and later professor (1911) of biology at the University of Tasmania. His mother was born Lily Mary Young, but shortly after marrying Theodore at St John\'s Church of England, Birchgrove, Sydney, on 23 January 1909, she changed her first name',
    'Errol Flynn early in his career: Errol Flynn Errol Leslie Thomson Flynn (20 June 1909 – 14 October 1959) was an Australian-born American actor during the Golden Age of Hollywood. Considered the natural successor to Douglas Fairbanks, he achieved worldwide fame for his romantic swashbuckler roles in Hollywood films, as well as frequent partnerships with Olivia de Havilland. He was best known for his role as Robin Hood in "The Adventures of Robin Hood" (1938); his portrayal of the character was named by the American Film Institute as the 18th greatest hero in American film history. His other famous roles included the',
]
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]

Evaluation

Metrics

Information Retrieval

• Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
• Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.28	0.6	0.64	0.3	0.66	0.38	0.34	0.26	0.86	0.34	0.18	0.56	0.4694
cosine_accuracy@3	0.46	0.78	0.9	0.38	0.78	0.5	0.5	0.48	0.96	0.48	0.52	0.66	0.7347
cosine_accuracy@5	0.54	0.84	0.92	0.42	0.82	0.6	0.56	0.64	0.96	0.6	0.64	0.68	0.8163
cosine_accuracy@10	0.68	0.92	0.94	0.48	0.92	0.74	0.6	0.72	1.0	0.7	0.8	0.84	0.9184
cosine_precision@1	0.28	0.6	0.64	0.3	0.66	0.38	0.34	0.26	0.86	0.34	0.18	0.56	0.4694
cosine_precision@3	0.18	0.52	0.3	0.18	0.3467	0.1667	0.3	0.16	0.4	0.2467	0.1733	0.2333	0.3946
cosine_precision@5	0.132	0.496	0.188	0.128	0.244	0.12	0.3	0.132	0.252	0.204	0.128	0.144	0.3878
cosine_precision@10	0.088	0.432	0.096	0.076	0.142	0.074	0.244	0.076	0.136	0.132	0.08	0.096	0.3265
cosine_recall@1	0.1217	0.0629	0.6167	0.1554	0.33	0.38	0.0121	0.25	0.7473	0.0737	0.18	0.54	0.0357
cosine_recall@3	0.2267	0.1301	0.8567	0.2226	0.52	0.5	0.0406	0.46	0.9253	0.1547	0.52	0.64	0.0855
cosine_recall@5	0.2773	0.1827	0.8767	0.2646	0.61	0.6	0.0587	0.61	0.942	0.2117	0.64	0.66	0.1405
cosine_recall@10	0.349	0.2871	0.8967	0.3058	0.71	0.74	0.0821	0.69	0.9933	0.2727	0.8	0.84	0.2204
cosine_ndcg@10	0.2921	0.5202	0.7847	0.2791	0.6314	0.5328	0.2699	0.465	0.9204	0.272	0.4849	0.6789	0.3691
cosine_mrr@10	0.3988	0.7049	0.7672	0.3567	0.7392	0.4702	0.428	0.3999	0.9073	0.4481	0.3844	0.6334	0.611
cosine_map@100	0.2301	0.3763	0.7406	0.247	0.5493	0.4806	0.0994	0.3974	0.8925	0.223	0.393	0.6282	0.2939

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "climatefever",
          "dbpedia",
          "fever",
          "fiqa2018",
          "hotpotqa",
          "msmarco",
          "nfcorpus",
          "nq",
          "quoraretrieval",
          "scidocs",
          "arguana",
          "scifact",
          "touche2020"
      ]
  }
  

Metric	Value
cosine_accuracy@1	0.4515
cosine_accuracy@3	0.6257
cosine_accuracy@5	0.6951
cosine_accuracy@10	0.7891
cosine_precision@1	0.4515
cosine_precision@3	0.277
cosine_precision@5	0.2197
cosine_precision@10	0.1537
cosine_recall@1	0.2697
cosine_recall@3	0.4063
cosine_recall@5	0.4672
cosine_recall@10	0.5528
cosine_ndcg@10	0.5
cosine_mrr@10	0.5576
cosine_map@100	0.427

Training Details

Training Dataset

trivia

• Dataset: trivia at bfe9460
• Size: 60,315 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 15.15 tokens max: 42 tokens	min: 113 tokens mean: 138.92 tokens max: 196 tokens	min: 111 tokens mean: 137.88 tokens max: 196 tokens

• Samples:

  anchor	positive	negative
  Which American-born Sinclair won the Nobel Prize for Literature in 1930?	Sinclair Lewis Sinclair Lewis Harry Sinclair Lewis (February 7, 1885 – January 10, 1951) was an American novelist, short-story writer, and playwright. In 1930, he became the first writer from the United States to receive the Nobel Prize in Literature, which was awarded "for his vigorous and graphic art of description and his ability to create, with wit and humor, new types of characters." His works are known for their insightful and critical views of American capitalism and materialism between the wars. He is also respected for his strong characterizations of modern working women. H. L. Mencken wrote of him, "[If] there	Nobel Prize in Literature analyze its importance on potential future Nobel Prize in Literature laureates. Only Alice Munro (2009) has been awarded with both. The Neustadt International Prize for Literature is regarded as one of the most prestigious international literary prizes, often referred to as the American equivalent to the Nobel Prize. Like the Nobel or the Man Booker International Prize, it is awarded not for any one work, but for an entire body of work. It is frequently seen as an indicator of who may be awarded the Nobel Prize in Literature. Gabriel García Márquez (1972 Neustadt, 1982 Nobel), Czesław Miłosz (1978 Neustadt,
  Where in England was Dame Judi Dench born?	Judi Dench regular contact with the theatre. Her father, a physician, was also the GP for the York theatre, and her mother was its wardrobe mistress. Actors often stayed in the Dench household. During these years, Judi Dench was involved on a non-professional basis in the first three productions of the modern revival of the York Mystery Plays in 1951, 1954 and 1957. In the third production she played the role of the Virgin Mary, performed on a fixed stage in the Museum Gardens. Though she initially trained as a set designer, she became interested in drama school as her brother Jeff	Judi Dench to independence, published in August 2014, a few weeks before the Scottish referendum. In September 2018, Dench criticized the response to the sexual misconduct allegations made against actor Kevin Spacey, referring to him as a "good friend". Judi Dench Dame Judith Olivia Dench (born 9 December 1934) is an English actress. Dench made her professional debut in 1957 with the Old Vic Company. Over the following few years, she performed in several of Shakespeare's plays, in such roles as Ophelia in "Hamlet", Juliet in "Romeo and Juliet", and Lady Macbeth in "Macbeth". Although most of her work during this period
  From which country did Angola achieve independence in 1975?	Corruption in Angola they really are. Angola's colonial era ended with the Angolan War of Independence against Portugal occurred between 1970 and 1975. Independence did not produce a unified Angola, however; the country plunged into years of civil war between the National Union for the Total Independence of Angola (UNITA) and the governing Popular Movement for the Liberation of Angola (MPLA). 30 years of war would produce historical legacies that combine to allow for the persistence of a highly corrupt government system. The Angolan civil war was fought between the pro-western UNITA and the communist MPLA and had the characteristics typical of a	Cuban intervention in Angola Cuban intervention in Angola In November 1975, on the eve of Angola's independence, Cuba launched a large-scale military intervention in support of the leftist People's Movement for the Liberation of Angola (MPLA) against United States-backed interventions by South Africa and Zaire in support of two right-wing independence movements competing for power in the country, the National Liberation Front of Angola (FNLA) and the National Union for the Total Independence of Angola (UNITA). By the end of 1975 the Cuban military in Angola numbered more than 25,000 troops. Following the withdrawal of Zaire and South Africa, Cuban forces remained in Angola

• 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: 128
• 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: 128
• per_device_eval_batch_size: 8
• 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}
• 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: 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
• 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	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0212	10	2.7514	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0424	20	2.7415	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0636	30	2.5319	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0847	40	2.3283	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1059	50	2.0535	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1271	60	1.8257	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1483	70	1.6569	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1695	80	1.5127	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1907	90	1.3586	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2119	100	1.3002	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2331	110	1.2825	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2542	120	1.1649	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2754	130	1.1589	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2966	140	1.1404	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3178	150	1.1462	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3390	160	1.1297	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3602	170	1.0774	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3814	180	1.0845	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4025	190	1.0574	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4237	200	1.1048	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4449	210	1.0817	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4661	220	1.0603	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4873	230	1.0383	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5085	240	1.0197	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5297	250	1.0979	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5508	260	1.0303	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5720	270	1.0363	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5932	280	1.0433	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6144	290	0.98	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6356	300	1.0272	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6568	310	1.054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6780	320	1.0213	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6992	330	1.0111	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7203	340	0.9849	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7415	350	1.0054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7627	360	0.9998	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7839	370	0.9871	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8051	380	1.0223	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8263	390	0.9592	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8475	400	0.9736	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8686	410	0.9653	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8898	420	0.9856	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9110	430	1.0445	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9322	440	0.9818	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9534	450	0.9937	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9746	460	0.9818	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9958	470	0.9799	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0169	480	0.908	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0381	490	0.9568	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0593	500	0.9887	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0805	510	0.9401	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1017	520	0.934	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1229	530	0.9245	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1441	540	0.9329	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1653	550	0.9985	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1864	560	0.9591	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2076	570	0.9433	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2288	580	0.9645	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.25	590	0.9682	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2712	600	0.9385	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2924	610	0.8819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3136	620	0.9471	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3347	630	0.919	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3559	640	0.9523	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3771	650	0.9248	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3983	660	0.9784	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4195	670	0.9003	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4407	680	0.9652	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4619	690	0.9286	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4831	700	0.8873	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5042	710	0.9252	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5254	720	0.938	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5466	730	0.9394	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5678	740	0.9224	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5890	750	0.9128	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6102	760	0.9367	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6314	770	0.9664	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6525	780	0.9307	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6737	790	0.8823	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6949	800	0.9306	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7161	810	0.8754	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7373	820	0.9376	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7585	830	0.8803	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7797	840	0.9254	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8008	850	0.9282	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8220	860	0.9175	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8432	870	0.9482	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8644	880	0.9289	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8856	890	0.9354	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9068	900	0.9253	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9280	910	0.9363	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9492	920	1.0037	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9703	930	0.8552	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9915	940	0.9267	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0127	950	0.9043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0339	960	0.8859	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0551	970	0.9149	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0763	980	0.917	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0975	990	0.8839	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1186	1000	0.9502	0.2921	0.5202	0.7847	0.2791	0.6314	0.5328	0.2699	0.4650	0.9204	0.2720	0.4849	0.6789	0.3691	0.5000
2.1398	1010	0.9131	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1610	1020	0.9191	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1822	1030	0.8992	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2034	1040	0.913	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2246	1050	0.871	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2458	1060	0.9336	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2669	1070	0.903	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2881	1080	0.8995	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3093	1090	0.9018	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3305	1100	0.861	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3517	1110	0.8548	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3729	1120	0.8928	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3941	1130	0.9606	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4153	1140	0.8921	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4364	1150	0.8511	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4576	1160	0.8977	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4788	1170	0.8894	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5	1180	0.8647	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5212	1190	0.8421	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5424	1200	0.8654	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5636	1210	0.926	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5847	1220	0.8911	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6059	1230	0.9191	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6271	1240	0.8731	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6483	1250	0.8757	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6695	1260	0.8825	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6907	1270	0.8881	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7119	1280	0.8745	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7331	1290	0.8404	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7542	1300	0.9377	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7754	1310	0.9149	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7966	1320	0.8881	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8178	1330	0.8889	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8390	1340	0.9289	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8602	1350	0.9169	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8814	1360	0.8803	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9025	1370	0.8398	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9237	1380	0.8716	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9449	1390	0.8912	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9661	1400	0.8471	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9873	1410	0.9158	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Framework Versions

• Python: 3.10.12
• 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",
}

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}
}