SentenceTransformer based on FacebookAI/roberta-base

This is a sentence-transformers model finetuned from FacebookAI/roberta-base on the csv 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: FacebookAI/roberta-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • csv

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: RobertaModel 
  (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})
)

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 = [
    "The account of an expedition against Fort Christina deserves to be\nquoted in full, for it is an example of what war might be, full of\nexcitement, and exercise, and heroism, without danger to life. We take\nup the narrative at the moment when the Dutch host...',
    '"He stood by me all these years," he thought, "he taught me all I know,\nthough I fear I am still very young and an ignoramus. But he\'s tried\nhard I know to impart all his own special knowledge to me, and he\'s\ngiven me chances that many a young officer would give his ears for.\nRight!...',
]
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

Binary Classification

• Datasets: litemb-dev and litemb-test
• Evaluated with BinaryClassificationEvaluator

Training Details

Training Dataset

csv

• Dataset: csv
• Size: 4,415,131 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 447 tokensmean: 510.65 tokensmax: 512 tokens	min: 450 tokensmean: 510.71 tokensmax: 512 tokens	min: 455 tokensmean: 510.83 tokensmax: 512 tokens

• Samples:

  anchor	positive	negative
  "That was curious," remarked Trent."I thought so, sir. But I recollected what I had heard about 'not a wordto a soul,' and I concluded that this about a moonlight drive wasintended to mislead.""What time was this?""It would be about ten, sir, I should say. After speaking to me, Mr.Manderson waited until Mr. Marlowe had come down and brought round thecar. He then went into the drawing-room, where Mrs. Manderson was.""Did that strike you as curious?"Martin looked down his nose. "If you ask me the question, sir," he saidwith reserve, "I had not known him enter that room since we came herethis year. He preferred to sit in the library in the evenings. Thatevening he only remained with Mrs. Manderson for a few minutes. Then heand Mr. Marlowe started immediately.""You saw them start?""Yes, sir. They took the direction of Bishopsbridge.""And you saw Mr. Manderson again later?""After an hour or thereabouts, sir, in the library. That would have beenabout a quarter past eleven, ...	Sir James turned instantly to Mr. Figgis, whose pencil was poised overthe paper. “Sigsbee Manderson has been murdered,” he began quickly andclearly, pacing the floor with his hands behind him. Mr. Figgisscratched down a line of shorthand with as much emotion as if he hadbeen told that the day was fine—the pose of his craft. “He and his wifeand two secretaries have been for the past fortnight at the housecalled White Gables, at Marlstone, near Bishopsbridge. He bought itfour years ago. He and Mrs. Manderson have since spent a part of eachsummer there. Last night he went to bed about half-past eleven, just asusual. No one knows when he got up and left the house. He was notmissed until this morning. About ten o’clock his body was found by agardener. It was lying by a shed in the grounds. He was shot in thehead, through the left eye. Death must have been instantaneous. Thebody was not robbed, but there were marks on the wrists which pointedto a struggle having taken place. Dr...	Holmes shook his head like a man who is far from being satisfied.“These are very deep waters,” said he; “pray go on with your narrative.”“Two years have passed since then, and my life has been until latelylonelier than ever. A month ago, however, a dear friend, whom I haveknown for many years, has done me the honor to ask my hand in marriage.His name is Armitage—Percy Armitage—the second son of Mr. Armitage,of Crane Water, near Reading. My step-father has offered no oppositionto the match, and we are to be married in the course of the spring. Twodays ago some repairs were started in the west wing of the building,and my bedroom wall has been pierced, so that I have had to move intothe chamber in which my sister died, and to sleep in the very bed inwhich she slept. Imagine, then, my thrill of terror when last night,as I lay awake, thinking over her terrible fate, I suddenly heard inthe silence of the night the low whistle which had been the herald ofher own death. I sprang ...
  'The condition of those blacks is assuredly better than that of the agricultural laborers in many parts of Europe. Their morality is far superior to that of the free negroes of the North; the planters encourage marriage, and thus endeavor to develop among them a sense of the family relation, with a view of attaching them to the domestic hearth, consequently to the family of the master. It will be then observed that in such a state of things the interests of the planter, in default of any other motive, promotes the advancement and well-being of the slave. Certainly, we believe it possible still to ameliorate their condition. It is with that view, even, that the South has labored for so long a time to prepare them for a higher civilization. 'In no part, perhaps, of the continent, regard being had to the population, do there exist men more eminent and gifted, with nobler or more generous sentiments, than in the Southern States. No co...	If we had clear and strong faith, our joy at the thought of a glorifiedspirit, however necessary its presence to us here, would transcend allour sorrows; the streaming beams of sunshine would irradiate ourweeping; we should think more of his happiness than of our discomfort.Instead of departed spirits falling asleep, it is we who have a spiritof slumber. O that we might walk by faith with glorified spirits beforethe throne, instead of remanding them,--as it seems we sometimes woulddo, if we could,--to the ignorance and infirmity of our condition.Our feelings towards the departed are the same as towards otherprohibited things. Many are continually seeking for pleasures which Godhas taken away, or is purposely withholding from them. Let any one lookat the history of his feelings, and see if his state of mind be not oneof perpetual expectation of some form of happiness yet to arrive; anideal of bliss, some prefigured condition, in which contentment andpeace are to abide; whi...	“And we? Now that we've fought and lied and sweated and stolen, andhated as only the disappointed strugglers in a bitter, dead littleWestern town know how to do, what have we got to show for it? HarveyMerrick wouldn't have given one sunset over your marshes for all you'vegot put together, and you know it. It's not for me to say why, in theinscrutable wisdom of God, a genius should ever have been called fromthis place of hatred and bitter waters; but I want this Boston man toknow that the drivel he's been hearing here tonight is the onlytribute any truly great man could ever have from such a lot of sick,side-tracked, burnt-dog, land-poor sharks as the here-present financiersof Sand City--upon which town may God have mercy!”The lawyer thrust out his hand to Steavens as he passed him, caught uphis overcoat in the hall, and had left the house before the Grand Armyman had had time to lift his ducked head and crane his long neck aboutat his fellows.Next day Jim Laird was drun...
  When Cowper became an author he paid the highest respect to Mrs. Unwinas an instinctive critic, and called her his Lord Chamberlain, whoseapprobation was his sufficient licence for publication.Life in the Unwin family is thus described by the new inmate;--"As toamusements, I mean what the world calls such, we have none. The placeindeed swarms with them; and cards and dancing are the professedbusiness of almost all the gentle inhabitants of Huntingdon. We refuseto take part in them, or to be accessories to this way of murdering ourtime, and by so doing have acquired the name of Methodists. Havingtold you how we do not spend our time, I will next say how we do. Webreakfast commonly between eight and nine; till eleven, we read eitherthe scripture, or the sermons of some faithful preacher of those holymysteries; at eleven we attend divine service, which is performed heretwice every day, and from twelve to three we separate, and amuseourselves as we please. During that in...	Peel’s Government having been overthrown on the question of the CornLaws by a combination which the Duke of Wellington characterized withmilitary frankness, of Tory Protectionists, Whigs, Radicals, and IrishNationalists, the whole under Semitic influence, its chief, for theshort remainder of his life, held himself aloof from the party fray,encouraging no new combination, and content with watching over the safetyof his great fiscal reform; though, as Greville says, had the Premiershipbeen put to the vote, Peel would have been elected by an overwhelmingmajority. His personal following, Peelites as they were called, Graham,Gladstone, Lincoln, Cardwell, Sidney Herbert, and the rest, remainedsuspended between the two great parties. When Disraeli had thrown overprotection, as he meant from the beginning to do, the only barrierof principle between the Peelites and the Conservatives was removed.Overtures were made by the Conservative leader, Lord Derby, to Gladstone,whose immense...	"If you take my advice," said Stanley who was fighting his way towardssome remote goal or other, "you'll take a little flyer on Dr. Rice.That's what I'm going to do. There's a fellow on the other side of thering has him a point higher than anyone else."Dick, without having made up his mind as to his own betting or notbetting, helped his companion in his struggle to get through the crowd.Desperate energy was necessary. There was never any time for apologies;elbows were pushed into sides, toes were trodden on, scarfs twisted andsleeve-links broken; no matter, there was money to be won and there wasno time either to consider passing annoyances or the possibility ofloss."Ah," said Stanley, finally, as they found themselves in front of ablack-board that had a figure "7" chalked to the left of the name Dr.Rice and a "3" to the right. "Here we are! Now then, what are you goingto do?" He whipped out a twenty dollar bill and crumpled it carefullyinto the palm of his hand.Dick th...

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 0.5
  }
  

Evaluation Dataset

csv

• Dataset: csv
• Size: 944,948 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 420 tokensmean: 510.66 tokensmax: 512 tokens	min: 432 tokensmean: 510.77 tokensmax: 512 tokens	min: 424 tokensmean: 510.38 tokensmax: 512 tokens

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 0.5
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 35
• per_device_eval_batch_size: 35
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• batch_sampler: no_duplicates

All Hyperparameters

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 35
• per_device_eval_batch_size: 35
• 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: 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: True
• 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: True
• 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
• 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

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.0
• Sentence Transformers: 3.4.0.dev0
• Transformers: 4.46.3
• PyTorch: 2.5.1+cu121
• Accelerate: 1.1.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}