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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 <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • csv

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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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Binary Classification

• Datasets: litemb-dev and litemb-test
• Evaluated with <code>BinaryClassificationEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

csv

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

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 447 tokens</li><li>mean: 510.65 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 450 tokens</li><li>mean: 510.71 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 455 tokens</li><li>mean: 510.83 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

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

• Loss: <code>TripletLoss</code> with these parameters:

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

Evaluation Dataset

csv

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

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 420 tokens</li><li>mean: 510.66 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 432 tokens</li><li>mean: 510.77 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 424 tokens</li><li>mean: 510.38 tokens</li><li>max: 512 tokens</li></ul>

• Loss: <code>TripletLoss</code> 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

<details><summary>Click to expand</summary>

• 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

</details>

• The bold row denotes the saved checkpoint.

</details>

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}
}

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