tartspuppy/bert-base-uncased-tsdae-encoder

SentenceTransformer based on google-bert/bert-base-uncased

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. 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-bert/bert-base-uncased <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
• —Maximum Sequence Length: 75 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 75, 'do_lower_case': False}) 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})
)

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("tartspuppy/bert-base-uncased-tsdae-encoder")
# Run inference
sentences = [
    'album five @ -, in an with Billboard magazine, said it was previously "something I wanted to revisit as been doing a while . "The medley a written whereas McCartney had worked the Beatles\' was made of "bits we had knocking . "The off with Vintage "McCartney sat one to looking back [and looking back . about life followed by the bass @ - @ led That Was Me, which is his school days and ",, "from there . songs "Feet the Clouds "about the inactivity while is up of ", about the life being a celebrity The final song medley, The End of ", written McCartney\'s unk> playing on his, Jim\'s piano',
    'The album features a five song @-@ medley , which in an interview with Billboard magazine , McCartney said that it was previously " something I wanted to revisit " as " nobody had been doing that for a while . " The medley was a group of intentionally written material , whereas McCartney had worked on the Beatles \' Abbey Road which , however , was actually made up of " bits we had knocking around . " The medley starts off with " Vintage Clothes " , which McCartney " sat down one day " to write , that was " looking back , [ and ] looking back . " , about life . It was followed by the bass @-@ led " That Was Me " , which is about his " school days and teachers " , the medley , as McCartney stated , then " progressed from there . " The next songs are " Feet in the Clouds " , about the inactivity while one is growing up , and " House of Wax " , about the life of being a celebrity . The final song in medley , " The End of the End " , was written at McCartney \'s <unk> Avenue home while playing on his father , Jim \'s , piano .',
    'Varanasi grew as an important industrial centre , famous for its muslin and silk <unk> , perfumes , ivory works , and sculpture . Buddha is believed to have founded Buddhism here around <unk> BC when he gave his first sermon , " The Setting in Motion of the Wheel of Dharma " , at nearby <unk> . The city \'s religious importance continued to grow in the 8th century , when Adi <unk> established the worship of Shiva as an official sect of Varanasi . Despite the Muslim rule , Varanasi remained the centre of activity for Hindu intellectuals and theologians during the Middle Ages , which further contributed to its reputation as a cultural centre of religion and education . <unk> Tulsidas wrote his epic poem on Lord Rama \'s life called Ram <unk> Manas in Varanasi . Several other major figures of the Bhakti movement were born in Varanasi , including Kabir and Ravidas . Guru Nanak Dev visited Varanasi for <unk> in <unk> , a trip that played a large role in the founding of <unk> . In the 16th century , Varanasi experienced a cultural revival under the Muslim Mughal emperor <unk> who invested in the city , and built two large temples dedicated to Shiva and Vishnu , though much of modern Varanasi was built during the 18th century , by the Maratha and <unk> kings . The kingdom of Benares was given official status by the <unk> in 1737 , and continued as a dynasty @-@ governed area until Indian independence in 1947 . The city is governed by the Varanasi Nagar Nigam ( Municipal Corporation ) and is represented in the Parliament of India by the current Prime Minister of India <unk> <unk> , who won the <unk> <unk> elections in 2014 by a huge margin . Silk weaving , carpets and crafts and tourism employ a significant number of the local population , as do the <unk> <unk> Works and Bharat Heavy <unk> Limited . Varanasi Hospital was established in 1964 .',
]
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]

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Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

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Downstream Usage (Sentence Transformers)

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

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Evaluation

Metrics

Semantic Similarity

• —Datasets: sts-dev and sts-test
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 21,196 training samples
• —Columns: <code>text</code>
• —Approximate statistics based on the first 1000 samples: | | text | |:--------|:----------------------------------------------------------------------------------| | type | string | | details | <ul><li>min: 6 tokens</li><li>mean: 51.01 tokens</li><li>max: 75 tokens</li></ul> |
• —Samples: | text | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>To promote the album , Carey announced a world tour in April 2003 . As of 2003 , " Charmbracelet World Tour : An Intimate Evening with Mariah Carey " was her most extensive tour , lasting over eight months and performing sixty @-@ nine shows in venues worldwide . Before tickets went on sale in the US , venues were switched from large arenas to smaller , more intimate theater shows . According to Carey , the change was made in order to give fans a more intimate show , and something more Broadway @-@ influenced . She said , " It 's much more intimate so you 'll feel like you had an experience . You experience a night with me . " However , while smaller productions were booked for the US leg of the tour , Carey performed at stadia and arenas in Asia and Europe , and performed for a crowd of over 35 @,@ 000 in Manila , 50 @,@ 000 in Malaysia , and to over 70 @,@ 000 people in China . In the UK , it was Carey 's first tour to feature shows outside London ; she performed in Glasgow , Birming...</code> | | <code>By 1916 , these raiding forces were causing serious concern in the Admiralty as the proximity of Bruges to the British coast , to the troopship lanes across the English Channel and for the U @-@ boats , to the Western Approaches ; the heaviest shipping lanes in the World at the time . In the late spring of 1915 , Admiral Reginald <unk> had attempted without success to destroy the lock gates at Ostend with monitors . This effort failed , and Bruges became increasingly important in the Atlantic Campaign , which reached its height in 1917 . By early 1918 , the Admiralty was seeking ever more radical solutions to the problems raised by unrestricted submarine warfare , including instructing the " Allied Naval and Marine Forces " department to plan attacks on U @-@ boat bases in Belgium .</code> | | <code>PWI International Heavyweight Championship ( 1 time )</code> |
• —Loss: <code>DenoisingAutoEncoderLoss</code>

Evaluation Dataset

Unnamed Dataset

• —Size: 2,355 evaluation samples
• —Columns: <code>text</code>
• —Approximate statistics based on the first 1000 samples: | | text | |:--------|:----------------------------------------------------------------------------------| | type | string | | details | <ul><li>min: 4 tokens</li><li>mean: 51.08 tokens</li><li>max: 75 tokens</li></ul> |
• —Samples: | text | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Wilde 's two final comedies , An Ideal Husband and The Importance of Being Earnest , were still on stage in London at the time of his prosecution , and they were soon closed as the details of his case became public . After two years in prison with hard labour , Wilde went into exile in Paris , sick and depressed , his reputation destroyed in England . In 1898 , when no @-@ one else would , Leonard Smithers agreed with Wilde to publish the two final plays . Wilde proved to be a <unk> <unk> , sending detailed instructions on stage directions , character listings and the presentation of the book , and insisting that a <unk> from the first performance be reproduced inside . Ellmann argues that the proofs show a man " very much in command of himself and of the play " . Wilde 's name did not appear on the cover , it was " By the Author of Lady Windermere 's Fan " . His return to work was brief though , as he refused to write anything else , " I can write , but have lost the joy of writing " ...</code> | | <code>= = = = Ely Viaduct = = = =</code> | | <code>= = World War I = =</code> |
• —Loss: <code>DenoisingAutoEncoderLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 64
• —per_device_eval_batch_size: 64
• —learning_rate: 3e-05
• —num_train_epochs: 100
• —warmup_ratio: 0.1
• —fp16: True
• —dataloader_num_workers: 2
• —load_best_model_at_end: True

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: 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: 3e-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: 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: 2
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —tp_size: 0
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

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

• —The bold row denotes the saved checkpoint. </details>

Framework Versions

• —Python: 3.12.9
• —Sentence Transformers: 4.0.1
• —Transformers: 4.50.1
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.5.2
• —Datasets: 3.4.1
• —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",
}

DenoisingAutoEncoderLoss

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}

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