ws-wr-questions-bert-TSDAE-v1

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: 512 tokens
• Output Dimensionality: 768 tokens
• 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("jameswright/ws-wr-questions-bert-TSDAE-v1")
# Run inference
sentences = [
    'You have been sick so If questions it just - ’ being sorted, thanks Then assertiveness course and a . overtime isn selfish it just doing ’ s right for Why you?',
    'You have been off sick so it makes sense. If anyone questions it just grey rock - it’s being sorted, thanks. Then do an online assertiveness course and ask your GP for a CBT referral. Not doing overtime isn’t selfish - it’s just you doing what’s right for you. Why would you do anything else?',
    'Science works by the accumulation of evidence.\xa0 Independent groups work on projects and publish results.\xa0 Those results are examined and tested and examined again and tested again, such that they\'re either confirmed or discarded and further work continues accordingly.\xa0 If a scientist or doctor disagrees with the \'official line\' they\'re asked to present the data, methods and conclusions that have led to that disagreement so that it can by examined by the broader scientific and medical community.\xa0 \xa0 And yes, someone who goes on YouTube or wherever - whether doctor, scientist or layperson - and tells viewers that a vaccine alters DNA structure and destroys the immune system is either a grifter or a fruitcake. 1 minute ago, FIRETHORN1 said: ...Can you not accept that some people can hold an opposite view quite genuinely? To me, a "conspiracy theorist" is someone who believes what they are told, without any evidence to back it up. I can absolutely accept that someone can genuinely believe something without having any evidence at all to support that view.\xa0 I\'m believing that right now, in fact. 1 minute ago, FIRETHORN1 said: There is no evidence whatsoever that the vaccine works That is categorically, absolutely and undeniably false, as the most cursory of research will tell you.\xa0 But then you don\'t actually want\xa0 to believe me, do you?',
]
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.* --> <!-- ## 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

Unnamed Dataset

• Size: 132,712 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 4 tokens</li><li>mean: 47.75 tokens</li><li>max: 460 tokens</li></ul>	<ul><li>min: 17 tokens</li><li>mean: 114.94 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>’ Can really go to the doctors I ’ bored of the ” . Feels more like than a doctor, does sound depression, so seeing GP a first</code>	<code>PetersRabbitt I don’t know? Can I really go to the doctors and say “hey, yes my problem is I’m bored all of the time”. Feels more like a me problem than one a doctor can help with. Yes, absolutely. It does sound like it could be depression, so seeing your GP is a good first step.</code>
  <code>Ursuladevine Between 11 16, if hasn t, what has been been providing education have LakieLady Yesterday 15:34 My that dismissed offhand son the school have up for assessment . Within years referred diagnosed with PTSD,,, social anxiety and, decided his be.</code>	<code>Ursuladevine · Yesterday 15:42 Between 11 and 16, if he hasn’t been attending school, what has he been doing? Has the LA been providing any education or have you been HE? LakieLady · Yesterday 15:34 My friend tried that, and the GP dismissed it offhand, saying that if her son was neurodivergent, the school would have picked up on it and referred him for assessment. Her DS was eight at the time. Within the next 2-3 years, he got much worse, was referred to CAMHS, diagnosed with significant MH problems (PTSD, GAD, depression, social anxiety disorder) and after a couple of years, CAMHS decided his mother might not be talking bollocks and that he might have ASD.</code>
  <code>It sounds you were a child then came along realised here was he - and this to it . young, I'd imagine</code>	<code>It sounds like you were hurt by one man when you were a child, then another came along and realised here was someone damaged he could dominate - and added his own abuse. They can sniff this out and are attracted to it. How old were you when he arrived? Very young, I'd imagine. Stepfather?</code>

• Loss: <code>DenoisingAutoEncoderLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 5
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 8
• 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
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

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

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.42.3
• PyTorch: 2.3.0+cu121
• Accelerate: 0.31.0
• Datasets: 2.20.0
• Tokenizers: 0.19.1

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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