fine-tuned-embedding-model

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What does this text say about data privacy?',
    'information during GAI training and maintenance. \nHuman-AI Configuration; Obscene, \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration; \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or levels of harmful bias, intellectual property infringement, \ndata privacy violations, obscenity, extremism, violence, or CBRN information in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety features of fine-tuned models when the negative risk exceeds \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to \nassess risks that may arise from unreliable downstream decision-making. \nValue Chain and Component \nIntegration; Dangerous, Violent, or \nHateful Content',
    'Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590 \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \nShevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 \nShumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 \nSoice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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: 555 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 555 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 11.2 tokens</li><li>max: 12 tokens</li></ul>	<ul><li>min: 156 tokens</li><li>mean: 199.37 tokens</li><li>max: 256 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>What does this text say about trustworthiness?</code>	<code>other systems. <br>Information Integrity; Value Chain <br>and Component Integration <br>MP-2.2-002 <br>Observe and analyze how the GAI system interacts with external networks, and <br>identify any potential for negative externalities, particularly where content <br>provenance might be compromised. <br>Information Integrity <br>AI Actor Tasks: End Users <br> <br>MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental <br>design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct <br>validation <br>Action ID <br>Suggested Action <br>GAI Risks <br>MP-2.3-001 <br>Assess the accuracy, quality, reliability, and authenticity of GAI output by <br>comparing it to a set of known ground truth data and by using a variety of <br>evaluation methods (e.g., human oversight and automated evaluation, proven <br>cryptographic techniques, review of content inputs). <br>Information Integrity <br> <br>25</code>
  <code>What does this text say about unclassified?</code>	<code>training and TEVV data; Filtering of hate speech or content in GAI system <br>training data; Prevalence of GAI-generated data in GAI system training data. <br>Harmful Bias and Homogenization <br> <br> <br>15 Winogender Schemas is a sample set of paired sentences which differ only by gender of the pronouns used, <br>which can be used to evaluate gender bias in natural language processing coreference resolution systems. <br> <br>37 <br>MS-2.11-005 <br>Assess the proportion of synthetic to non-synthetic training data and verify <br>training data is not overly homogenous or GAI-produced to mitigate concerns of <br>model collapse. <br>Harmful Bias and Homogenization <br>AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End-Users, <br>Operation and Monitoring, TEVV <br> <br>MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP <br>function – are assessed and documented. <br>Action ID <br>Suggested Action <br>GAI Risks</code>
  <code>What does this text say about unclassified?</code>	<code>Padmakumar, V. et al. (2024) Does writing with language models reduce content diversity? ICLR. <br>https://arxiv.org/pdf/2309.05196 <br>Park, P. et. al. (2024) AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5). <br>arXiv. https://arxiv.org/pdf/2308.14752 <br>Partnership on AI (2023) Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect <br>Disclosure. https://partnershiponai.org/glossary-for-synthetic-media-transparency-methods-part-1-<br>indirect-disclosure/ <br>Qu, Y. et al. (2023) Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-<br>To-Image Models. arXiv. https://arxiv.org/pdf/2305.13873 <br>Rafat, K. et al. (2023) Mitigating carbon footprint for knowledge distillation based deep learning model <br>compression. PLOS One. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285668 <br>Said, I. et al. (2022) Nonconsensual Distribution of Intimate Images: Exploring the Role of Legal Attitudes</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• 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: 16
• per_device_eval_batch_size: 16
• 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
• num_train_epochs: 3
• 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
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Framework Versions

• Python: 3.11.5
• Sentence Transformers: 3.1.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cpu
• Accelerate: 0.34.2
• Datasets: 3.0.0
• 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",
}

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}
}

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