all-mpnet-base-v2-ds-rag-17g

SentenceTransformer

This is a sentence-transformers model trained. 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:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 384 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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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})
  (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 = [
    'In doing this, there are three decisions we must make:\n\n1.',
    'g(•) defines the hypothesis class H , and a particular value of θ instantiates one hypothesis h ∈ H .',
    'Although replacing traces (Section 7.8) are known to have advantages in tabular methods, replacing traces do not directly extend to the use of function approximation.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3814, 0.1328],
#         [0.3814, 1.0000, 0.1478],
#         [0.1328, 0.1478, 1.0000]])

<!-- ### 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

Semantic Similarity

• Dataset: val
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

Training Data

The model was fine-tuned using 17 reference books in Data Science and Machine Learning, including: All source books were preprocessed using GROBID, an open-source tool for extracting and structuring text from PDF documents.
The raw PDF files were converted into structured text, segmented into sentences, and cleaned before being used for training.
This ensured consistent formatting and reliable sentence boundaries across the dataset.

1. Aßenmacher, Matthias. Multimodal Deep Learning. Self-published, 2023.
2. Bertsekas, Dimitri P. A Course in Reinforcement Learning. Arizona State University.
3. Boykis, Vicki. What are Embeddings. Self-published, 2023.
4. Bruce, Peter, and Andrew Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts. O’Reilly Media, 2017.
5. Daumé III, Hal. A Course in Machine Learning. Self-published.
6. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020.
7. Devlin, Hannah, Guo Kunin, Xiang Tian. Seeing Theory. Self-published.
8. Gutmann, Michael U. Pen & Paper: Exercises in Machine Learning. Self-published.
9. Jung, Alexander. Machine Learning: The Basics. Springer, 2022.
10. Langr, Jakub, and Vladimir Bok. Deep Learning with Generative Adversarial Networks. Manning Publications, 2019.
11. MacKay, David J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
12. Montgomery, Douglas C., Cheryl L. Jennings, and Murat Kulahci. Introduction to Time Series Analysis and Forecasting. 2nd Edition, Wiley, 2015.
13. Nilsson, Nils J. Introduction to Machine Learning: An Early Draft of a Proposed Textbook. Stanford University, 1996.
14. Prince, Simon J.D. Understanding Deep Learning. Draft Edition, 2024.
15. Shashua, Amnon. Introduction to Machine Learning. The Hebrew University of Jerusalem, 2008.
16. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd Edition, MIT Press, 2018.
17. Alpaydin, Ethem. Introduction to Machine Learning. 3rd Edition, MIT Press, 2014.

⚠️ Note: Due to copyright restrictions, the full text of these books is not included in this repository. Only the fine-tuned model weights are shared.

Unnamed Dataset

• Size: 167,112 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: 7 tokens</li><li>mean: 31.74 tokens</li><li>max: 384 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 33.03 tokens</li><li>max: 384 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>The weights w are not given but they can be estimated using the training set of X which we can divide as [X, r].</code>	<code>As we see in equation 14.14, what we are effectively doing is estimating the posterior p(w|X, r) and then integrating over it.</code>
  <code>These methodologies are now mature and provide † A common description is that "the machine learns sequentially how to make decisions that maximize a reward signal, based on the feedback received from the environment."</code>	<code>At the same time, RL and machine learning have ushered opportunities for the application of DP techniques in new domains, such as machine translation, image recognition, knowledge representation, database organization, large language models, and automated planning, where they can have a significant practical impact.</code>
  <code>Using Lagrange multipliers (Section 7.2), we will derive the dual optimization problem of the SVM in Section 12.3.</code>	<code>We subtract the value of ξ n from the margin, constraining ξ n to be non-negative.</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 6
• fp16: True
• 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: 6
• 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
• 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: 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• hub_revision: None
• 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
• include_tokens_per_second: False
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.11.7
• Sentence Transformers: 5.1.1
• Transformers: 4.57.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.10.1
• Datasets: 4.2.0
• Tokenizers: 0.22.1

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}
}

If you use this model, please cite:

@misc{aghakhani2025synergsticrag,
  author       = {Danial Aghakhani Zadeh},
  title        = {Fine-tuned all-mpnet-base-v2 for Data Science RAG},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/DigitalAsocial/all-mpnet-base-v2-ds-rag-17g}}
}

Contact

For questions, feedback, or collaboration requests regarding this dataset/model, please contact:

• Name: Danial Aghakhani Zadeh
• Email: [[email protected]]
• GitHub: https://github.com/digitalasocial
• Hugging Face Profile: https://huggingface.co/DigitalAsocial

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