AtmicQuoterv1

SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-small-en-v1.5 <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', '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 = [
    'Represent this sentence for searching relevant passages: In Guru Vachaka Kovai, how does the ego dissolve and what does the jiva become when it merges with Siva?',
    '[Verse 514] If the jiva’s head but merges / In Siva’s Feet, the jiva shines / As Siva Himself. The ego-ripple dies / And gains the stillness of true Being | Topics: guru_vachaka_kovai',
    '[Verse 406] By the transmuting touch of daily, / Methodical, untiring self-enquiry, / The base metal jiva shines forth clear / As Siva, freed from the mental rust, / The demon ego | Topics: guru_vachaka_kovai',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7078, 0.5582],
#         [0.7078, 1.0000, 0.5088],
#         [0.5582, 0.5088, 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

Information Retrieval

• Dataset: atmic-val
• Evaluated with <code>InformationRetrievalEvaluator</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

Unnamed Dataset

• Size: 3,830 training samples
• Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>negative_3</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative_1	negative_2	negative_3
  type	string	string	string	string	string
  details	<ul><li>min: 13 tokens</li><li>mean: 25.15 tokens</li><li>max: 49 tokens</li></ul>	<ul><li>min: 13 tokens</li><li>mean: 42.27 tokens</li><li>max: 123 tokens</li></ul>	<ul><li>min: 14 tokens</li><li>mean: 44.49 tokens</li><li>max: 123 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 43.86 tokens</li><li>max: 116 tokens</li></ul>	<ul><li>min: 13 tokens</li><li>mean: 44.95 tokens</li><li>max: 128 tokens</li></ul>

• Samples:

  anchor	positive	negative_1	negative_2	negative_3
  <code>Represent this sentence for searching relevant passages: What is Jnana according to Ramana Maharshi?</code>	<code>[] This knowledge that there is nothing but God or the Self... is Jnana | Topics: citation_sentences</code>	<code>[Upadesa Saram] Verse 30: All ego gone, Living as That alone Is penance good for growth, Sings Ramana, the Self. | Topics: ego-extinction, self-abidance, tapas, ramana</code>	<code>[Birth name and family] Bhagavan Sri Ramana Maharshi, born Venkataraman Iyer, was the second of four children born to a devout family in Tiruchuzhi, Tamil Nadu, India.</code>	<code>[] Everything happens according to each individual’s karma | Topics: citation_sentences</code>
  <code>Represent this sentence for searching relevant passages: What does it mean to have the knowledge that nothing exists except God?</code>	<code>[] This knowledge that there is nothing but God or the Self... is Jnana | Topics: citation_sentences</code>	<code>[] That silence could not be understood except by the very advanced | Topics: citation_sentences</code>	<code>[] What exists apart from yourself? You ought to understand that by the Self neither the physical body nor the subtle body is meant | Topics: citation_sentences</code>	<code>[] The flag of vairagya will never move. What greater sign does a Jnani require than that? | Topics: citation_sentences</code>
  <code>Represent this sentence for searching relevant passages: What did the Guru say a disciple should do to repay his Guru's grace?</code>	<code>[] When the disciple asked what he could do in return for his Guru’s grace, the Guru said that he should always be in meditation | Topics: citation_sentences</code>	<code>[Verse 304] The service rendered to the Guru / By the chela called and chosen by him / Should be respectful and correct, / Like a woman’s to her husband / In company | Topics: guru_vachaka_kovai</code>	<code>[] The Guru’s grace... is beyond words or thoughts | Topics: citation_sentences</code>	<code>[Verse 165] Why did the three-eyed Lord ordain / That Kannappa should pluck and plant / Those eyes of which he was so proud? / It was to save him from the death, / The deadly folly of deeming he / Was flesh inert. Such is the might / Of Siva’s grace, of love for Him | Topics: guru_vachaka_kovai</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 2.0
• learning_rate: 2e-05
• warmup_steps: 0.1
• weight_decay: 0.01
• load_best_model_at_end: True

All Hyperparameters

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

• per_device_train_batch_size: 8
• num_train_epochs: 2.0
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: True
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 3.7 minutes
• Evaluation: 8.9 seconds
• Total: 3.8 minutes

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 5.4.1
• Transformers: 5.7.0
• PyTorch: 2.10.0
• Accelerate: 1.13.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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