02262025_V2_fine-tuned-bge-small-optimized

SentenceTransformer based on BAAI/bge-small-en

This is a sentence-transformers model finetuned from BAAI/bge-small-en. 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: BAAI/bge-small-en <!-- at revision 2275a7bdee235e9b4f01fa73aa60d3311983cfea -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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 = [
    'Can the 3051s transmitter be ordered with a G½ conduit entry size?',
    'Info for 8700m\n|2 (50)|ASME 600 DERAT. So / RF|8705 020C6|223|223|221|224|223| |165|132|122|126|92| |\n|2 (50)|Din Pn40 So / RF|8705 OzocH|200|200|198|201|200|200|165|132|122|126|102| |\n|2 (50)|AS2129 TABLE D, So / RF| |8705|200| |198|201|200| |50|32|122|126|90|\n|2 (50)|AS2129 TABLE E, So / RF|8705 020CL|200| |198|201|200| |50|132|122|126|90| |\n|2 (50)|JIS B220 10K, So / RF|8705 02CP|200| |198|201|200| |155|132|122|126|96| |\n|2 (50)|JIS B220 20K, So / RF|8705 O2OCR|200| |198|201|200| |55|132|122|126|96| |\n|2 (50)|JIS B220 40K, So / RF|8705 O20CI|223| |22|224|223| |165|32|122|126|105| |\n|2 (50)|AS4087 Pni6_ Sq / RF|8705 020CU|200| |198|201|200| |150|132|122|126|90| |\n|2 (50)|AS4087 Pn2| , So / RF|8705 020CW|200| |198|201|200| |165|132|122|126|103| |\n|2 (50)|AS4087 Pn35 , So / RF|8705 O20cy|200| |198|201|200| |65|132|122|126|103| |\n|2.5 (65)|ASME 150 So / RF|8705 025C1|199| |197| |78|160|136|140|105|12| | |\n|2.5 (65)|ASME 300 So / RF|8705 025c3|199| |197| |19|60|136|140|105|15| | |',
    'Info for 8700m\n# Process connections\n\n|ASME B16.5|Class 150 and Class 300: ½ inch to 24 inch (15 mm to 600 mm)|\n|---|---|\n| |Class 600: ½ inch to 24 inch (15 mm to 600 mm)(1)|\n| |Class 900: 1 inch to 12 inch (25 mm to 300 mm)(2)|\n| |Class 1500: 1½ inch to 12 inch (40 mm to 300 mm)(2)|\n| |1½ inch to 6 inch (40 mm to 150 mm)(2)|\n|ASME B16.47|Class 150: 30 inch to 36 inch (750 mm to 900 mm)|\n| |Class 300: 30 inch to 36 inch (750 mm to 900 mm)|\n|AWWA C207|Class D: 30 inch and 36 inch (750 mm and 900 mm)|\n|MSS SP44|Class 150: 30 inch to 36 inch (750 mm to 900 mm)|\n|EN 1092-1|PN10: 200 mm to 900 mm (8 inch to 36 inch)|\n| |PN16: 100 mm to 900 mm (4 inch to 36 inch)|\n| |PN25: 200 mm to 900 mm (8 inch to 36 inch)|\n| |PN40: 15 mm to 900 mm (½ inch to 36 inch)|\n|AS2129|Table D and Table E: 15 mm to 900 mm (½ inch to 36 inch)|\n|AS4087|PN16, PN21, PN35: 50 mm to 600 mm (2 inch to 24 inch)|\n|JIS B2220|10K, 20K, 40K: 15 mm to 200 mm (½ inch to 8 inch)|',
]
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.* -->

Evaluation

Metrics

Semantic Similarity

• Dataset: valid
• Evaluated with <code>EmbeddingSimilarityEvaluator</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: 41,760 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 12 tokens</li><li>mean: 20.93 tokens</li><li>max: 46 tokens</li></ul>	<ul><li>min: 16 tokens</li><li>mean: 304.51 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 1.0</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>How much does the 8705 240CE model weigh in the DIN PNI6 So / RF setup?</code>	<code>Info for 3051s<br># Process connection size<br><br>	Code
  <code>What type of instrument flange is used for the Rosemount 3051S_T with F11 code?</code>	<code>Info for 3051s<br># Electrical connections<br><br>½–14 NPT, G½, and M20 × 1½ conduit. HART® interface connections fixed to terminal block for Output code A and X.<br><br># Process connections<br><br>Coplanar sensor module (Rosemount 3051S_C, 3051SMV, 3051SF, 3051SAM_ _G or A)<br><br>	Standard
  <code>I need info on the 8700m sensor with ASME -1500 WN / RTJ, can you help?</code>	<code>Info for 8700m<br>	(350) JIS B2200 20k, So / RF

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

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 6
• multi_dataset_batch_sampler: round_robin

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: 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
• 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: 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: round_robin

</details>

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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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