sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:22604 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-large-en base_model:finetune:BAAI/bge-large-en model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-large-en
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:22604
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC
    Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations
    - QC Lab
  sentences:
  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
  - '21-1313-2.0 : Layout Drawings'
  - '26-0500-1.0a : Breakers (2P 20A)'
- source_sentence: 23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC
    Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations
    - QC Lab
  sentences:
  - '26-0500-1.3 : Cabling / Wiring'
  - '26-0500-1.0a : Breakers (2P 20A)'
  - '23-2000-1.1 : HWR and HWS Pipe, Valves and Fittings'
- source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 5-P-3783
  sentences:
  - 'mat-3783s8 : 3783 Seq 8 - Material Order'
  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
  - 'mat-3786s18 : 3786 Seq 18 - Material Order'
- source_sentence: 3786 Rady (Pacific - JD Hudson)->Seq 18-P-3786
  sentences:
  - '26-0500-1.0a : Breakers (2P 20A)'
  - 'dwg-3786s18 : 3786 Seq 18 - Drawings'
  - '23-7000-4.0b : EAV-91623'
- source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783
  sentences:
  - 'mat-3783s5 : 3783 Seq 5 - Material Order'
  - 'dwg-3783s8 : 3783 Seq 8 - Drawings'
  - 'dwg-3783s18 : 3783 Seq 18 - Drawings'
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: custom bge dev
      type: custom-bge-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9838187702265372
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.016181229773462782
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9838187702265372
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9838187702265372
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9838187702265372
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: custom bge test
      type: custom-bge-test
    metrics:
    - type: cosine_accuracy
      value: 0.9838187702265372
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.016181229773462782
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9838187702265372
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9838187702265372
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9838187702265372
      name: Max Accuracy
---

# SentenceTransformer based on BAAI/bge-large-en

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en). It maps sentences & paragraphs to a 1024-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-large-en](https://huggingface.co/BAAI/bge-large-en) <!-- at revision abe7d9d814b775ca171121fb03f394dc42974275 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("rnbokade/custom-bge")
# Run inference
sentences = [
    '3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783',
    'dwg-3783s18 : 3783 Seq 18 - Drawings',
    'mat-3783s5 : 3783 Seq 5 - Material Order',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Triplet
* Dataset: `custom-bge-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9838     |
| dot_accuracy       | 0.0162     |
| manhattan_accuracy | 0.9838     |
| euclidean_accuracy | 0.9838     |
| **max_accuracy**   | **0.9838** |

#### Triplet
* Dataset: `custom-bge-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9838     |
| dot_accuracy       | 0.0162     |
| manhattan_accuracy | 0.9838     |
| euclidean_accuracy | 0.9838     |
| **max_accuracy**   | **0.9838** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 22,604 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                            |
  | details | <ul><li>min: 22 tokens</li><li>mean: 25.35 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 18.84 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.74 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                         | positive                                                 | negative                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:--------------------------------------------------------|
  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>dwg-3783s16 : 3783 Seq 16 - Drawings</code>       |
  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>mat-3783s16 : 3783 Seq 16 - Material Order</code> |
  | <code>MOD 1- Metal Decking - Floor<br>Stud Wall Panels<br>Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines</code> | <code>EW1001-125 : Door Slabs / Frames / Hardware</code> | <code>dwg-3786s292 : 3786 Seq 292 - Drawings</code>     |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 618 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 22 tokens</li><li>mean: 33.18 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 17.48 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 17.48 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                | positive                                     | negative                                                |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:--------------------------------------------------------|
  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>dwg-3786s17 : 3786 Seq 17 - Drawings</code>       |
  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>mat-3786s17 : 3786 Seq 17 - Material Order</code> |
  | <code>23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab</code> | <code>26-0500-1.0 : Breakers (3P 20A)</code> | <code>09-9000-2.0 : Paint and Coatings</code>           |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 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
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 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}
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | custom-bge-dev_max_accuracy | custom-bge-test_max_accuracy |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:----------------------------:|
| 0      | 0    | -             | -      | 0.8463                      | -                            |
| 0.0708 | 100  | 0.5651        | 0.6065 | 0.9919                      | -                            |
| 0.1415 | 200  | 0.168         | 0.4217 | 0.9935                      | -                            |
| 0.2123 | 300  | 0.0499        | 0.6747 | 0.9951                      | -                            |
| 0.2831 | 400  | 0.2205        | 0.8112 | 0.9951                      | -                            |
| 0.3539 | 500  | 0.1167        | 0.7040 | 0.9903                      | -                            |
| 0.4246 | 600  | 0.0968        | 0.7364 | 0.9822                      | -                            |
| 0.4954 | 700  | 0.1704        | 0.5540 | 0.9968                      | -                            |
| 0.5662 | 800  | 0.1104        | 0.7266 | 0.9951                      | -                            |
| 0.6369 | 900  | 0.1698        | 1.1020 | 0.9725                      | -                            |
| 0.7077 | 1000 | 0.1077        | 0.9028 | 0.9790                      | -                            |
| 0.7785 | 1100 | 0.1667        | 0.8478 | 0.9757                      | -                            |
| 0.8493 | 1200 | 0.0707        | 0.7629 | 0.9887                      | -                            |
| 0.9200 | 1300 | 0.0299        | 0.8024 | 0.9871                      | -                            |
| 0.9908 | 1400 | 0.0005        | 0.8161 | 0.9838                      | -                            |
| 1.0    | 1413 | -             | -      | -                           | 0.9838                       |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@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
```bibtex
@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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