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---
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** |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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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