sentence-transformers safetensors distilbert sentence-similarity feature-extraction generated_from_trainer dataset_size:6192 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/msmarco-distilbert-base-tas-b base_model:finetune:sentence-transformers/msmarco-distilbert-base-tas-b autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/msmarco-distilbert-base-tas-b
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6192
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: how to calculate a service load
  sentences:
  - what is the height of a lead in antenna
  - types se cable
  - what is the purpose of a circuit breaker
- source_sentence: minimum ampacity for ungrounded conductors
  sentences:
  - types of mv cables
  - can optical fiber cables be installed in raceway
  - what is a motor and motor operated equipment
- source_sentence: what is the code for a circuit breaker
  sentences:
  - what color insulation is required to be grounded
  - what is a suitable marker for antflix
  - what conductors are permitted to originate in auxiliary gutter
- source_sentence: what is plfa cable
  sentences:
  - what is a noncombustible surface
  - how much liquid can be enclosed in a capacitor
  - what is flammable gas in a busway
- source_sentence: how many volts to ground a transformer
  sentences:
  - what is a grounded conductor
  - how long is a plenum cable
  - what is the operating voltage of a transformer
---

# SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-tas-b

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b). 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:** [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) <!-- at revision 996dfc6404137c6d89c7bf647a4bae62fdf8dd9a -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 768 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': 1024, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'how many volts to ground a transformer',
    'how long is a plenum cable',
    'what is the operating voltage of a transformer',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 6,192 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1         | sentence_2         |
  |:--------|:---------------------------------------------------------------------------------|:-------------------|:-------------------|
  | type    | string                                                                           | dict               | dict               |
  | details | <ul><li>min: 5 tokens</li><li>mean: 9.71 tokens</li><li>max: 33 tokens</li></ul> | <ul><li></li></ul> | <ul><li></li></ul> |
* Samples:
  | sentence_0                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is a metal water piping system</code>          | <code>{'content': 'Metal water piping system(s) installed in or attached to a building or structure shall be bonded to any of the following: Service equipment enclosureGrounded conductor at the serviceGrounding electrode conductor, if of sufficient sizeOne or more grounding electrodes used, if the grounding electrode conductor or bonding jumper to the grounding electrode is of sufficient sizeThe bonding jumper(s) shall be installed in accordance with 250.64(A), (B), and (E). The points of attachment of the bonding jumper(s) s'}</code> | <code>{'content': 'Metal fences enclosing, and other metal structures in or surrounding, a substation with exposed electrical conductors and equipment shall be grounded and bonded to limit step, touch, and transfer voltages. [250.194](https://2023.antflix.net#250.194)'}</code>                                                                                                                                                                                                                                                                        |
  | <code>how many amperes should a circuit breaker be</code> | <code>{'content': '10 amperes, provided all the following conditions are met: Continuous loads do not exceed 8 amperesOvercurrent protection is provided by one of the following:Branch-circuit-rated circuit breakers are listed and marked for use with 14 AWG copper-clad aluminum conductor.Branch-circuit-rated fuses are listed and marked for use with 14 AWG copper-clad aluminum conductor. [240.4(D)(3)](https://2023.antflix.net#240.4(D)(3))'}</code>                                                                                            | <code>{'content': 'For installations to supply only limited loads of a single branch circuit, the branch circuit disconnecting means shall have a rating of not less than 15 amperes. [225.39(A)](https://2023.antflix.net#225.39(A))'}</code>                                                                                                                                                                                                                                                                                                               |
  | <code>phase converter installation</code>                 | <code>{'content': 'This article covers the installation and use of phase converters. [455.1](https://2023.antflix.net#455.1)'}</code>                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>{'content': 'The 120-volt ac side of the voltage converter shall be wired in full conformity with the requirements of Parts I, II, and IV of this article for 120-volt electrical systems. Exception: Converters supplied as an integral part of a listed appliance shall not be subject to 551.20(B). All converters and transformers shall be listed for use in recreational vehicles and designed or equipped to provide overtemperature protection. To determine the converter rating, the following percentages shall be applied to the '}</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `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
- `num_train_epochs`: 10
- `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
- `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}
- `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
- `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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.2920 | 500  | 0.2747        |
| 2.5840 | 1000 | 0.0887        |
| 3.8760 | 1500 | 0.0512        |
| 5.1680 | 2000 | 0.0344        |
| 6.4599 | 2500 | 0.0279        |
| 7.7519 | 3000 | 0.0213        |
| 9.0439 | 3500 | 0.02          |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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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