sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:2036 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-uncased
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2036
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Proven ability to establish and lead complex projects and programs
    within a multilayered, hierarchical organization.
  sentences:
  - Managed multiple concurrent projects in a large healthcare organization
  - Assisted in project documentation without direct management responsibilities
  - Skilled in creating presentations using Microsoft PowerPoint
- source_sentence: Experience in evaluating and planning projects to minimize scheduled
    overtime requirements.
  sentences:
  - Validated release packages and coordinated Salesforce release cycles
  - Oversaw daily housekeeping operations
  - Successfully managed facility renovation projects to reduce overtime
- source_sentence: Candidates should have significant experience in a commercial construction
    environment, ideally with a minimum of 10 years in the field.
  sentences:
  - Built strong partnerships with cross-functional teams to deliver projects
  - over 12 years of experience managing commercial construction projects
  - 2 years of experience in residential construction
- source_sentence: Possession of strong leadership skills in a Workday professional
    context.
  sentences:
  - 3 years of experience with cardiac mapping technologies
  - Managed Workday implementation projects and trained team members
  - Developed marketing strategies for new products
- source_sentence: Ability to manage TikTok Shop setup and troubleshoot operational
    issues effectively.
  sentences:
  - Troubleshot various operational issues during the setup of a TikTok Shop
  - Handled customer support queries for social media platforms
  - Consistently maintained client trust through transparent communication
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.7481079446812986
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7505186904322839
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7554763601200802
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.758901200634132
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7545320893124581
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7581291583714751
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6010864985986635
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5940811367263572
      name: Spearman Dot
    - type: pearson_max
      value: 0.7554763601200802
      name: Pearson Max
    - type: spearman_max
      value: 0.758901200634132
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7078369274551736
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6860532079702527
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7195614364247788
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6992090523383406
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7199683293098692
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.699729559217933
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4876300833689144
      name: Pearson Dot
    - type: spearman_dot
      value: 0.47135994215107385
      name: Spearman Dot
    - type: pearson_max
      value: 0.7199683293098692
      name: Pearson Max
    - type: spearman_max
      value: 0.699729559217933
      name: Spearman Max
---

# SentenceTransformer based on google-bert/bert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). 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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("trbeers/bert-base-uncased-nli-v0")
# Run inference
sentences = [
    'Ability to manage TikTok Shop setup and troubleshoot operational issues effectively.',
    'Troubleshot various operational issues during the setup of a TikTok Shop',
    'Handled customer support queries for social media platforms',
]
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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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7481     |
| **spearman_cosine** | **0.7505** |
| pearson_manhattan   | 0.7555     |
| spearman_manhattan  | 0.7589     |
| pearson_euclidean   | 0.7545     |
| spearman_euclidean  | 0.7581     |
| pearson_dot         | 0.6011     |
| spearman_dot        | 0.5941     |
| pearson_max         | 0.7555     |
| spearman_max        | 0.7589     |

#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7078     |
| **spearman_cosine** | **0.6861** |
| pearson_manhattan   | 0.7196     |
| spearman_manhattan  | 0.6992     |
| pearson_euclidean   | 0.72       |
| spearman_euclidean  | 0.6997     |
| pearson_dot         | 0.4876     |
| spearman_dot        | 0.4714     |
| pearson_max         | 0.72       |
| spearman_max        | 0.6997     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 2,036 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: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.23 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.39 tokens</li><li>max: 15 tokens</li></ul> |
* Samples:
  | anchor                                                                                                     | positive                                                                           | negative                                                                                  |
  |:-----------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|
  | <code>Sensitivity to the needs of patients, families, and physicians to deliver compassionate care.</code> | <code>worked closely with families to address patient concerns</code>              | <code>specialized in technical equipment management without direct patient contact</code> |
  | <code>Ability to lift 25 lbs. or more as required for handling athletic equipment.</code>                  | <code>Handled and organized equipment, ensuring safe lifting of heavy items</code> | <code>Coordinated scheduling for team practices and meetings</code>                       |
  | <code>The candidate should have significant development experience, preferably around 10 years.</code>     | <code>developed and implemented data architecture projects for a decade</code>     | <code>worked in customer service for 5 years</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: 510 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: 8 tokens</li><li>mean: 16.39 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.34 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.41 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
  | anchor                                                                                         | positive                                                                              | negative                                                       |
  |:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | <code>Qualified to provide personalized and friendly client interactions</code>                | <code>Assisted clients with inquiries and ensured a welcoming environment</code>      | <code>Conducted market research for product development</code> |
  | <code>Understanding of network architecture principles and design patterns is critical.</code> | <code>Designed and implemented network architectures for cloud-based solutions</code> | <code>Managed on-premises server infrastructure</code>         |
  | <code>Knowledge of cloud technologies and their implications for customer engagement.</code>   | <code>Managed customer onboarding for cloud-based services</code>                     | <code>Handled sales inquiries for software licenses</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`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:----:|:------:|:-----------------------:|:------------------------:|
| 0     | 0    | -      | 0.5931                  | -                        |
| 0.625 | 10   | 1.4252 | 0.7505                  | -                        |
| 1.0   | 16   | -      | -                       | 0.6861                   |


### Framework Versions
- Python: 3.10.11
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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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