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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2036
- loss:MultipleNegativesRankingLoss
base_model: google-bert/bert-base-uncased
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
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
pipeline_tag: sentence-similarity
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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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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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