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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8137
- loss:CosineSimilarityLoss
base_model: distilbert/distilbert-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: Proficient in chemical or plasma cleaning methods.
sentences:
- Skilled in circuit board assembly
- Created custom reports in Workday for HR metrics
- Developed a website using HTML and CSS
- source_sentence: Expertise in data modeling, SQL query design, and execution, preferably
in the financial services sector.
sentences:
- over 2 years of working in a retail customer support role
- Operated a forklift for material handling
- Proficient in crafting SQL queries for large datasets
- source_sentence: The ability to collaborate across teams and adapt to a fast-paced
environment is highly valued.
sentences:
- Demonstrated flexibility in meeting tight deadlines while working with cross-functional
teams
- Processed confidential client documents with high attention to detail
- Assisted with quality control checks on finished products
- source_sentence: Experience advocating for clients while effectively managing tough
conversations.
sentences:
- Designed responsive web layouts with HTML and CSS
- managed BIM coordination projects using Navisworks
- Focused solely on administrative tasks without client involvement
- source_sentence: Knowledge of medical equipment and veterinary terminology is necessary.
sentences:
- Conducted electrical system design reviews
- Skilled in component sorting for various projects
- Worked as a pet trainer for obedience classes
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.924349195128016
name: Pearson Cosine
- type: spearman_cosine
value: 0.8484422411286455
name: Spearman Cosine
- type: pearson_manhattan
value: 0.905333549482094
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8466001874220329
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9058195955220477
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8467373800357263
name: Spearman Euclidean
- type: pearson_dot
value: 0.9171267699712237
name: Pearson Dot
- type: spearman_dot
value: 0.8472543590835093
name: Spearman Dot
- type: pearson_max
value: 0.924349195128016
name: Pearson Max
- type: spearman_max
value: 0.8484422411286455
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9188359916169351
name: Pearson Cosine
- type: spearman_cosine
value: 0.8446914904867927
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8975506707051996
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8409328944635871
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8980683704843317
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8413207901292724
name: Spearman Euclidean
- type: pearson_dot
value: 0.9108792364321198
name: Pearson Dot
- type: spearman_dot
value: 0.8438956330799119
name: Spearman Dot
- type: pearson_max
value: 0.9188359916169351
name: Pearson Max
- type: spearman_max
value: 0.8446914904867927
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilbert-base-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-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:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **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: DistilBertModel
(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/distilbert-base-uncased-sts")
# Run inference
sentences = [
'Knowledge of medical equipment and veterinary terminology is necessary.',
'Worked as a pet trainer for obedience classes',
'Skilled in component sorting for various projects',
]
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 | 0.9243 |
| **spearman_cosine** | **0.8484** |
| pearson_manhattan | 0.9053 |
| spearman_manhattan | 0.8466 |
| pearson_euclidean | 0.9058 |
| spearman_euclidean | 0.8467 |
| pearson_dot | 0.9171 |
| spearman_dot | 0.8473 |
| pearson_max | 0.9243 |
| spearman_max | 0.8484 |
#### 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 | 0.9188 |
| **spearman_cosine** | **0.8447** |
| pearson_manhattan | 0.8976 |
| spearman_manhattan | 0.8409 |
| pearson_euclidean | 0.8981 |
| spearman_euclidean | 0.8413 |
| pearson_dot | 0.9109 |
| spearman_dot | 0.8439 |
| pearson_max | 0.9188 |
| spearman_max | 0.8447 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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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: 8,137 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.34 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.58 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~49.50%</li><li>1: ~50.50%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
| <code>Ability to use tools such as power drills as required for the job.</code> | <code>Proficient in operating power tools for installation tasks</code> | <code>1</code> |
| <code>Experience with networking, specifically the TCP/IP stack, routing, ports, and services is essential.</code> | <code>Designed user interfaces for web applications</code> | <code>0</code> |
| <code>Ability to establish and maintain positive relationships with coaches, student-athletes, and vendors regarding equipment selection.</code> | <code>Developed strong partnerships with vendors forEquipment procurement</code> | <code>1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 2,035 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.77 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.65 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>0: ~48.10%</li><li>1: ~51.90%</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:---------------|
| <code>Experience with vulnerability management tools like Nessus and Nexpose.</code> | <code>managed network configurations</code> | <code>0</code> |
| <code>Willingness to obtain a Texas fire extinguishers license as necessary.</code> | <code>Currently pursuing a Texas fire extinguishers license</code> | <code>1</code> |
| <code>Experience in defining and maintaining enterprise architecture that supports business scalability.</code> | <code>Led the development of enterprise architecture frameworks for a multinational corporation</code> | <code>1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
#### 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`: 4
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
| 0.1965 | 100 | 0.1588 | 0.0884 | 0.8247 | - |
| 0.3929 | 200 | 0.0784 | 0.0686 | 0.8397 | - |
| 0.5894 | 300 | 0.067 | 0.0538 | 0.8455 | - |
| 0.7859 | 400 | 0.0626 | 0.0482 | 0.8450 | - |
| 0.9823 | 500 | 0.0533 | 0.0452 | 0.8454 | - |
| 1.1788 | 600 | 0.0346 | 0.0437 | 0.8434 | - |
| 1.3752 | 700 | 0.0328 | 0.0435 | 0.8465 | - |
| 1.5717 | 800 | 0.0306 | 0.0445 | 0.8465 | - |
| 1.7682 | 900 | 0.0317 | 0.0399 | 0.8481 | - |
| 1.9646 | 1000 | 0.0315 | 0.0448 | 0.8517 | - |
| 2.1611 | 1100 | 0.017 | 0.0388 | 0.8489 | - |
| 2.3576 | 1200 | 0.016 | 0.0396 | 0.8501 | - |
| 2.5540 | 1300 | 0.0129 | 0.0393 | 0.8465 | - |
| 2.7505 | 1400 | 0.0128 | 0.0396 | 0.8471 | - |
| 2.9470 | 1500 | 0.0147 | 0.0388 | 0.8483 | - |
| 3.1434 | 1600 | 0.009 | 0.0396 | 0.8460 | - |
| 3.3399 | 1700 | 0.0078 | 0.0390 | 0.8460 | - |
| 3.5363 | 1800 | 0.0063 | 0.0380 | 0.8475 | - |
| 3.7328 | 1900 | 0.0079 | 0.0377 | 0.8484 | - |
| 3.9293 | 2000 | 0.0062 | 0.0376 | 0.8484 | - |
| 4.0 | 2036 | - | - | - | 0.8447 |
### 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",
}
```
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