---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8137
- loss:CosineSimilarityLoss
base_model: distilbert/distilroberta-base
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/distilroberta-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8711224171717953
name: Pearson Cosine
- type: spearman_cosine
value: 0.8269886257122767
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8510242443923921
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8224876706713816
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8563696604724638
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8221599636921783
name: Spearman Euclidean
- type: pearson_dot
value: 0.8482029844070074
name: Pearson Dot
- type: spearman_dot
value: 0.8223271611305473
name: Spearman Dot
- type: pearson_max
value: 0.8711224171717953
name: Pearson Max
- type: spearman_max
value: 0.8269886257122767
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). 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/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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: RobertaModel
(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/distilroberta-base-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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.8711 |
| **spearman_cosine** | **0.827** |
| pearson_manhattan | 0.851 |
| spearman_manhattan | 0.8225 |
| pearson_euclidean | 0.8564 |
| spearman_euclidean | 0.8222 |
| pearson_dot | 0.8482 |
| spearman_dot | 0.8223 |
| pearson_max | 0.8711 |
| spearman_max | 0.827 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 8,137 training samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 16.7 tokens
- max: 40 tokens
| - min: 5 tokens
- mean: 10.46 tokens
- max: 24 tokens
| |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
| Ability to use tools such as power drills as required for the job. | Proficient in operating power tools for installation tasks | 1 |
| Experience with networking, specifically the TCP/IP stack, routing, ports, and services is essential. | Designed user interfaces for web applications | 0 |
| Ability to establish and maintain positive relationships with coaches, student-athletes, and vendors regarding equipment selection. | Developed strong partnerships with vendors forEquipment procurement | 1 |
* Loss: [CosineSimilarityLoss](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: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 16.2 tokens
- max: 36 tokens
| - min: 5 tokens
- mean: 10.47 tokens
- max: 22 tokens
| |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:---------------|
| Experience with vulnerability management tools like Nessus and Nexpose. | managed network configurations | 0 |
| Willingness to obtain a Texas fire extinguishers license as necessary. | Currently pursuing a Texas fire extinguishers license | 1 |
| Experience in defining and maintaining enterprise architecture that supports business scalability. | Led the development of enterprise architecture frameworks for a multinational corporation | 1 |
* Loss: [CosineSimilarityLoss](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`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters
Click to expand
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional