metadata

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 model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with 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 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

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: 16.34 tokens
max: 40 tokens

min: 5 tokens
mean: 9.58 tokens
max: 24 tokens

0: ~49.50%
1: ~50.50%

	sentence1	sentence2	score
type	string	string	int
details	min: 6 tokens mean: 16.34 tokens max: 40 tokens	min: 5 tokens mean: 9.58 tokens max: 24 tokens	0: ~49.50% 1: ~50.50%

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 with these parameters:

{
    "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: 15.77 tokens
max: 34 tokens

min: 5 tokens
mean: 9.65 tokens
max: 21 tokens

0: ~48.10%
1: ~51.90%

	sentence1	sentence2	score
type	string	string	int
details	min: 6 tokens mean: 15.77 tokens max: 34 tokens	min: 5 tokens mean: 9.65 tokens max: 21 tokens	0: ~48.10% 1: ~51.90%

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 with these parameters:

{
    "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

Click to expand

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

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

@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",
}