metadata

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 model finetuned from 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
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: 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:

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/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]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with 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 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

Training Details

Training Dataset

Unnamed Dataset

Size: 2,036 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 16.07 tokens max: 39 tokens	min: 7 tokens mean: 11.23 tokens max: 24 tokens	min: 5 tokens mean: 8.39 tokens max: 15 tokens

Samples:

anchor	positive	negative
`Sensitivity to the needs of patients, families, and physicians to deliver compassionate care.`	`worked closely with families to address patient concerns`	`specialized in technical equipment management without direct patient contact`
`Ability to lift 25 lbs. or more as required for handling athletic equipment.`	`Handled and organized equipment, ensuring safe lifting of heavy items`	`Coordinated scheduling for team practices and meetings`
`The candidate should have significant development experience, preferably around 10 years.`	`developed and implemented data architecture projects for a decade`	`worked in customer service for 5 years`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 510 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 16.39 tokens max: 34 tokens	min: 6 tokens mean: 11.34 tokens max: 20 tokens	min: 5 tokens mean: 8.41 tokens max: 16 tokens

Samples:

anchor	positive	negative
`Qualified to provide personalized and friendly client interactions`	`Assisted clients with inquiries and ensured a welcoming environment`	`Conducted market research for product development`
`Understanding of network architecture principles and design patterns is critical.`	`Designed and implemented network architectures for cloud-based solutions`	`Managed on-premises server infrastructure`
`Knowledge of cloud technologies and their implications for customer engagement.`	`Managed customer onboarding for cloud-based services`	`Handled sales inquiries for software licenses`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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

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: no_duplicates
multi_dataset_batch_sampler: proportional

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

@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

@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}
}