metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:48
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: >-
What types of training did the drivers complete in the past year to
enhance their skills?
sentences:
- >-
department. It provides guidelines to ensure safe, efficient, and
customer-focused transportation
services. Please read this manual carefully and consult with your
supervisor or the department
manager if you have any questions or need further clarification.
Department Overview
The Transportation Department plays a critical role in providing
reliable transportation services to
our customers. Our department consists of 50 drivers, 10 dispatchers,
and 5 maintenance
technicians. In the past year, we transported over 500,000 passengers
across various routes, ensuring
their safety and satisfaction.
Safety and Vehicle Maintenance
Safety is our top priority. All vehicles undergo regular inspections and
maintenance to ensure they
- >-
Compliance with local, state, and federal regulations is crucial. Our
drivers are required to maintain
up-to-date knowledge of transportation laws and regulations. In the past
year, we conducted 20
compliance audits to ensure adherence to regulatory requirements.
Training and Development
Continuous training and development are vital for our department's
success. In the past year, our
drivers completed over 100 hours of professional development training,
focusing on defensive
driving, customer service, and emergency preparedness.
Communication and Collaboration
Effective communication and collaboration are essential within the
Transportation Department and
- >-
Customer Service
We prioritize exceptional customer service. Our drivers are trained to
provide a friendly and
respectful experience to all passengers. In the past year, we received
an average customer
satisfaction rating of 4.5 out of 5, demonstrating our commitment to
meeting customer needs and
exceeding their expectations.
Incident Reporting and Investigation
Accidents or incidents may occur during transportation operations. In
such cases, our drivers are
trained to promptly report incidents to their supervisor or the incident
response team. In the past
year, we reported and investigated 10 incidents, implementing corrective
actions to prevent future
occurrences.
Compliance with Regulations
- source_sentence: >-
Who should be contacted for questions or further information regarding the
HR Policy Manual?
sentences:
- >-
responsible for familiarizing themselves with the latest version of the
manual.
Conclusion
Thank you for reviewing our HR Policy Manual. It serves as a guide to
ensure a positive and inclusive
work environment. If you have any questions or need further information,
please reach out to the HR
department. We value your contributions and commitment to our company's
success.
- >-
for familiarizing themselves with the latest version of the manual.
Conclusion
Thank you for reviewing the Transportation Department Policy Manual.
Your commitment to safety,
customer service, and compliance plays a crucial role in our
department's success. If you have any
questions or need further information, please reach out to your
supervisor or the department
manager. Your dedication and professionalism are appreciated.
- >-
Leaves of Absence
We provide various types of leaves of absence, including vacation leave,
sick leave, parental leave,
and bereavement leave. Employees are entitled to 15 days of paid
vacation leave per year. The
average sick leave utilization in 2022 was 4.2 days per employee. We
offer flexible parental leave
policies, allowing employees to take up to 12 weeks of leave after the
birth or adoption of a child.
Compensation and Benefits
Our employees receive competitive compensation packages. In 2022, the
average annual salary
across all positions was $60,000. We offer a comprehensive benefits
package, including health
insurance, dental coverage, retirement plans, and employee assistance
programs. On average, our
- source_sentence: >-
How much did the average route duration decrease in the past year due to
route planning and optimization?
sentences:
- >-
Our drivers are responsible for operating vehicles safely, following
traffic rules and regulations. They
are required to hold a valid driver's license and maintain a clean
driving record. In the past year, our
drivers completed over 2,000 hours of driving training to enhance their
skills and knowledge.
Route Planning and Optimization
Efficient route planning is essential for timely transportation
services. Our department utilizes
advanced routing software to optimize routes and minimize travel time.
In the past year, we reduced
our average route duration by 15% through effective route planning and
optimization strategies.
Customer Service
- >-
Our fare collection system ensures fair and consistent fee collection
from passengers. The current fee
structure is as follows:
Regular fare: $2.50
Senior citizens and students: $1.50
Children under 5 years old: Free
Fee collection is primarily done through electronic payment methods,
such as smart cards and
mobile payment apps. Drivers are responsible for ensuring correct fare
collection and providing
receipts upon request.
Route Information and Rules
Our transportation department operates multiple routes within the city.
Route information, including
maps, schedules, and stops, is available on our website and at
designated information centers.
- >-
manual carefully and contact the HR department if you have any questions
or need further
clarification.
Equal Employment Opportunity
Our company is committed to providing equal employment opportunities to
all individuals. We strive
to create a diverse and inclusive workplace. In 2022, our workforce
comprised 55% male and 45%
female employees. We actively recruit and promote individuals from
different backgrounds, including
racial and ethnic minorities. Our goal is to maintain a workforce that
reflects the diverse
communities we serve.
Anti-Harassment and Anti-Discrimination
We maintain a zero-tolerance policy for harassment and discrimination.
In the past year, we received
- source_sentence: How many employees are served by the organization's email system?
sentences:
- >-
only two reports of harassment, which were promptly investigated and
resolved. We provide training
to all employees on recognizing and preventing harassment. We encourage
employees to report any
incidents of harassment or discrimination and ensure confidentiality
throughout the investigation
process.
- >-
Passengers are expected to follow the rules and regulations while
utilizing our transportation
services, including:
Boarding and exiting the vehicle in an orderly manner.
Yielding seats to elderly, disabled, and pregnant passengers.
Keeping noise levels to a minimum.
Refraining from eating, drinking, or smoking onboard.
Using designated safety equipment, such as seat belts, if available.
Reporting any suspicious activity or unattended items to the driver.
Amendments to the Policy Manual
This policy manual is subject to periodic review and amendments. Any
updates or changes will be
communicated to employees through email or departmental meetings.
Employees are responsible
- >-
Network and Systems Access
Access to the organization's network and systems is granted based on job
roles and responsibilities.
Employees must adhere to the network access policies and protect their
login credentials. In the past
year, we reviewed and updated access privileges for 300 employees to
align with their job functions.
Email and Communication
The organization's email system is to be used for official communication
purposes. Employees are
expected to follow email etiquette and avoid the use of offensive or
inappropriate language. The
email system is monitored for security purposes and to ensure compliance
with policies. We manage
and maintain an email system that serves 500 employees.
Data Security and Confidentiality
- source_sentence: >-
How often were departmental meetings conducted to address information
sharing and problem-solving?
sentences:
- >-
Leaves of Absence
We provide various types of leaves of absence, including vacation leave,
sick leave, parental leave,
and bereavement leave. Employees are entitled to 15 days of paid
vacation leave per year. The
average sick leave utilization in 2022 was 4.2 days per employee. We
offer flexible parental leave
policies, allowing employees to take up to 12 weeks of leave after the
birth or adoption of a child.
Compensation and Benefits
Our employees receive competitive compensation packages. In 2022, the
average annual salary
across all positions was $60,000. We offer a comprehensive benefits
package, including health
insurance, dental coverage, retirement plans, and employee assistance
programs. On average, our
- >-
responsible for familiarizing themselves with the latest version of the
manual.
Conclusion
Thank you for reviewing our HR Policy Manual. It serves as a guide to
ensure a positive and inclusive
work environment. If you have any questions or need further information,
please reach out to the HR
department. We value your contributions and commitment to our company's
success.
- >-
with other departments. In the past year, we conducted monthly
departmental meetings and
established communication channels to facilitate information sharing and
problem-solving.
Fare Collection and Fee Structure
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 1
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 1
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 1
name: Cosine Recall@1
- type: cosine_recall@3
value: 1
name: Cosine Recall@3
- type: cosine_recall@5
value: 1
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 1
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 1
name: Cosine Mrr@10
- type: cosine_map@100
value: 1
name: Cosine Map@100
SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-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: Snowflake/snowflake-arctic-embed-l
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 dimensions
- 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
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("deepali1021/finetuned_arctic_ft-v2")
# Run inference
sentences = [
'How often were departmental meetings conducted to address information sharing and problem-solving?',
'with other departments. In the past year, we conducted monthly departmental meetings and \nestablished communication channels to facilitate information sharing and problem-solving. \n \nFare Collection and Fee Structure',
"responsible for familiarizing themselves with the latest version of the manual. \n \nConclusion \nThank you for reviewing our HR Policy Manual. It serves as a guide to ensure a positive and inclusive \nwork environment. If you have any questions or need further information, please reach out to the HR \ndepartment. We value your contributions and commitment to our company's success.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 1.0 |
cosine_accuracy@3 | 1.0 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 1.0 |
cosine_precision@3 | 0.3333 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 1.0 |
cosine_recall@3 | 1.0 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 1.0 |
cosine_mrr@10 | 1.0 |
cosine_map@100 | 1.0 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 48 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 48 samples:
sentence_0 sentence_1 type string string details - min: 11 tokens
- mean: 16.25 tokens
- max: 27 tokens
- min: 31 tokens
- mean: 99.96 tokens
- max: 143 tokens
- Samples:
sentence_0 sentence_1 What topics are covered in the Transportation Department Policy Manual?
Transportation Department Policy Manual
Table of Contents:
•
Introduction
•
Department Overview
•
Safety and Vehicle Maintenance
•
Driver Responsibilities
•
Route Planning and Optimization
•
Customer Service
•
Incident Reporting and Investigation
•
Compliance with Regulations
•
Training and Development
•
Communication and Collaboration
•
Fare Collection and Fee Structure
•
Route Information and Rules
•
Amendments to the Policy Manual
•
Conclusion
Introduction
Welcome to the Transportation Department Policy Manual! This manual serves as a comprehensive
guide to the policies, procedures, and expectations for employees working in the transportationWhat is the purpose of the Transportation Department Policy Manual?
Transportation Department Policy Manual
Table of Contents:
•
Introduction
•
Department Overview
•
Safety and Vehicle Maintenance
•
Driver Responsibilities
•
Route Planning and Optimization
•
Customer Service
•
Incident Reporting and Investigation
•
Compliance with Regulations
•
Training and Development
•
Communication and Collaboration
•
Fare Collection and Fee Structure
•
Route Information and Rules
•
Amendments to the Policy Manual
•
Conclusion
Introduction
Welcome to the Transportation Department Policy Manual! This manual serves as a comprehensive
guide to the policies, procedures, and expectations for employees working in the transportationWhat is the primary focus of the Transportation Department as outlined in the manual?
department. It provides guidelines to ensure safe, efficient, and customer-focused transportation
services. Please read this manual carefully and consult with your supervisor or the department
manager if you have any questions or need further clarification.
Department Overview
The Transportation Department plays a critical role in providing reliable transportation services to
our customers. Our department consists of 50 drivers, 10 dispatchers, and 5 maintenance
technicians. In the past year, we transported over 500,000 passengers across various routes, ensuring
their safety and satisfaction.
Safety and Vehicle Maintenance
Safety is our top priority. All vehicles undergo regular inspections and maintenance to ensure they - Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 10per_device_eval_batch_size
: 10num_train_epochs
: 10multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 10per_device_eval_batch_size
: 10per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | cosine_ndcg@10 |
---|---|---|
1.0 | 5 | 0.9431 |
2.0 | 10 | 1.0 |
3.0 | 15 | 1.0 |
4.0 | 20 | 1.0 |
5.0 | 25 | 1.0 |
6.0 | 30 | 1.0 |
7.0 | 35 | 1.0 |
8.0 | 40 | 1.0 |
9.0 | 45 | 1.0 |
10.0 | 50 | 1.0 |
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
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",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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}
}