---
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 \nservices. Please read this manual carefully and consult with\
\ your supervisor or the department \nmanager if you have any questions or need\
\ further clarification. \n \nDepartment Overview \nThe Transportation Department\
\ plays a critical role in providing reliable transportation services to \nour\
\ customers. Our department consists of 50 drivers, 10 dispatchers, and 5 maintenance\
\ \ntechnicians. In the past year, we transported over 500,000 passengers across\
\ various routes, ensuring \ntheir safety and satisfaction. \n \nSafety and Vehicle\
\ Maintenance \nSafety 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 \nup-to-date knowledge of transportation laws and regulations.\
\ In the past year, we conducted 20 \ncompliance audits to ensure adherence to\
\ regulatory requirements. \n \nTraining and Development \nContinuous training\
\ and development are vital for our department's success. In the past year, our\
\ \ndrivers completed over 100 hours of professional development training, focusing\
\ on defensive \ndriving, customer service, and emergency preparedness. \n \n\
Communication and Collaboration \nEffective communication and collaboration are\
\ essential within the Transportation Department and"
- "Customer Service \nWe prioritize exceptional customer service. Our drivers are\
\ trained to provide a friendly and \nrespectful experience to all passengers.\
\ In the past year, we received an average customer \nsatisfaction rating of 4.5\
\ out of 5, demonstrating our commitment to meeting customer needs and \nexceeding\
\ their expectations. \n \nIncident Reporting and Investigation \nAccidents or\
\ incidents may occur during transportation operations. In such cases, our drivers\
\ are \ntrained to promptly report incidents to their supervisor or the incident\
\ response team. In the past \nyear, we reported and investigated 10 incidents,\
\ implementing corrective actions to prevent future \noccurrences. \n \nCompliance\
\ 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.\
\ \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."
- "for familiarizing themselves with the latest version of the manual. \n \nConclusion\
\ \nThank you for reviewing the Transportation Department Policy Manual. Your\
\ commitment to safety, \ncustomer service, and compliance plays a crucial role\
\ in our department's success. If you have any \nquestions or need further information,\
\ please reach out to your supervisor or the department \nmanager. Your dedication\
\ and professionalism are appreciated."
- "Leaves of Absence \nWe provide various types of leaves of absence, including\
\ vacation leave, sick leave, parental leave, \nand bereavement leave. Employees\
\ are entitled to 15 days of paid vacation leave per year. The \naverage sick\
\ leave utilization in 2022 was 4.2 days per employee. We offer flexible parental\
\ leave \npolicies, allowing employees to take up to 12 weeks of leave after the\
\ birth or adoption of a child. \n \nCompensation and Benefits \nOur employees\
\ receive competitive compensation packages. In 2022, the average annual salary\
\ \nacross all positions was $60,000. We offer a comprehensive benefits package,\
\ including health \ninsurance, 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 \nare required to hold a valid driver's license\
\ and maintain a clean driving record. In the past year, our \ndrivers completed\
\ over 2,000 hours of driving training to enhance their skills and knowledge.\
\ \n \nRoute Planning and Optimization \nEfficient route planning is essential\
\ for timely transportation services. Our department utilizes \nadvanced routing\
\ software to optimize routes and minimize travel time. In the past year, we reduced\
\ \nour average route duration by 15% through effective route planning and optimization\
\ strategies. \n \nCustomer Service"
- "Our fare collection system ensures fair and consistent fee collection from passengers.\
\ The current fee \nstructure is as follows: \n \nRegular fare: $2.50 \nSenior\
\ citizens and students: $1.50 \nChildren under 5 years old: Free \nFee collection\
\ is primarily done through electronic payment methods, such as smart cards and\
\ \nmobile payment apps. Drivers are responsible for ensuring correct fare collection\
\ and providing \nreceipts upon request. \nRoute Information and Rules \nOur transportation\
\ department operates multiple routes within the city. Route information, including\
\ \nmaps, 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 \nclarification. \n \nEqual Employment Opportunity \nOur company is\
\ committed to providing equal employment opportunities to all individuals. We\
\ strive \nto create a diverse and inclusive workplace. In 2022, our workforce\
\ comprised 55% male and 45% \nfemale employees. We actively recruit and promote\
\ individuals from different backgrounds, including \nracial and ethnic minorities.\
\ Our goal is to maintain a workforce that reflects the diverse \ncommunities\
\ we serve. \n \nAnti-Harassment and Anti-Discrimination \nWe 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 \nto all employees on recognizing and preventing harassment.\
\ We encourage employees to report any \nincidents of harassment or discrimination\
\ and ensure confidentiality throughout the investigation \nprocess."
- "Passengers are expected to follow the rules and regulations while utilizing our\
\ transportation \nservices, including: \n \nBoarding and exiting the vehicle\
\ in an orderly manner. \nYielding seats to elderly, disabled, and pregnant passengers.\
\ \nKeeping noise levels to a minimum. \nRefraining from eating, drinking, or\
\ smoking onboard. \nUsing designated safety equipment, such as seat belts, if\
\ available. \nReporting any suspicious activity or unattended items to the driver.\
\ \nAmendments to the Policy Manual \nThis policy manual is subject to periodic\
\ review and amendments. Any updates or changes will be \ncommunicated to employees\
\ through email or departmental meetings. Employees are responsible"
- "Network and Systems Access \nAccess to the organization's network and systems\
\ is granted based on job roles and responsibilities. \nEmployees must adhere\
\ to the network access policies and protect their login credentials. In the past\
\ \nyear, we reviewed and updated access privileges for 300 employees to align\
\ with their job functions. \n \nEmail and Communication \nThe organization's\
\ email system is to be used for official communication purposes. Employees are\
\ \nexpected to follow email etiquette and avoid the use of offensive or inappropriate\
\ language. The \nemail system is monitored for security purposes and to ensure\
\ compliance with policies. We manage \nand maintain an email system that serves\
\ 500 employees. \n \nData Security and Confidentiality"
- source_sentence: How often were departmental meetings conducted to address information
sharing and problem-solving?
sentences:
- "Leaves of Absence \nWe provide various types of leaves of absence, including\
\ vacation leave, sick leave, parental leave, \nand bereavement leave. Employees\
\ are entitled to 15 days of paid vacation leave per year. The \naverage sick\
\ leave utilization in 2022 was 4.2 days per employee. We offer flexible parental\
\ leave \npolicies, allowing employees to take up to 12 weeks of leave after the\
\ birth or adoption of a child. \n \nCompensation and Benefits \nOur employees\
\ receive competitive compensation packages. In 2022, the average annual salary\
\ \nacross all positions was $60,000. We offer a comprehensive benefits package,\
\ including health \ninsurance, dental coverage, retirement plans, and employee\
\ assistance programs. On average, our"
- "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."
- "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"
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: 1.0
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 1.0
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: 1.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 1.0
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 1.0
name: Cosine Mrr@10
- type: cosine_map@100
value: 1.0
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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: 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:
```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("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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 and sentence_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 transportation |
| What 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 transportation |
| What 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"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`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin