projfinetuned

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("zlf18/projfinetuned")
# Run inference
sentences = [
    'Customer Engineering Manager, State Local Education, Public Sector',
    "Customer Engineering Manager, State Local Education, Public Sector Google Lead a team of Customer Engineers and build a thriving growth culture. Focus on talent strategy and skills development to deliver on successful cloud transformation outcomes for our customers and accelerate business goals for your territory.\nFoster strong partnerships with key customers across the book of business. Provide leadership related to cloud, transformation and relevant industry trends.\nPartner with Google Cloud Sales leadership to define technical go-to-market strategies and execution plan for the team's book of business.\nBalance technical leadership with operational excellence; lead workload and opportunity review meetings and provide insight into how to achieve a technical agreement and migration strategy, working directly with our customers, partners, and prospects.\nWork cross-functionally across Google, our partners, and your team to resolve technical roadblocks including capacity needs, constraints and product issues affecting customer satisfaction. Bachelor's degree or equivalent practical experience.\n10 years of experience with cloud native architecture in a customer-facing or support role.\n3 years of leadership experience, such as people management, team lead, mentorship, or coaching.\nExperience as a Pre-Sales Manager or a people manager in a technical customer-facing role within a Sales Engineering team.\nAbility to travel up to 50% of the time as needed.\n2 years of experience supporting or selling to state, county, local municipal agencies, and academic institutions.\nExperience with software life-cycles, building tools, and architecting and developing software for scalable, distributed systems, including data platform, AI/ML including Generative AI and infrastructure.\nExperience managing a team through sales processes, operations and career development, including account mapping, quota setting, quarterly/annual performance management, and managing sensitive information.\nExperience presenting to technical stakeholders and executive leaders, including delivering messages by the audience, asking tactical questions, and leading conversations that drive business opportunities.\nGoogle is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.\nGoogle Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.\nTo all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.",
    "Cook 4 - Full Time, $32.58/hour Aulani, A Disney Resort & Spa Prepares, seasons and cooks to order menu items for all meals throughout the day, including Breakfast, Lunch and Dinner meal periods\nPortions and arranges food on serving dishes and is responsible for portion control and plate presentation\nMay cook, mix, and/or season ingredients to make dressings, sauces, gravies, batters, fillings and spreads\nMay wash, peel, slice, scoop, dice and julienne vegetables and fruits\nPrepares, measures, mixes (following recipes) and/or cooks and garnishes basic appetizers (hot or cold), salads, pastas, sandwich fillings, Waffles and other food items\nSome knowledge of cooking equipment such as grill, gas range, electric range, broiler, deep fat fryer, serving table, waffle iron, griddle, skillets and other standard kitchen equipment\nAbility to prepare products according to recipe guidelines\nKnowledge and understanding of kitchen safety and sanitation including temperature requirements\nHas good judgment of food quality and production, understands the impact of spoilage\nAbility to assist Chef in preparing items for Guests with special dietary needs\nCleans kitchen equipment and practices HACCP (Hazard Analysis and Critical Control Points) Procedures\nExplore our commitments and our work to create a better world through our stories, experiences, operations, and philanthropy. Experience in culinary field/high volume restaurant minimum 3-6 months, or up to 1 year\nAbility to multi task and work in a very fast paced team environment\nDemonstrates a desire to work in a guest service and team environment\nDemonstrates passion and enthusiasm for working in the kitchen\nStrong listening skills and ability to follow direction\nEnrolled in a culinary education program or equivalent\nRecommendation from school\nFood Safety Certification or equivalent\nKnowledge of Hawaiian/Japanese language preferred\nBe Part of the Story There are many different brands and businesses to explore. Once you've found the opportunity that is right for you, take the next step by completing your application.\nThere are many different brands and businesses to explore. Once you've found the opportunity that is right for you, take the next step by completing your application.\nGet the latest job opportunities as they become available.\nJob Category Select a Job Category Administration Animation and Visual Effects Architecture and Design Asset Management Banking Building, Construction and Facilities Business Strategy and Development Call Center Communications Creative Culinary Data Science and Analytics Disneyland Resort Casting Hourly Engineering Finance and Accounting Food and Beverage Gaming and Interactive Governmental Affairs Graphic Design Health Services Horticulture and Landscaping Hotel and Resorts Human Resources Legal and Business Affairs Licensing Maritime and Cruise Operations Marketing and Digital Media Merchandising Operations Production Project Management Publishing Quality Assurance Research and Development Retail Operations Sales Sciences and Animal Programs Security Social Responsibility Sports and Recreation Stage Productions Supply Chain Management Talent Technology Theme Park Operations Walt Disney World Casting Hourly\nJob Level Select Professional Operations / Production Internships / Programs Management Business Support / Administrative Executive Talent 100% full coverage of healthcare for you and your eligible dependents\nFree theme park admission and much more!\nCombining the natural beauty and spirit of the Hawaiian islands with a touch of Disney magic, Aulani, a Disney Resort & Spa embraces and celebrates Hawaiian culture and storytelling. Situated on 21 acres of oceanfront property on the island of O‘ahu, the resort was uniquely designed for families to discover the culture, history and traditions of Hawai‘i against a backdrop of blue skies and beautiful views. Cast members are integral to bringing these stories of Hawai‘i to life, while upholding Disney’s renowned service and enchanting entertainment offerings.\nThe Walt Disney Company, together with its subsidiaries and affiliates, is a leading diversified international family entertainment and media enterprise that includes three core business segments: Disney Entertainment, ESPN, and Disney Experiences. From humble beginnings as a cartoon studio in the 1920s to its preeminent name in the entertainment industry today, Disney proudly continues its legacy of creating world-class stories and experiences for every member of the family. Disney’s stories, characters and experiences reach consumers and guests from every corner of the globe. With operations in more than 40 countries, our employees and cast members work together to create entertainment experiences that are both universally and locally cherished.\nWhere Does Your Story Begin? Explore Disney Careers and the Life at Disney blog to learn about all the amazing opportunities waiting to be discovered at The Walt Disney Company.\nExplore Disney Careers and the Life at Disney blog to learn about all the amazing opportunities waiting to be discovered at The Walt Disney Company.\nOur senior executives bring tremendous experience, visionary thinking and a shared commitment to excellence, creativity and innovation to the day to day operation of the company.\nAt Disney, we are committed to creating a better world. A world of belonging where each person feels seen, heard, and understood. A world filled with hope and promise.\nHeroes Work Here reflects the long history of respect and appreciation Disney has for the U.S. Armed Services. We recognize the commitment and dedication it takes to serve your country, both as military personnel and military spouses, and value the leadership skills and sense of purpose it has instilled in you.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5033, 0.0885],
#         [0.5033, 1.0000, 0.0802],
#         [0.0885, 0.0802, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 391 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 391 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 2 tokens</li><li>mean: 7.03 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 240.47 tokens</li><li>max: 256 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>UX Researcher</code>	<code>UX Researcher Dropbox Design and conduct user research studies, including interviews, surveys, and A/B testing.<br>Analyze research data and identify key insights and trends.<br>Create research reports and presentations for stakeholders.<br>Advocate for user needs throughout the product development process. Bachelor's degree in a related field; Master's preferred.<br>3+ years of experience in UX research.<br>Experience with both qualitative and quantitative research methods.<br>Excellent analytical and communication skills. Plan and execute user research to inform product strategy and design. You will be responsible for designing research studies, analyzing data, and presenting findings to product and engineering teams. This role is critical for ensuring products meet the needs of users.</code>
  <code>Senior PC Support Technician</code>	<code>Senior PC Support Technician Public Storage Diagnose systems and equipment failures and perform corrective actions to resolve any issues.<br>Perform preliminary analysis of major systems problems and escalate the problems in accordance with prescribed procedures.<br>Respond to and manage the IT Service Desk ticket queue and provide principal support on incident resolution.<br>Consult with IT teams and various departments to make sure projects are in line with the company’s IT strategies and plans.<br>Collaborate with existing vendors to evaluate the new technologies that come available and make recommendations to management on possible improvements using these technologies.<br>Installation and maintenance of system updates for OS, software and hardware.<br>Monitor the performance of the company’s desktop infrastructure and make suggestions for improving efficiency.<br>Must be Customer Service oriented and show an ability to take ownership of an issue.<br>One of our values pillars is to work as OneTeam and we ...</code>
  <code>Full Stack Software Engineer, GSCCE</code>	<code>Full Stack Software Engineer, GSCCE Boeing Designs, develops, analyzes, and maintains software systems that meet industry, customer and internal quality, safety, security and certification standards.<br>Completes software system-level analyses to identify risks, issues and opportunities; integrates and deploys mitigation actions throughout the software lifecycle.<br>Performs code reviews to ensure alignment to requirements and standards. Monitors and reviews test completion, verification processes, and issue resolution for software systems.<br>Develops user documentation and training to educate end users about the usage of software products.<br>Develop clean, maintainable, and efficient code within the existing codebase, ensuring alignment with established architecture and coding standards.<br>Works under general direction.<br>Get updates about Boeing job opportunities, events and career information. Our teams are currently hiring for a broad range of experience levels including; Associate, Experienced ...</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• 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
• 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: 1
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.1
• Transformers: 4.56.2
• PyTorch: 2.8.0+cu126
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.22.1

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}
}

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