Mickele-hub/cv-ai-embedding-model

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps inputs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    "SummaryHighly motivated Sales Associate with extensive customer service and sales experience. Outgoing sales professional with track record of driving increased sales, improving buying experience and elevating company profile with target market.\nHighlightsLabVIEW, Virtuoso (Cadence) Design Suite, P LE CS, Cadence Schematic editor, AWR Microwave Office,\n   MATLAB/SIMULINK, Code Composer Studio, National Instruments PXI, Texas Instruments VLCT for IC Testing-\n   ATE\n   Programming Languages: C, C++, Python, Assembly and VHDL\nExperience06/2014toPresentElectrical Design Engineer|,,Working on developing next generation intelligent down-hole oil drilling tools.Working on automating Calibration process for Accelerometers and Magnetometers Working on developing automated test systems using LabVIEW and Python.toResearch AssistantRockwell Automation, Inc.|,,Worked on developing high accuracy GPS System and also on finding outliers in large collection of Cotton fiber cross-section scan data.toProduct Engineering InternDartmouth College|,,www.physitechelectronics.com) Developed Hardware and Software for various Major projects on embedded systems with variety of sensors.Developed Firmware for a Programmer to program AVR Microcontrollers through USB Port Served as the President of The Electronics Club, SNIST, Hyderabad for two years (2010-2012) Electronix Club is a student's organization founded in 2009 in Sreenidhi Institute of Science and Technology, Hyderabad, India by a group of electronics freaks to learn and share the concepts of electronics and prosper in the process Taught classes on basic Analog Circuits, Microcontroller Programming and Sensor interfaces during this period.\nEducationExpected inMay, 2014totoMaster of Science|Electrical EngineeringTexas Tech University,Lubbock,TXGPA:GPA: 3.9/4.0Electrical Engineering GPA: 3.9/4.0Expected inMay, 2012totoBachelor of Technology|Electronics and Communications EngineeringJawaharlal Nehru Technological University,,GPA:GPA: 3.7/4.0Electronics and Communications Engineering GPA: 3.7/4.0\nSkillsArchitect, basic, C, C++, CAD, Cadence, Calibration, com, Hardware, editor, Electronics, embedded systems, GPS, LabVIEW, MATLAB, Office, Microwave, Composer, Networks, next, oil, Programmer, Programming, prosper, Python, USB, VHDL",
    'At Monument, were building a vertical software platform for the self-storage industry. This is a rare opportunity to reinvent a $30 billion market from the ground up.As a Lead Engineer, you will work closely with engineering and product leadership to design and build the first version of Monuments software platform from scratch, using the most modern technologies available - no tech debt, no legacy systems, just a blank whiteboard. Your ResponsibilitiesDesign highly-scalable solutions for an enterprise application.Lead a team of engineers to deliver quality software, on time and on budget.Empower your teammates to tackle projects by assisting with technical planning, reviewing code, and promoting strong engineering practices. Participate in interviewing and hiring engineers.Troubleshoot and fix technical issues quickly. Who You Are5+ years of experience building web apps; 3+ years building B2B SaaS apps.Experience leading a technical implementation.Experience with JavaScript, React, Node.js, and Nest.js (or similar framework).Experience with one or more object-oriented programming languages.Experience with MySQL or other relational database management systems.Experience optimizing web apps for both performance and scalability. You have a deep understanding of and experience writing effective unit tests.You are passionate about building something from scratch.You thrive in a team setting and are excited to mentor other engineers - you see their successes as your own. You care as much about building great products as you do writing great code. Comp & BenefitsCompetitive salary and equity grantMedical, Dental, and VisionUnlimited PTO Remote Work PolicyMonument supports remote work for this position. We also have an office in Dallas, TX where team members are welcome to work.To ensure alignment and cohesion, Monument holds quarterly in-person all-hands gatherings in the Dallas, Texas area. All team members are expected to attend.',
    'Data EngineerLength of Assignment: 6+ monthsWork Location: Remote US (NJ preferred)\nSUMMARY OF ESSENTIAL JOB FUNCTIONS:Design and develop analytical models and be the face to the data consumers.Perform data curation to meet the business requirements.Build batch and streaming data pipelines.Develop processes for automating, testing, and deploying your work.Identify risks and opportunities of potential logic and data issues within the data environment.Collaborate effectively with the global team and ensure day to day deliverables are met.\nMINIMUN REUIREMENTS:MUST HAVE experience with GCPGoogle Cloud Platform andor Spark.Bachelors degree and 5+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets.3+ years of experience as Data Engineer or in a similar roleProven experience with AWS andor GCP, Hadoop, Vertica, Talend, Tableau, and other modern technology platforms is required.Cloud to Cloud migration experience preferred.Have knowledge of data management fundamentals and data storage principlesHave knowledge of systems as it pertains to data storage and computing.Strong source to target mapping experience and ETL principlesknowledge.Excellent verbal and written communication skills.Strong quantitative and analytical skills with accuracy and attention to detailAbility to work well independently with minimal supervision and can manage multiple priorities.',
]
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,  0.1306,  0.0253],
#         [ 0.1306,  1.0000, -0.0420],
#         [ 0.0253, -0.0420,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 6,241 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  modality	text	text
  details	min: 128 tokens mean: 128.0 tokens max: 128 tokens	min: 19 tokens mean: 125.45 tokens max: 128 tokens	min: 0.0 mean: 0.43 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  QUALIFICATIONS•   Over two years' experience of data analysis at Loyola Chicago University; providing statistical analysis plan and generating report, tables, graphs; proficient in R and SAS.•   Proficient in Microsoft Office, Word, Excel and Power Point.•   Ability to effectively organize and manage multiple assignments with hard deadlines.•   Great team player and extensive experience and skills in collaboration and communication. PROFESSIONAL EXPERIENCE09/2016toCurrentData Analyst InternEnvestnet|Secaucus,NJ,•    Merge spreadsheets from different sources to create a more compact and centralized data platform for easy updating and data analysis using MS Excel advanced functions and formulas.•    Data cleaning and data validation of existing spreadsheets to promote a robust data management platform for accurate data entry and data analysis.•    Train employees on the use of pivot tables and tips and tricks in MS Excel for efficient data entry and analysis.07/2013to04/2014Research Assi...	HYBRID - In office Wednesdays in Chesterfield, Missouri (Work from home 4 days per week) Contract to Hire - USC or GCH REQUIRED - No sponsorship offered Sr. IT BA (Agile)Will be a member of a highly technical, custom proprietary software application engineering team working closely with in-house actuaries, data scientists and data engineers.Team size: ~102 week sprints production cycles Required ExperiencePrevious experience (min 7 years) as an Agile BA working on a technical, development team that created proprietary, custom application(s).Knowledge of backend API integrations and custom development software; experience integrating products with APIs. Ability to support software engineers fleshing out details by writing clearly defined user stories, acceptance criteria for technical stories, and supporting engineers through development, as well as backlog refinement. Sound interesting? Let's talk!!!!	0.0
  Career OverviewHighly capable, versatile, and accomplished IT professional with more than 20 years of experience in planning and implementing all aspects of a Linux/UNIX-based network. Demonstrated ability in network design and configuration as well as user-oriented, security conscious history; expertise in problem solving with outstanding research skills, able to work efficiently and deliver results regardless of previous experience. Possess well-honed leadership and interpersonal skills, experienced in supervising various IT professionals, engineers, and Ph.D. holders as well as experts in other technical fields. Reliable team player, capable of working independently and prioritizing tasks, utilizing flexible approaches. QualificationsLinux System Administration (RedHat, CentOS, Ubuntu) * UNIX (Dec, Sun, HP, and SGI variants) System Administration * Cisco SAN Switch Configuration (MDS9000)* Network Hardware Configuration (Bay Networks, Cisco, Linksys) * iSCSI Legato Networker * EMC ...	The Staff Accountant will report to the CFO and work closely with members of the Finance department. The Staff Accountant will be responsible for monthly and daily reconciliations and assisting in the preparation of all annual tax compliance. Responsibilities:Monitor day-to-day financial transactions across multiple entities, including cash movement related to investment capital activity, personal expensescharitable giving, amongst other cash activity.Record journal entries such as cash transfers between entities, as well as investment activities, such as stock splitmerger, purchasesale, capital calldistribution, etc.Assist team with dailyweekly imports and reconciliation of transactions to accounting software.Review monthly financial statements such as balance sheet, income statement, cash flow, open positions and private equity summary; book corresponding adjusting entries as necessaryAssist in the gathering of data for all tax reportingPerform reconciliations and recording of Form 1...	0.0
  ProfileSoftware Test Engineer with two years of experience and a recent Computer Information Systems degree. Earned a reputation as a hard worker with a strong work ethic and a team player. Regularly volunteer for extra work or helps others to get the job done. SkillsMicrosoft Test Management, Jira, VMWare, Test Complete, Test Execute, SeleniumProgramming: C#, C++, Java, JavaScript, .Net, HTML, XML,Software: Microsoft Office, Microsoft Visio, Microsoft Project, Microsoft Visual Studio, Microsoft Team Foundation ServerCertificationsCompTIA Security+, June 2013, CompTIA A+, May 2013, Microsoft Windows 7, August 2013 Professional Experience10/2015toPresentSoftware Test EngineerConduent Incorporated–Elkridge,MD,Work on the software testing for the Navy System for Naval Target Control.Troubleshoot the software to find any imperfections causing issues with the program's functionality.Created test scripts in Word according to the requirements designated by the Navy.Modified the proprietary te...	Who We Are Apex Fintech Solutions (AFS) powers innovation and the future of digital wealth management by processing millions of transactions daily, to simplify, automate, and facilitate access to financial markets for all. Our robust suite of fintech solutions enables us to support clients such as Stash, Betterment, SoFi, and WeBull, and more than 20 million of our clients' customers. Collectively, AFS creates an environment in which companies with the biggest ideas in fintech are empowered to change the world. We are based in Dallas, TX and also have offices in Austin, New York, Chicago, Portland, and Belfast. If you are seeking a fast-paced and entrepreneurial environment where you'll have the opportunity to make an immediate impact, and you have the guts to change everything, this is the place for you. AFS has received a number of prestigious industry awards, including: 2021, 2020, 2019, and 2018 Best Wealth Management Company - presented by Fintech Breakthrough Awards2021 Most Inno...	0.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss",
      "cos_score_transformation": "torch.nn.modules.linear.Identity"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• num_train_epochs: 4
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• per_device_train_batch_size: 16
• num_train_epochs: 4
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 16
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• dataloader_multiprocessing_context: None
• dataloader_in_order: True
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}
• warmup_ratio: None

Training Logs

Epoch	Step	Training Loss
1.2788	500	0.1312
2.5575	1000	0.0876
3.8363	1500	0.0688

Training Time

• Training: 2.2 hours

Framework Versions

• Python: 3.13.14
• Sentence Transformers: 6.0.1
• Transformers: 5.17.0
• PyTorch: 2.14.0+cpu
• Accelerate: 1.15.0
• Datasets: 5.0.1
• Tokenizers: 0.23.2

Additional Resources

• Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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",
}