Fe2x/distilroberta-ai-job-embeddings

SentenceTransformer based on sentence-transformers/all-distilroberta-v1

This is a sentence-transformers model finetuned from sentence-transformers/all-distilroberta-v1 on the ai-job-embedding-finetuning dataset. 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: sentence-transformers/all-distilroberta-v1
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • ai-job-embedding-finetuning

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, 'architecture': 'RobertaModel'})
  (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})
  (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("Fe2x/distilroberta-ai-job-embeddings")
# Run inference
queries = [
    "Deep learning research, large-scale driving data, road scene understanding",
]
documents = [
    "experience where customer success continues to motivate what is next.\n\nNetradyne is committed to building a world-class team of technologists and industry experts to deliver products that improve safety, increase productivity, and optimize collaboration within organizations. With growth exceeding 4x year over year, our solution is quickly being recognized as a significant disruptive technology – that has put ‘legacy’ providers in a “spin” cycle trying to catch up. Our team is growing, and we need forward-thinking, uncompromising, competitive team members to continue to facilitate our growth.\n\nAI Engineer - Deep Learning\n\nWe are looking for a highly independent and self-driven Senior Research Engineer who is passionate about pushing the boundaries of deep learning research, to join our fast-growing technology team. This person should be able to work autonomously, think creatively, and explore new ideas and approaches to tackle complex problems in the field. You will have an opportunity to work with very large-scale real-world driving data. Netradyne analyzes over 100 million miles of driving data every month, covering over 1.25 million miles of US roads. This role provides a unique opportunity to work with cutting-edge technology and tackle complex problems in the field of deep learning using vast real-world datasets. The Deep Learning Research Engineer will have the chance to make a significant impact on road safety and advance the field of deep learning research. If you are driven by curiosity and have a passion for innovation, we encourage you to apply.\n\nResponsibilities\n\nDevelop and implement deep learning algorithms to extract valuable insights from large-scale real-world vision data.Design and commercialize algorithms characterizing driving behavior.Innovate and develop proof-of-concept solutions showcasing novel capabilities.\n\n\nRequirements\n\nPh.D. in Computer Science, Electrical Engineering, or a related field with publications in top conferences (CVPR/NeurIPs/ICML/ICLR).Strong background in deep learning, machine learning, and computer vision.Excellent programming skills – Python.Proficiency in PyTorch or TensorFlow.Experience with training large models with huge datasets.Ability to take abstract product concepts and turn them into reality.Location: San Diego, CA - Hybrid\n\n\nDesired Skills\n\nExperience with image, video, and time-series data.Experience with road scene understanding (objects, lanes, interactions, signs, etc.).Experience with person/driver scene understanding (pose, distracted, eye status etc.).Experience with Predictive analytics.\n\n\nOther Essential Abilities and Skills: \n\nStrong analytical and problem-solving skills.Excellent verbal and written communication skills.Energetic or passionate about AI.Ability to work independently and as part of a team.\n\n\nEconomic Package Includes:\n\nSalary $145,000- $180,000Company Paid Health Care, Dental, and Vision CoverageIncluding Coverage for your partner and dependentsThree Health Care Plan OptionsFSA and HSA OptionsGenerous PTO and Sick Leave401(K) Disability and Life Insurance Benefits$50 phone stipend per pay period\n\nSan Diego Pay Range\n\n$145,000—$180,000 USD\n\nWe are committed to an inclusive and diverse team. Netradyne is an equal-opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status, or any legally protected status.\n\nIf there is a match between your experiences/skills and the Company's needs, we will contact you directly.\n\nNetradyne is an equal-opportunity employer.\n\nApplicants only - Recruiting agencies do not contact.\n\nCalifornia Consumer Privacy Act Notice\n\nThis notice applies if you are a resident of California (“California Consumer”) and have provided Personal Information to Netradyne that is subject to the California Consumer Privacy Act (“CCPA”). We typically collect Personal Information in the capacity of a service provider to our clients, who are responsible for providing notice to their employees and contractors and complying with CCPA requirements.\n\nDuring the past 12 months, we have collected the following categories of Personal Information: (a) identifiers; (b) biometric information (see our Biometric Data Privacy Policy for more information); (c) Internet or other electronic network activity information; (d) geolocation data; (e) Audio, electronic, visual, thermal, olfactory, or similar information; (f) professional or employment-related information (from job applicants and from clients regarding their employees and contractors); and (g) education information (from job applicants). We will not discriminate against any person that exercises any rights under the CCPA.\n\nWe have collected this Personal Information for the business purposes and commercial purposes described in this Policy, including to provide the Services to our clients, process job applications, and for marketing and promotion.\n\nThe sources of such Personal Information are you, our clients and our service providers. We have shared this information this only with our clients (if you are an employee or contractor of them) or our service providers.\n\nIf you are a California Consumer, you have the following rights under the CCPA:\n\nYou have the right to request:The categories and specific pieces of your Personal Information that we’ve collected;The categories of sources from which we collected your Personal Information;The business or commercial purposes for which we collected or sold your Personal Information; andThe categories of third parties with which we shared your Personal Information.You can submit a request to us for the following additional information:The categories of third parties to whom we’ve sold Personal Information, and the category or categories of Personal Information sold to each; andThe categories of third parties to whom we’ve disclosed Personal Information, and the category or categories of Personal Information disclosed to each.You can request that we delete the Personal Information we have collected about you, except for situations when that information is necessary for us to: provide you with a product or service that you requested; perform a contract we entered into with you; maintain the functionality or security of our systems; comply with or exercise rights provided by the law; or use the information internally in ways that are compatible with the context in which you provided the information to us, or that are reasonably aligned with your expectations based on your relationship with us.You have the right to request that we not sell your Personal Information. However, we do not offer this opt-out as we do not sell your Personal Information as that term is defined under the CCPA.\n\nYou can make a request under the CCPA by e-mailing us at privacy@netradyne.com We may request additional information from you to verify your identify. You may also designate an authorized agent to submit a request on your behalf. To do so, we will require either (1) a valid power of attorney, or (2) signed written permission from you. In the event your authorized agent is relying on signed written permission, we may also need to verify your identity and/or contact you directly to confirm permission to proceed with the request.\n\nAs noted above, if your request concerns Personal Information collected in our capacity as a service provider to a client, we are not responsible for responding to the request and may send the request to the client for a response.\n\nGoverning law\n\nThis Services are provided in the United States, and are located and targeted to persons in the United States and our policies are directed at compliance with those laws. If you are uncertain whether this Policy conflicts with the applicable local privacy laws where you are located, you should not submit your Personal Information to Netradyne.",
    'QUALIFICATIONSMust-Have:Bachelor’s Degree in Computer Science, Information Systems, or related field.A minimum of 3-5 years of experience as a data engineer or in a similar role (SQL, Python, etc.)Experience working in cloud environments (AWS, Azure, etc.)Solid understanding of data governance principles and practices.Knowledge of a Data Catalog, Data Lineage, and Data Quality frameworksPrior experience with Data governance tools such as Atlan, Collibra, Alation, Manta, etc. is highly desired.Strong analytical and technical problem-solving skills.Excellent interpersonal and communication skills.Takes ownership and pride in end-to-end delivery of projects and initiatives.Comfort with a data-intensive and high transaction volume environmentDeadline-driven mindsetNice-to-have:Prior experience in Finance and Asset management domain is a plus.Prior experience with Snowflake and DBT is a plus',
    'Qualifications\n\nYour Experience\n\nM.S. or Ph.D degree in Computer Science, Mathematics, Electrical Engineering or related field or equivalent military experience required8+ years industry experience in Machine Learning techniques and data analytics8+ experience in design, algorithms and data structures - Expertise with one or more of the following languages is must - Java, C++, Python, RustExperience with NLP, Recommender Systems, and LLM is strongly preferredExperience with Formal Methods toolchain (z3, cvc5, TLA+) will be a plusExcellent communication skills with the ability to influence at all levels of the organizationA self driven individual contributor and an excellent team player\n\nAdditional Information\n\nThe Team\n\nDrawing on the near real-time data collected through PAN-OS device telemetry, our industry-leading next generation insights product (AIOps for NGFW) gives large cybersecurity operators a force multiplier that provides visibility into the health of their next-generation-firewall (NGFW) devices. It enables early detection of issues at various levels of the stack via advanced time-series forecasting and anomaly detection using novel deep learning techniques. Our goal is to be able to prevent service-impacting issues in critical security infrastructure that operates 24/7/365 with zero false positives and zero false negatives.You will be working on the best large language model in the cyber security industry.\n\nOur Commitment\n\nWe’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.\n\nWe are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.\n\nPalo Alto Networks is \n\nAll your information will be kept confidential according to \n\nThe compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be between $140,100/yr to $220,600/yr. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here.\n\nIs role eligible for Immigration Sponsorship?: Yes',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7183, -0.0743,  0.1433]])

Evaluation

Metrics

Triplet

• Datasets: ai-job-validation and ai-job-test
• Evaluated with TripletEvaluator

Metric	ai-job-validation	ai-job-test
cosine_accuracy	0.9894	1.0

Training Details

Training Dataset

ai-job-embedding-finetuning

• Dataset: ai-job-embedding-finetuning at 90f9c04
• Size: 757 training samples
• Columns: query, job_description_pos, and job_description_neg
• Approximate statistics based on the first 757 samples:

  	query	job_description_pos	job_description_neg
  type	string	string	string
  details	min: 8 tokens mean: 17.65 tokens max: 54 tokens	min: 15 tokens mean: 347.91 tokens max: 512 tokens	min: 10 tokens mean: 358.46 tokens max: 512 tokens

• Samples:

  query	job_description_pos	job_description_neg
  Data Analyst job Zest AI expertise: advanced statistical techniques, data wrangling Python SQL, project management skills	Requirements:- Expertise in data wrangling and manipulation in Python and SQL- Solid understanding of machine learning and statistical analysis- Excellent business acumen and ability to understand and solve complex business problems- Strong coding skills, comfortable with Object-Oriented Programming- Strong communication skills, with the ability to present complex data in a clear and concise manner- Good project management skills, with a proven track record of delivering projects on time and within scope- Bachelor's degree in Computer Science, Statistics, or a related field Perks and benefits:All Zestys experience:The opportunity to join a mission-focused companyPeople – the best part of ZestRobust medical, dental and vision insurance plansAnnual bonus plan participation401(k) with generous matchEmployee Awards and Recognition11 company holidaysWinter break (office closed between Christmas and New Year's Day)Unlimited vacation timeEmployee Resource GroupsGenerous family leave policy (1...	skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. ResponsibilitiesAnalyze raw data: assessing quality, cleansing, structuring for downstream processingDesign accurate and scalable prediction algorithmsCollaborate with engineering team to bring analytical prototypes to productionGenerate actionable insights for business improvements Qualifications Degree 1-3 Years of Experience (industry experience required for years) or Ph.D. Degree 0-2 Years of Experience (in school experience will be considered)with scientists to define/understand work and data pipelines in-labBenchling protocols and templates to capture necessary data and align across teams.Have coding experience SQL, Python, and LIMS Lab Information Systemexperience, industry setting (biotech)Experience (or Gene Data or comparable), Bench Experience in Molecular Biology
  Research Data Analyst hospice care qualitative analysis health equity	experience with work related to health equity and anti-racism, aging, serious illness, hospice or grief, would be preferred. We are seeking an individual who is highly collaborative, mission-driven, and has a strong interest in, and ideally background in, research related to diverse populations, equity, older adults, hospice care, dementia care, and/or policy. A successful candidate is highly organized and able to prioritize multiple deadlines and competing tasks. Working with sensitive participant data requires utmost discretion and confidentiality. This position will be perform duties related to a study that aims to generate data to address inequities in access to and quality of hospice care at end-of-life among Black/African American, Latino/x/Hispanic, Latinx, Asian, Hawaiian Native, Pacific Islander American, or multiracial older adults with dementia, and thus, candidates who identify as Black/African American/ multiracial/Latino/Hispanic OR are fluent in Chinese / Mandarin/ Canto...	Requirements Typically requires 13+ years of professional experience and 6+ years of diversified leadership, planning, communication, organization, and people motivation skills (or equivalent experience). Critical Skills 12+ years of experience in a technology role; proven experience in a leadership role, preferably in a large, complex organization.8+ years Data Engineering, Emerging Technology, and Platform Design experience4+ years Leading large data / technical teams – Data Engineering, Solution Architects, and Business Intelligence Engineers, encouraging a culture of innovation, collaboration, and continuous improvement.Hands-on experience building and delivering Enterprise Data SolutionsExtensive market knowledge and experience with cutting edge Data, Analytics, Data Science, ML and AI technologiesExtensive professional experience with ETL, BI & Data AnalyticsExtensive professional experience with Big Data systems, data pipelines and data processingDeep expertise in Data Archit...
  higher education data analytics, data literacy programs, cloud data storage solutions	Qualifications) High school diploma or equivalent Minimum of 2 years (24 months) of college coursework or work experience in IT-related functions Additional education, training, and work experience may be required based on position requirements Excellent communication skills, both oral and written Demonstrated ability to prioritize and collaborate in a team-oriented environment How To Stand Out (Preferred Qualifications) Experience in a higher education environment Demonstrated experience with cloud data storage solutions Drive to learn and master new technologies and techniques Demonstrated ability to gather requirements and develop data analytics solutions iteratively Experience with SQL query development #DataAnalytics #HigherEducation #CareerOpportunity #CompetitivePay #DataLiteracy At Talentify, we prioritize candidate privacy and champion equal-opportunity employment. Central to our mission is our partnership with companies that share this commitment. We aim to foster a fa...	Contract Duration 6+ monthsPay rate up to $51.07/hr Job Description: Data Analyst is responsible for pulling data to support the trending of product complaints and medical device reports utilizing data that resides in the complaint handling database for all product lines. This will include detailed data reports (e.g. graphs, charts, tables) prepared for routine trending, senior management reviews, ad-hoc requests, and cross-functional requests as needed (e.g. Regulatory, Quality Engineering, R&D). The Data Analyst will establish and maintain complex reporting formulas and templates using reporting tools such as Excel and other databases (e.g. Business Objects). Benefits: Medical, Vision, and Dental Insurance Plans401k Retirement Fund

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

ai-job-embedding-finetuning

• Dataset: ai-job-embedding-finetuning at 90f9c04
• Size: 94 evaluation samples
• Columns: query, job_description_pos, and job_description_neg
• Approximate statistics based on the first 94 samples:

  	query	job_description_pos	job_description_neg
  type	string	string	string
  details	min: 10 tokens mean: 17.56 tokens max: 35 tokens	min: 15 tokens mean: 362.02 tokens max: 512 tokens	min: 17 tokens mean: 321.64 tokens max: 512 tokens

• Samples:

  query	job_description_pos	job_description_neg
  ACH Data Analyst specialized in payment solutions, reconciliation, and Azure expertise	requirements, activities and design. The ACH Data Analyst will develop and interpret analysis and reporting capabilities. They will also monitor performance and quality control plans to identify improvements. Job Description Works closely with ACH Product Manager, Business Analyst, and Support teams Interpret data, analyze results using statistical techniques and provide ongoing reports Research outgoing ACH batches and files and their response files to troubleshoot discrepancies Acquire data from primary or secondary data sources and maintain databases/data systems Identify, analyze, and interpret trends or patterns in complex data sets Work with management to prioritize business and information needs Locate and define new process improvement opportunities Using automated tools to extract data from primary and secondary sources Work with developers to address merchant and or partner impacting issues Assigning numerical value to essential business functions so that business...	experienced data scientist who thrives on innovation and craves the vibrancy of a startup environment. ResponsibilitiesProven experience in applying advanced data science algorithms such as neural networks, SVM, random forests, gradient boosting machines, or deep learning.Demonstrable expertise in at least three classes of advanced algorithms.Prior experience with live recommender systems and their implementation.Proficiency in deep learning frameworks, preferably TensorFlow.Proven track record in implementing scalable, distributed, and highly available systems on Cloud Platform (AWS, Azure, or GCP).Strong machine learning and AI skills.Strong communication skills, adaptability, and a thirst for innovation.High autonomy, ownership, and leadership mentality are crucial as you will be a pivotal member shaping our organization's future.Strong skills in data processing with R, SQL, Python, and PySpark. Nice to haveSolid understanding of the computational complexity involved in model traini...
  Microsoft Dynamics 365 data integration expert, Azure Synapse, REST API development	requirements and building relationships.Drive risk-based data and integration decisions to minimize ERP implementation risks.Lead data extraction, transformation, and loading from legacy sources into Dynamics 365.Design, develop, and troubleshoot integrations with Dynamics 365 and other systems.Develop and maintain documentation for data processes and integration architecture.Enhance the enterprise data strategy in collaboration with leadership.Build and deploy scalable data pipelines and APIs to support evolving data needs.Drive data integrations for future acquisitions and ensure data integrity and governance.Collaborate with stakeholders to design and implement data models, dashboards, and reports. Qualifications for the Enterprise Data Engineer include: Proficiency in ETL processes and tools, preferably with experience in Microsoft Dynamics 365.Knowledge of Azure data platforms and tools like Power Automate, Azure Synapse, SQL database, Power BI, and more.Experience with REST-ba...	Experience with genomics data, and molecular genetics. Distributed computing tools like Ray, Dask, and Spark. Note: We need a Data Scientist with demonstrated expertise in training and evaluating transformers such as BERT and its derivatives.
  Loan Transformation Data Analyst: KNIME data pipelines, SharePoint site creation, VBA for automation	experienced Data Analyst, who is proactive, independent, and comfortable with identifying and resolving blockers. Role includes creating and maintaining centralized SharePoint site and associated content for the overall Data Remediation Transformation Program. Develop and maintain automated workflow tools to facilitate regulatory remediation efforts. Support BAU and analytics processes. You will interact and work closely with multiple areas across the organization, including the broader Institutional Credit Management (ICM) function and the business lines supported by ICM, as we enhance our processes and technology to better deliver for our clients. You will provide data management support to the Transformation teams initiatives. Qualifications:• 10+ years of experience in finance/ project management• Experience and proficiency building data pipelines and performing analytics using KNIME (or similar software)• Experience creating team SharePoint sites and maintaining content to make in...	experience to our customers and maintain the highest standards of protection and availability. Our team thrives and succeeds in delivering high-quality technology products and services in a hyper-growth environment where priorities shift quickly. The ideal candidate is a lead Data Engineer with experience in ETL or ELT processing with SQL/NoSQL databases, a background in transforming existing tech to new open source technologies (ideally Postgres) as well as a strong development background in Spark, Scala, Java and/or Python. Position Responsibilities As a Staff Data Engineer, you will: Focus on multiple areas and provide leadership to the engineering teamsOwn complete solution across its entire life cycleInfluence and build vision with product managers, team members, customers, and other engineering teams to solve complex problems for building enterprise-class business applicationsAccountable for the quality, usability, and performance of the solutionsLead in design sessions and c...

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• 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: 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: 2e-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_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: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	ai-job-validation_cosine_accuracy	ai-job-test_cosine_accuracy
-1	-1	0.9894	1.0

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.0
• Transformers: 4.55.2
• PyTorch: 2.8.0+cu126
• Accelerate: 1.10.0
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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}
}