NaqibL/bge-base-sgmarket-v2

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-base-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': 768, 'pooling_mode': 'cls', '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("NaqibL/bge-base-sgmarket-v2")
# Run inference
sentences = [
    'Data Engineer\n\nOur client, one of Asia-Pacific’s leading organizations is looking for: Data Engineer Scope of Work: Key Responsibilities Design, develop and deploy data tables, views and marts in data warehouses, operational data store, data lake and data virtualization.\nPerform data extraction, cleaning, transformation, and flow.\nDesign, build, launch and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks.\nIntegrate and collate data silos in a manner which is both scalable and compliant Collaborate with Product Manager, Data Architect, Business Analysts, Frontend Developers, Designers and Data Analyst to build scalable data-driven products.\nBe responsible for developing backend APIs & working on databases to support the applications.\nWork in an Agile Environment that practices Continuous Integration and Delivery.\nWork closely with fellow developers through pair programming and code review process.\nQualifications: Familiar with GIS platforms: ArcGIS Server, PostGIS Able to use spatial Python libraries: GeoPandas, Shapely Build scalable geospatial data pipelines for ingestion, transformation, storage, and quality checks.\nImplements data cataloging, metadata, lineage, and security; optimizes storage and performance for GIS analytics (e.g., PostGIS, data lakes).\nProvides reliable data services and contracts to support downstream GIS analytics and front-end visualizations, collaborating closely with GIS and frontend teams.\nData management: applies validation, cleansing, reprojection, metadata, and data lineage of geospatial data Designs enterprise geospatial data models, schemas, pipelines, and services; builds or configures web map services and front-end visualization components.',
    "Data Engineer (IT Consultancy, AWS)\n\nYour new company An IT solutions consultancy is looking for an experienced Data Engineer to join their team. Your new role Build and maintain scalable data systems including databases, warehouses, and processing architectures. Design data pipelines for modeling, mining, and production to support analytics and business needs. Clean and prepare raw data using programming tools for descriptive and predictive modeling applications. Optimize data quality, reliability, and efficiency through strategic improvements and system Organize data assets and catalogs for efficient storage, access, and retrieval, tune SQL performance What you'll need to succeed Over 3 years’ experience in data architecture, warehousing, modeling, ETL/ELT, and streaming solutions. Skilled in Kubernetes-based DevOps, container orchestration, CI/CD pipelines, and microservices deployment. Proficient in Oracle SQL/PLSQL development and basic understanding of Oracle database architecture. Experienced in AWS services including EC2, S3, EMR, Redshift, Athena, and Kinesis. Strong programming skills in Python, R, SQL; familiar with Java, Spark, Hive, and Airflow. Hands-on with data crawling, modeling, lake formation, warehouse construction, and machine learning deployment. What you'll get in return In return, you'll be part of an organization that values its employees. You’ll be rewarded with: Structured career growth and plenty of developmental opportunities Hybrid and stable working environment What you need to do now If you're interested in this role, click 'apply now' or for more information and a confidential discussion on this role or to find out about more opportunities in Technology, contact Yuki Cheung or email yuki.cheung@hays.com.sg. Referrals are welcome. At Hays, we value diversity and are passionate about placing people in a role where they can flourish and succeed. We actively encourage people from diverse backgrounds to apply. EA Reg Number: R22110258 | EA License Number: 07C3924 | Company Registration No: 200609504D",
    'Research Assistant\n\nJob Scope: Gather and analyse data and feedback on the research evaluation projects (like Mid-Term reviews and Full-Term reviews) through various modalities such as engaging schools and stakeholders, as well as developing / managing surveys and focused group discussions / interviews.\nCode and verify quantitative and qualitative data in accordance with specified research protocols and coding procedures.\nDocument key processes of research / evaluation work, monitor progress and provide regular updates of projects Requirements: Trained in Data Science and possess working knowledge of Statistics, Measurement and Research.\nAt least 1 year of relevant experience in conducting research and data analytics.\nProficient in Microsoft Office Suite of tools, especially MS Excel.\nFamiliarity with analytical and statistical software and database management such as Tableau.\nStrong conceptual, analytical and problem-solving skills.\nMeticulous and possess leadership qualities to lead and drive results.\nAbility to work well independently and in teams and within tight timelines.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8860, 0.6206],
#         [0.8860, 1.0000, 0.6310],
#         [0.6206, 0.6310, 1.0000]])

Evaluation

Metrics

Triplet

• Dataset: eval
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8534

Training Details

Training Dataset

Unnamed Dataset

• Size: 26,953 training samples

• Columns: sentence_0, sentence_1, and sentence_2

• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 31 tokens mean: 305.72 tokens max: 512 tokens	min: 45 tokens mean: 309.59 tokens max: 512 tokens	min: 41 tokens mean: 291.49 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  Customer Service Officer (6 Months Contract) | Residential Our client is in the Property Mangement Industry. Due to business needs, they are now recruiting for Customer Service Officer to be part of their team for ongoing transformation projects. They are located at Bugis - Walking distance from the MRT station. 5.25 days work week - Mon-Fri: 9am-6pm, Alt Sat: 9am-1pm Duties Coordinate appointment schedules with residents. Ensure workers report punctually for assigned duties. Inform residents promptly in the event of any delay in scheduled appointments. Monitor and track the progress of ongoing work assignments. Requirements Minimum qualification: O Level or equivalent. Excellent communication skills. Interested candidates who wish to apply for the advertised position, please click APPLY NOW or email an updated copy of your resume/cv.	Auto Parts CS Coordinator (Tuas, 5 Days, $3.5k) Salary: up to $3,500 Work Days: 5 Days (9am to 6.15pm) Work Location: Tuas (near MRT) Responsibilities: - Sales Order Processing (prepare quotation, sales order, invoice, D/O & P/O) for domestics and export markets. - Coordinate packing and delivery arrangements for orders. - Follow up on customer enquiries and quotations. - Liaise with internal departments to ensure smooth operations and customer support. - Monitor inventory movement and maintain records. - Assist with general administrative duties as assigned.	10 weeks Temp PSA Patient Service Associate (For Call Centre) GOVT

  Commitment period: minimally till early September Location : Jalan Bukit Merah Central Working Hours: Office hours 5.5days Mon to Fri - 8am to 5pm Every Sat - 8am to 12pm Salary: $10.50 / hour Job Scope - Answer and manage appointments calls and general enquiries from members of public and patients with high service quality and operational standards.

  • Assist to answer calls and provide one-stop information and assistance to callers when required.
  • Handle the feedbacks from patients and resolving service related incidents, as well as enquiries such as the rates of consultations and medical package.
  • Liaise with other departments on the Call Centre Services and procedures - Work with the Call Centre Executive to maintain a high service level of Call Centre Services, and update patients' data when required.
  • Perform other ad-hoc duties as assigned Requirement: - Min N / O / A / NITEC in any field - No experience req... | | URGENT 1 Year Call Centre Officer (Up to $3,000) #NJA

Ubi Any 5 days of the week from Mon to SUN 8am to 6pm (Mon-Fri), 8am to 5pm (Sat/SUN) Attend to incoming calls Provide customer service Escalate matters to relevant personnel Other ad hoc duties assigned Interestedapplicants can send their detailed resumes to janelui@recruitexpress.com.sgor call JANE @ 6735 1955.
JANE LUI JIE'EN CEI: R1104482 Company Reg.No.
199601303W || EA Licence No.
99C4599 | Customer Service Officer (Live Chat) (Insurance) (Up to $4,000) (12 Months Contract) #NKC

Attend to customer and financial representative enquiries via phone and live chat professionally and promptly.
Resolve complaints and issues at the first touchpoint wherever possible.
Maintain accurate records of interactions in the CRM system.
Ensure timely follow-up on enquiries and escalate when necessary.
Assess appeals and provide recommendations to management.
Support additional duties during operational needs or peak periods.
Interested applicants may email resume to kellychooi@recruitexpress.com.sg Chooi Kelly (CEI Registration No: R25136207) Recruit Express Pte Ltd (EA: 99C4599) | 10 weeks Temp PSA Patient Service Associate (For Call Centre) GOVT

  Commitment period: minimally till early September Location : Jalan Bukit Merah Central Working Hours: Office hours 5.5days Mon to Fri - 8am to 5pm Every Sat - 8am to 12pm Salary: $10.50 / hour Job Scope - Answer and manage appointments calls and general enquiries from members of public and patients with high service quality and operational standards.

  • Assist to answer calls and provide one-stop information and assistance to callers when required.
  • Handle the feedbacks from patients and resolving service related incidents, as well as enquiries such as the rates of consultations and medical package.
  • Liaise with other departments on the Call Centre Services and procedures - Work with the Call Centre Executive to maintain a high service level of Call Centre Services, and update patients' data when required.
  • Perform other ad-hoc duties as assigned Requirement: - Min N / O / A / NITEC in any field - No experience req... | | CUSTOMER SERVICE

Ensure customer bookings are promptly documented, processed and reviewed for accuracy and completeness. Input export job reference. Any special shipment requirements shall be resolved with the shipper prior accepting the booking. Keep Sales Personnel about their bookings. Upon receipt of booking from shipper, Customer Service will book shipment direct with shipping lines or our consol for both FCL and LCL cargo. After confirmation of space with shipping lines or consol, Customer Service will advise shipper via email or fax. Customer Service will proceed to arrange the trucking and collection of cargo if customer require this service. Any changes in vessel details or delay in arrival date will made known to shipper via phone or email by Customer Service. Ensure all cargoes send in good condition and if any damage shall revert to customer immediately. Verify vendor’s invoice and close files. Other ad-hoc duties as assigned by the supervisor | Studio Ambassador (am Pilates)

  As our Studio Ambassador , you have an influence over our members by fostering a positive atmosphere, building community engagement, promoting membership growth. We are seeking driven and customer-oriented individuals who are willing to go to the extra mile for our members. Join us and be part of a dynamic and upbeat team that will value add your learning and growth as a Studio Ambassador ! Your contributions to our joint success: Welcome members to the studio and provide exceptional customer service Manage studio operations, including scheduling, check-ins, and maintaining a clean and organized environment Promote studio services, driving membership sales and retention Engage with the local community through social gatherings, workshops, and member appreciation events to strengthen relationships Contribute ideas and collect feedback to improve the overall client experience and studio atmosphere What does it take to be a Studio Ambassador: Passion for he... | Service Desk Agent ( Interns)

  We are hiring Internship, you will be equipped with the necessary skillset and knowledge to better prepare you in securing your ideal job, especially in the digital space. Responsibilities: To log, validate and diagnose customer issues, on the full range of products and applications used at the customer site. Providing the customer with a solution through information gathering, analytical trouble shooting and problem research, or to route or escalate the call to the appropriate resolution group. Ensure escalation and management of calls is to agreed service levels. Requirement Proficient in Microsoft Office. Knowledge in desktop operating systems, various software applications and basic hardware for the PC; principles and theories of network systems and Internet technology. Good oral and written communication skills and ability to establish and maintain effective working relationship. Ability to coordinate several diverse job requirements and projects. Ab... |

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• 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_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• 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: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	eval_cosine_accuracy
1.0	211	-	0.7835
2.0	422	-	0.8419
2.3697	500	3.0351	-
3.0	633	-	0.8534
-1	-1	-	0.8534

Training Time

• Training: 4.7 hours

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.0
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.8.5
• Tokenizers: 0.22.2

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}