NaqibL/bge-base-sgmarket-v1

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-v1")
# Run inference
sentences = [
    'Data Modeler\n\nProject Description: To design the logical data models for Producers and Consumers of Enterprise Data.\nTo create physical data model design for the logical data model in the respective data systems.\nTo help create Data Models suitable for Medallion Lakehouse Architecture.\nTo help automate processes and activities involved in Data Modeling.\nSkillset: - Strong experience with Modeling Financial Data.\n- Experience with Databricks or other Lakehouse Systems - Relational Database Expertise (Oracle, MySQL, PostgreSQL, Snowflake) - Basic Programming and Automation experience - Experience with using GenAI related tools or technologies - Strong Communication skills to gather business requirements - Good documentation skills (Confluence, SharePoint) - Knowledgeable in Agile (JIRA) - Data Warehouse Experience (Support/Development)',
    'Python Data Engineer - Cloudera\n\nClient: Bank Sector Technology Operations Hands-on techno-functional role working with technology teams across infrastructure and other divisions to deliver system solutions for the business Involve in development and system integration relevant systems with CDP / CDSW Strong understanding of Cloudera framework including CDP (Cloudera Data Platform) and CDSW (Cloudera Data Science Workbench) Hands-on experience in Big Data solution and development based on Python technologies stack Design and implement secure, scalable and high-performance data processing pipelines Key technologies: Python, SQL, Spark, PySpark, Hadoop, Hive, Impala, Kafka, Zookeeper, Ranger, Atlas, Cloudera Manager, Django, Red Hat Linux, Shell Script, ETL, data pipelines, Parquet, ORC, JSON , basic Java / Scala Good to have financial industry experience, especially global market products , market data analytics , data science projects, and identifying risk factors affecting pricing of products',
    'Analyst - Data Science\n\nAt American Express, our mission is to elevate the customer experience daily by delivering unparalleled products and services.\nWe are seeking a highly motivated individual with a solid background in software engineering, machine learning or/and AI.\nThis role is pivotal in advancing our data science capabilities to bolster enterprise-wide initiatives, in collaboration with our Technology, Marketing, Servicing, and Risk departments.\nRole Overview: As an Analyst, AI/ML, you will play a crucial role in the development and implementation of cutting-edge Generative AI solutions that address significant business challenges.\nThis role focusses on hands-on implementation, rapid prototyping, and collaboration with cross-functional teams to deliver innovative solutions that address real business challenges.\nYour contributions will directly impact our strategic goals, driving innovation and excellence in customer interactions.\nResponsibilities Develop and implement Proof of Concepts (PoCs) prototypes for AI/ML and Generative AI solutions, ensuring alignment with business objectives.\nWrite clean, efficient, and well-documented code for data processing, model training, and deployment.\nCollaborate with the Technology team to integrate solutions into our existing systems and platforms.\nConduct experiments to evaluate model performance and optimize algorithms for scalability.\nStay abreast of the latest trends in AI/ML and actively share knowledge and insights to contribute to the collective growth and technical proficiency of the team.\nCollaborate with the Technology team to integrate solutions into our existing systems and platforms, enhancing functionality and user engagement.\nActively participate in technical discussions and forums across Amex Qualifications Advanced degree (Master/ PhD) in quantitative discipline (e.g., Computer Science, Engineering, Statistics, Mathematics).\nStrong programming skills in at least one language (Python, Java, C++, etc.) Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and data processing libraries Extensive experience in machine learning, NLP and Generative AI, with a demonstrated ability to build scalable prototypes from inception to solution.\nProven expertise in applying deep learning and NLP techniques to solve complex problems, with a portfolio of projects on real-world problems.\nSolid understanding of Large Language Models and practical experience with Generative AI Data processing skills such as SQL and BigQuery is a plus.\nStrong ability to translate real-world problems to data science problems.\nExcellent communication and collaboration skills, with the ability to collaborate effectively with both technical and non-technical teams.',
]
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, 0.9353, 0.8922],
#         [0.9353, 1.0000, 0.8577],
#         [0.8922, 0.8577, 1.0000]])

Evaluation

Metrics

Triplet

• Dataset: eval
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8338

Training Details

Training Dataset

Unnamed Dataset

• Size: 21,335 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
• num_train_epochs: 6
• 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: 6
• 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	167	-	0.7530
2.0	334	-	0.8176
2.9940	500	3.0582	-
3.0	501	-	0.8236
4.0	668	-	0.8338

Training Time

• Training: 5.1 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}
}