shahamitkumar/resume-bge-small

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

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. 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: BAAI/bge-small-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

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': True, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("shahamitkumar/resume-bge-small")
# Run inference
sentences = [
    'exit interviews AND employee feedback',
    'e l p S M E s i n t h e i r g l o b a l m a r k e t r e s e a r c h ● C u l t i v a t e d s t r o n g c l i e n t r e l a t i o n s h i p s a n d a c h i e v e d a 1 0 0 % j o b s u c c e s s s c o r e ● S h a r p e n e d a b i l i t y t o c o m m u n i c a t e e \x00 e c t i v e l y w i t h g l o b a l c l i e n t s a n d s t a k e h o l d e r s H u m a n R e s o u r c e s I n t e r n | P I A ( P a k i s t a n I n t e r n a t i o n a l A i r l i n e s ) | J u n e 2 0 2 1 - J u l y 2 0 2 1 ● C o n t r i b u t e d t o a s i g n i fi c a n t r e d u c t i o n i n w o r k f o r c e s i z e , w i t h n e a r l y 2 0 0 0 e m p l o y e e s o p t i n g f o r t h e V S S , l e a d i n g t o e n h a n c e d o p e r a t i o n a l a g i l i t y a n d c o s t s a v i n g s f o r t h e o r g a n i z a t i o n ● A s s i s t e d i n c o n d u c t i n g e x i t i n t e r v i e w s f o r d e p a r t i n g e m p l o y e e s , g a t h e r i n g v a l u a b l e f e e d b a c k t o i d e n t i f y a r e a s f o r i m p r o v e m e n',
    'Settlement Process SKILLS • Investment decisioning • Funds Flow process • Prepaid card processing knowledge (VISA, MasterCard, Discover & FIS) • ACH Transaction processing system • ACH Migration Project Lead • Understanding of Debit & Prepaid card settlement Process • Funds flow process between Program Manager, Issuing Financial Ins. & Processor and the role of ODFI & RDFI. • Team Player and Proficient in MS E xcel • Fluent in [ English, Urdu & Punjabi ]',
]
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, 0.2549, 0.2693],
#         [0.2549, 1.0000, 0.1984],
#         [0.2693, 0.1984, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,865 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 5 tokens mean: 10.29 tokens max: 21 tokens	min: 25 tokens mean: 330.39 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1
  operations management AND marketing research	be a great lead in customer service and effecting it with your behavior”. Advanced Operations Management: Analyzed supply chain system of Packages limited w.r.t dynamics. Marketing Research: Researched and presented solutions for the problem of low sales volume for “DeSOM” ADDITIONAL EXPERIENCE General Secretary , Business Management Club 2014 – 2015 • Conducted and promoted several university trips to Northern areas with my team of 12 members. Team Leader, Polo Club 2015 - 2016 • I was the team leader university polo club team. • Going to be a certified Customer Re lation M anager By BMW. HONORS, AWARDS & TRAININGS • 3rd Position representing LGU at LUMS supply chain summit. • 1st position in university for finishing the fairy meadow trek. • 1st position in university dramatic competition • Hands on experience on SAP for 2.5 years SKILLS & INTERESTS • Competent user of Microsoft Office, SAP & CRM systems. • Vast interest in travelling, hiking and keeping pet. Pets make your surroundin...
  Microsoft CRM AND sales strategy	internet lead phone calls Selling a minimum number of products or bringing in a minimum of customers from the Internet, based on goals and objectives defined. INTECH Process Automation Microsoft CRM Administator July 2014 - December 2014 (6 months) Lahore, Pakistan Education COMSATS Institute of Information and Technology Master of Business Administration - MBA, Business Administration and Management, General · (2018 - 2019) COMSATS Institute of Information and Technology Bachelor's degree, Business/Managerial Economics · (2012 - 2016) Page 3 of 3
  who has experience with SAP ERP and Oracle?	SHAHRUKH ALI shahrukh.ali@descon.com 4618452 linkedin.com/in/shahrukh-ali-3021ab24a Summary A highly motivated and result-driven Business Development Executive who also has served as a Finance & Accounts Professional with 12 years of invaluable experience including being skilled in numerous business development, financial & accounting fields. I have the ability to handle complex assignments effectively & possessing the confidence to work as part of a team or independently. An established reputation for self‐ motivation, attention to detail excellent organizational skills, and high ethical conduct. Presently looking for the best opportunity with a goal-oriented company where I can excel, deliver and achieve according to my potential. I am enthusiastic to learn keenly about emerging developments in my job sector. Experience Senior Associate KPMG In Pakistan Aug 2021 - Feb 2022 (7 months) Team Supervision and Training Data entry analyst: Update the system as invoices are received. Bank Pa...

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "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: 32
• per_device_eval_batch_size: 32
• 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: 32
• per_device_eval_batch_size: 32
• 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: {}

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.3.0
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• 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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}