AI_captone1

SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v1

This is a sentence-transformers model finetuned from sentence-transformers/distiluse-base-multilingual-cased-v1. It maps sentences & paragraphs to a 512-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/distiluse-base-multilingual-cased-v1 <!-- at revision 30487a83b196a60bdf70abff0505ec70f112213c -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 512 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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 = [
    'EDUCATION\nBA in Management Information Systems\nDuy Tan University (2021 - Expected completion 05/2025)\nGPA: 3.6/4.0\nTECHNICAL SKILLS\nFrontend:\n\nLanguages ??& Frameworks: HTML, CSS, JavaScript, TypeScript\nLibraries & Tools: TailwindCSS, React.js, Next.js\nBackend:\n\nMain Framework: NestJS, ExpressJS\nDatabase & ORM: PostgreSQL, MongoDB, TypeOrm, Mongoose\nCloud Services: AWS, Elasticsearch\n\nMessaging & Streaming: KafkaJS, WebSocket\n\nContainer & Deployment: Docker\n\nOther Tools:\n\nVersion Control: Git, GitHub\nCI/CD: Vercel\nPERSONAL PROJECTS\nNestgres\nGitHub Repository: Learning Project with NestJS and PostgreSQL. Integrate AWS S3 for file storage, Docker for containerization, and Elasticsearch for advanced search. This project helped me gain a deep understanding of building a scalable backend system and handling big data.\n\nSimple Todo\nLive Demo: A simple to-do list application to reinforce my knowledge of React, including component architecture, state management, and efficient rendering.\n\nNestactube\nGitHub Repository: A full-stack video platform combining NestJS and React, serving video streaming from backend to frontend. Focusing on handling large media files and ensuring smooth video playback.\n\nMindForge\nGitHub Repository - Live Demo: Clone of Notion with note creation and editing features. Using Convex for secure data storage and Clerk for user authentication. The project helped me develop a friendly interface and handle complex data structures.\n\nAWARDS & ACHIEVEMENTS\nExcellent Academic Performance - 2022, 2023\nBoeing Scholarship - 2022, 2023\nThird Prize - Duy Tan Informatics Competition, 2023\nThird Prize - Informatics Olympiad (non-specialist group), 2023\nCERTIFICATIONS\nFoundations of User Experience (UX) Design',
    "Skills and Qualifications Required:\n\nQualifications:\n\nBachelor's degree in Computer Science, Computer Networking or related fields from Universities such as University of Science, University of Natural Sciences, University of Information Technology, Vietnam National University, Ho Chi Minh City, or Vietnam National University, Hanoi.\nOnly candidates with academic background and practical experience directly related to Information Technology will be considered (candidates from short-term or non-major programs will not be accepted).\nGrade Point Average (GPA):\n\nMinimum GPA: 7.0 (on a scale of 10) or 2.8 (on a scale of 4)\n\nTechnical Skills:\n\nStrong programming skills with C/C++, along with knowledge of object-oriented programming.\nHave practical experience (1 year or more) working with C/C++.\nBasic knowledge of operating systems such as Windows, Linux, and MacOS.\nUnderstanding of network protocols and security principles. Strong team working skills and problem solving ability.",
    "Qualifications:\n\nExpected Skills:\n\nProgramming Languages: Proficiency in C# and experience working with Unity3D.\nGame Development: Solid understanding of game mechanics, UI/UX, and physics.\nCybersecurity Tools: Experience with cyber security tools with a defensive/offensive mindset.\nPerformance Optimization: Solid skills in game optimization and memory management.\nVersion Management Knowledge: Familiarity with Git or similar version control systems.\nPreferred Skills:\n\nAR/VR development experience is a plus.\nKnowledge of multi-player and networking concepts, along with creativity.\nEducation:\n\nBachelor's degree in Computer Science, Game Development, or related field, or equivalent hands-on experience.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 5,108 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 44 tokens</li><li>mean: 106.09 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 46 tokens</li><li>mean: 117.55 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 0.5</li><li>mean: 0.66</li><li>max: 0.74</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>EDUCATION<br>Software Engineer (CMU)<br>Duy Tan University, 09/2021 - Present<br>GPA: 3.6/4.0<br>OUTSTANDING PROJECTS<br>Fashion Store Website<br>Time: 17/01 - 25/02<br>Role: Backend Developer<br><br>Features:<br><br>Register, log in, log out (customer rights)<br>Log in and manage with admin rights<br>Add, edit, delete products and vouchers (admin)<br>Display product and voucher list<br>Technology:<br><br>Frontend: Customer page & admin dashboard (edit from available template)<br>Backend: Typescript (NestJS)<br>Database: MongoDB (Mongoose)<br>GitHub Link: Fashion Store Project<br><br>SKILLS<br>Front-end: HTML, CSS, JavaScript, Bootstrap 5, ReactJS<br>Back-end: ExpressJS, NestJS (TypeScript)<br>Database: MySQL, MongoDB<br>OTHER TOOLS & TECHNOLOGIES<br>Tools: Git (GitHub), Docker, Postman<br>Other languages: C# (ASP.NET), Java (Spring Boot)<br>LANGUAGES<br>English: Good communication and reading skills</code>	<code>Professional Qualifications:<br>Experience: Minimum 2 years of experience working with Java, proficient in Spring Boot.<br>Frontend Framework: Proficient in using one of the Frontend frameworks (such as Vue, React,...) to develop interactive user interfaces.<br>Database: Experience working with relational databases such as MS SQL Server, Oracle, PostgreSQL, MongoDB.<br>Microservice Architecture: Experience with microservice architecture and containerization technologies such as Kubernetes/Docker.<br>Multitasking: Ability to handle multitasking, multithreading, multiprocessing, and mechanisms such as hash tables, file processing mechanisms.<br>API Integration: Experience in integrating/developing RESTful, SOAP, TCP/IP APIs.<br>Operating System: Experience working with Enterprise Linux and Windows Server operating systems.</code>	<code>0.65</code>
  <code>EDUCATION HISTORY University of Roseton Master of Science in Software Engineering	Graduated: 2020 Best Thesis Awardee Berou Solutions Scholarship Recipient De Loureigh University Bachelor of Science in Computer Science	Graduated: 2016 (Cum Laude) Founder of DLU Programming Club Hackathon Champion Beechtown 2015 RELEVANT SKILLS Programming Languages: JavaScript, C/C++, Java, Python, Kotlin, Go Core Skills: Problem Solving, Team Communication</code>
  <code>EDUCATION Bachelor of Science in Computer Science Rutgers University ? New Brunswick, NJ 2008 - 2012 SKILLS HTML CSS JavaScript React jQuery Angular.js Vue.js Enzyme Jest Git</code>	<code>ABBANK<br><br>Skill requirements:<br><br>Minimum 5 years of experience as a BA in CLIMS system.<br>Experience in lending activities.<br>Experience in developing test scenarios and test plans for software projects.<br>Understanding of software development processes.<br>Ensure compliance with application development standards and quality processes.<br>Ability to analyze and resolve complex technical issues, provide feasible and timely solutions.<br>Effective communication and teamwork skills when collaborating with different functional groups.<br>Focus on Agile implementation and willingness to learn and grow from experience.<br>Education requirements:<br><br>University degree in IT, banking, finance or related fields.<br>Language requirements:<br><br>Being able to communicate in English is an advantage.</code>	<code>0.62</code>

• Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• 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: 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: {}
• warmup_ratio: 0.0
• 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
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: 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
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.1
• Transformers: 4.44.2
• PyTorch: 2.5.0+cu121
• Accelerate: 0.34.2
• Datasets: 3.1.0
• Tokenizers: 0.19.1

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",
}

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