metadata

base_model: saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:500
  - loss:SoftmaxLoss
widget:
  - source_sentence: >-
      Servicio consultor SAP MM con experiencia Data Maestra SemiSenior,
      actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de
      SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a
      implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30%
      en master data.
    sentences:
      - >-
        Data mining of Clinical Databases - CDSS 1.Data Science.Machine
        Learning.Understand the Schema of publicly available EHR databases
        (MIMIC-III). Recognise the International Classification of Diseases
        (ICD) use. Extract and visualise descriptive statistics from clinical
        databases. Understand and extract key clinical outcomes such as
        mortality and stay of length
      - >-
        Natural Language Processing on Google Cloud.Data Science.Machine
        Learning.Machine Learning, Natural Language Processing, Tensorflow
      - >-
        Auditing I: Conceptual Foundations of Auditing.Business.Business
        Essentials.Accounting, Audit, Critical Thinking, Financial Analysis,
        Regulations and Compliance, Risk Management, Financial Accounting,
        General Accounting, Leadership and Management, Finance
  - source_sentence: >-
      Servicio consultor SAP MM con experiencia Data Maestra SemiSenior,
      actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de
      SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a
      implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30%
      en master data.
    sentences:
      - >-
        Generando modelos con Auto Machine Learning.Data Science.Machine
        Learning.Desarrollar modelos utilizando herramientas de Auto Machine
        Learning. Explorar los datos y hacer el tratamiento para su uso al
        generar modelos
      - >-
        Professionalism in Allied Health.Personal Development.Personal
        Development.Gain an understanding of the expectations of an allied
        healthcare professional in the workplace. Develop and exercise emotional
        intelligence, self-management, and interpersonal skills. Build and
        improve internal and external communication skills with all exchanges.
        Enhance the patient care experience with successful interactions and
        patient satisfaction
      - >-
        Big Data, Genes, and Medicine.Health.Health Informatics.Big Data,
        Bioinformatics, Data Analysis, Data Analysis Software, Statistical
        Programming, Algorithms, Exploratory Data Analysis, Computer Programming
  - source_sentence: >-
      Servicio consultor SAP MM con experiencia Data Maestra SemiSenior,
      actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de
      SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a
      implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30%
      en master data.
    sentences:
      - >-
        Retail Marketing Strategy.Business.Marketing.Brand Management,
        Leadership and Management, Marketing, Sales, Strategy, Strategy and
        Operations, Retail Sales, Retail Store Operations, Data Analysis,
        E-Commerce
      - >-
        Supporting Veteran Success in Higher Education.Personal
        Development.Personal Development.Supporting Veteran Success in Higher
        Education
      - >-
        Advanced AI Techniques for the Supply Chain.Data Science.Machine
        Learning.Machine Learning, Natural Language Processing
  - source_sentence: >-
      Servicio consultor SAP MM con experiencia Data Maestra SemiSenior,
      actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de
      SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a
      implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30%
      en master data.
    sentences:
      - >-
        Fundamentals of Flight mechanics.Physical Science and
        Engineering.Physics and Astronomy.How Mach number can impact stall
        speed.. Why turboprops consume less than turbojets.. What exactly mean
        indications given by flight instruments (i.e. anemometer, altimeter).
      - >-
        Learn English: Beginning Grammar.Language Learning.Learning
        English.Writing, Communication
      - >-
        Product Management Certification.Business.Leadership and
        Management.Apply key product management skills, tools, and techniques to
        engage and manage key stakeholders and clients. Identify product
        strategy development and implementation methods and best practices to
        ensure the right product is produced. Describe product development and
        analysis best practices to effectively manage change and ensure a
        successful product launch. Test what you have learned in a series of
        practical exercises allowing you to demonstrate real-word product
        management
  - source_sentence: >-
      Servicio consultor SAP MM con experiencia Data Maestra SemiSenior,
      actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de
      SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a
      implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30%
      en master data.
    sentences:
      - >-
        Python, Bash and SQL Essentials for Data Engineering.Computer
        Science.Software Development.Develop data engineering solutions with a
        minimal and essential subset of the Python language and the Linux
        environment. Design scripts to connect and query a SQL database using
        Python. Use a scraping library in Python to read, identify and extract
        data from websites 
      - >-
        AI-Enhanced Content Creation:Elevate Copywriting with Humata.Data
        Science.Machine Learning.Use prompts in Humata AI to get the information
        needed to generate an ad copy from the source files.  . Create engaging
        ads and blog posts tailored to your audience with the help of Humata AI
        prompts.  . Create a compelling advertisement for various online
        platforms using prompt engineering in Humata AI.    
      - >-
        SQL for Data Science Capstone Project.Data Science.Data Analysis.Develop
        a project proposal and select your data. Perform descriptive statistics
        as part of your exploratory analysis. Develop metrics and perform
        advanced techniques in SQL. Present your findings and make
        recommendations

SentenceTransformer based on saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2. 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: saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 tokens
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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
)

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("saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2")
# Run inference
sentences = [
    'Servicio consultor SAP MM con experiencia Data Maestra SemiSenior, actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30% en master data.',
    'Python, Bash and SQL Essentials for Data Engineering.Computer Science.Software Development.Develop data engineering solutions with a minimal and essential subset of the Python language and the Linux environment. Design scripts to connect and query a SQL database using Python. Use a scraping library in Python to read, identify and extract data from websites ',
    'AI-Enhanced Content Creation:Elevate Copywriting with Humata.Data Science.Machine Learning.Use prompts in Humata AI to get the information needed to generate an ad copy from the source files.  . Create engaging ads and blog posts tailored to your audience with the help of Humata AI prompts.  . Create a compelling advertisement for various online platforms using prompt engineering in Humata AI.    ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 500 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 77 tokens
mean: 77.0 tokens
max: 77 tokens

min: 14 tokens
mean: 64.05 tokens
max: 128 tokens

0: ~17.00%
1: ~25.00%
2: ~58.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 77 tokens mean: 77.0 tokens max: 77 tokens	min: 14 tokens mean: 64.05 tokens max: 128 tokens	0: ~17.00% 1: ~25.00% 2: ~58.00%

Samples:

sentence1	sentence2	label
`Servicio consultor SAP MM con experiencia Data Maestra SemiSenior, actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30% en master data.`	`Introduction to Generative AI - 한국어.Information Technology.Cloud Computing.생성형 AI 정의. 생성형 AI의 작동 방식 설명. 생성형 AI 모델 유형 설명. 생성형 AI 애플리케이션 설명`	`0`
`Servicio consultor SAP MM con experiencia Data Maestra SemiSenior, actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30% en master data.`	`Mastering Excel Essentials to Enhance Business Value.Business.Business Essentials.Effectively input data and efficiently navigate large spreadsheets.. Employ various "hacks" and expertly apply (the most appropriate) built-in functions in Excel to increase productivity and streamline workflow.. Apply the "what-if" analysis tools in Excel to conduct break-even analysis, conduct sensitivity analysis and support decision-making.`	`1`
`Servicio consultor SAP MM con experiencia Data Maestra SemiSenior, actualizaciones, referencias, ingles B2, remoto. Que maneje plantillas de SAP Bridge e Ibérico. Con experiencia en ServiceNow. No llegan ni a implementar, ni a ejecutar ni a hacer roll out. Llega a enfocarse en 30% en master data.`	`Exploring Piano Literature: The Piano Sonata.Arts and Humanities.Music and Art.Identify specific historical time periods in which the popularity of sonatas increases or decreases and the reasons behind these trends. . Identify sonata form. Recognize the most influential pieces in the sonata repertoire.`	`2`

Loss: SoftmaxLoss

Training Hyperparameters

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1.0
num_train_epochs: 3.0
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.1+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}