metadata

base_model: sentence-transformers/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: >-
      Reportando a Mánager ventasLograr un crecimiento sostenible de los
      ingresos mediante la negociación, cierre, implementación y cumplimiento de
      acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y
      apertura de nuevos clientes a nivel LATAM . Entender requerimientos y
      saber asesorar de la mejor manera para un buen cierre de negocio. Alto
      conocimiento en la línea de flotas y Camiones.
    sentences:
      - >-
        Modernize Infrastructure and Applications with Google Cloud.Data
        Science.Business Strategy.Understand the role that cloud modernization
        and migration plays in an organization's digital transformation..
        Examine available options to run compute workloads in the cloud..
        Explore the advantages of using containers, serverless computing, and
        APIs in application modernization.. Learn about the business reasons to
        choose hybrid or multi-cloud strategies, and how GKE Enterprise can help
        support these strategies.
      - >-
        Microsoft 365 Copilot for Leaders.Data Science.Machine Learning.Risk
        Management
      - >-
        Decoding AI: A Deep Dive into AI Models and Predictions.Data
        Science.Machine Learning.Learn key concepts and terminology in
        artificial intelligence (AI), including machine learning, generative AI,
        and deep learning . Learn the core components of machine learning
        systems, including data, models, and evaluation techniques. Recognize
        why AI systems can fail and identify the kinds of work required to make
        useful technology. Identify common pitfalls in conversations about AI
        and recognize conflicts of interest when interpreting claims about AI
        systems
  - source_sentence: >-
      Reportando a Mánager ventasLograr un crecimiento sostenible de los
      ingresos mediante la negociación, cierre, implementación y cumplimiento de
      acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y
      apertura de nuevos clientes a nivel LATAM . Entender requerimientos y
      saber asesorar de la mejor manera para un buen cierre de negocio. Alto
      conocimiento en la línea de flotas y Camiones.
    sentences:
      - >-
        Getting Started with BigQuery Machine Learning.Data Science.Cloud
        Computing.How to create, evaluate and use machine learning models in
        BigQuery. 
      - >-
        Convolutional Neural Networks.Data Science.Machine Learning.Artificial
        Neural Networks, Computer Vision, Machine Learning, Applied Machine
        Learning, Deep Learning, Machine Learning Software, Machine Learning
        Algorithms, Network Model, Tensorflow, Network Architecture, Human
        Learning
      - >-
        Understanding Plants - Part II: Fundamentals of Plant Biology.Data
        Science.Basic Science.Understanding Plants - Part II: Fundamentals of
        Plant Biology
  - source_sentence: >-
      Reportando a Mánager ventasLograr un crecimiento sostenible de los
      ingresos mediante la negociación, cierre, implementación y cumplimiento de
      acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y
      apertura de nuevos clientes a nivel LATAM . Entender requerimientos y
      saber asesorar de la mejor manera para un buen cierre de negocio. Alto
      conocimiento en la línea de flotas y Camiones.
    sentences:
      - >-
        Introduction to Computer Science and Programming.Data Science.Software
        Development.1. Use the Javascript language to create interactive
        programs in the browser with 2D graphics.. 2. Convert between number
        bases, work with modular arithmetic, sequences and series and plot
        graphs.. 3. Develop and use mental models to describe the workings of a
        range of computer systems.
      - >-
        Programming Languages, Part A.Data Science.Software Development.Computer
        Programming, Programming Principles, Algorithms, Critical Thinking
      - >-
        Global Health Innovations.Data Science.Public Health.Describe the
        principles and key types of innovation in order to characterise the
        fundamental features of new models of care and technologies. Compare and
        contrast systems that support the development, investment, and
        protection of healthcare innovation to navigate the innovation journey.
        Evaluate key factors influencing the adoption and scaling of different
        healthcare innovations, and examine the reasons why some innovations
        fail . Critique a particular innovation, using a given framework, in
        order to make a recommendation to a panel of decision makers. 
  - source_sentence: >-
      Reportando a Mánager ventasLograr un crecimiento sostenible de los
      ingresos mediante la negociación, cierre, implementación y cumplimiento de
      acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y
      apertura de nuevos clientes a nivel LATAM . Entender requerimientos y
      saber asesorar de la mejor manera para un buen cierre de negocio. Alto
      conocimiento en la línea de flotas y Camiones.
    sentences:
      - >-
        Development of Secure Embedded Systems.Data Science.Computer Security
        and Networks.Operating Systems, Systems Design, Computer Programming,
        System Software, Computer Architecture, Computer Networking, C
        Programming Language Family, Computer Programming Tools, Hardware
        Design, Networking Hardware, System Programming, Theoretical Computer
        Science, Algorithms
      - >-
        GST - Genesis and imposition!.Data Science.Finance.Explain the genesis
        of GST, the need for its introduction and the Constitutional and legal
        framework under which it was introduced. .  Identify and describe
        different forms of supplies of goods and services, deemed supplies and
        transactions excluded from the scope of supply..  Differentiate various
        types of supplies and identify whether a supply is inter-State or
        intra-State, exempt or composite supply..  Critically analyse whether a
        given transaction is a supply and define the nature of supply.
      - >-
        AI for Project Managers and Scrum Masters.Data Science.Business
        Essentials.Identify key elements of AI for Project Management . Evaluate
        AI Tools and Techniques for Projects . Integrate AI into Project
        Lifecycles 
  - source_sentence: >-
      Reportando a Mánager ventasLograr un crecimiento sostenible de los
      ingresos mediante la negociación, cierre, implementación y cumplimiento de
      acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y
      apertura de nuevos clientes a nivel LATAM . Entender requerimientos y
      saber asesorar de la mejor manera para un buen cierre de negocio. Alto
      conocimiento en la línea de flotas y Camiones.
    sentences:
      - >-
        Introduction to Data Science and scikit-learn in Python.Data
        Science.Data Analysis.Employ artificial intelligence techniques to test
        hypothesis in Python. Apply a machine learning model combining Numpy,
        Pandas, and Scikit-Learn
      - >-
        Planejamento de projetos: Como reunir tudo.Data Science.Leadership and
        Management.Descrever os componentes da fase de planejamento e a
        significância deles.. Identificar ferramentas e práticas recomendadas
        para criar um plano de projeto e um plano de gestão de riscos. .
        Descrever como estimar, acompanhar e manter um orçamento.. Elaborar um
        plano de comunicação e explicar como gerenciá-lo.
      - >-
        Microsoft 365 Copilot for Leaders.Data Science.Machine Learning.Risk
        Management

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/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: sentence-transformers/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/GURU-paraphrase-multilingual-MiniLM-L12-v2")
# Run inference
sentences = [
    'Reportando a Mánager ventasLograr un crecimiento sostenible de los ingresos mediante la negociación, cierre, implementación y cumplimiento de acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y apertura de nuevos clientes a nivel LATAM . Entender requerimientos y saber asesorar de la mejor manera para un buen cierre de negocio. Alto conocimiento en la línea de flotas y Camiones.',
    'Introduction to Data Science and scikit-learn in Python.Data Science.Data Analysis.Employ artificial intelligence techniques to test hypothesis in Python. Apply a machine learning model combining Numpy, Pandas, and Scikit-Learn',
    'Planejamento de projetos: Como reunir tudo.Data Science.Leadership and Management.Descrever os componentes da fase de planejamento e a significância deles.. Identificar ferramentas e práticas recomendadas para criar um plano de projeto e um plano de gestão de riscos. . Descrever como estimar, acompanhar e manter um orçamento.. Elaborar um plano de comunicação e explicar como gerenciá-lo.',
]
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: 85 tokens
mean: 85.0 tokens
max: 85 tokens

min: 13 tokens
mean: 65.22 tokens
max: 128 tokens

0: ~10.80%
1: ~13.20%
2: ~76.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 85 tokens mean: 85.0 tokens max: 85 tokens	min: 13 tokens mean: 65.22 tokens max: 128 tokens	0: ~10.80% 1: ~13.20% 2: ~76.00%

Samples:

sentence1	sentence2	label
`Reportando a Mánager ventasLograr un crecimiento sostenible de los ingresos mediante la negociación, cierre, implementación y cumplimiento de acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y apertura de nuevos clientes a nivel LATAM . Entender requerimientos y saber asesorar de la mejor manera para un buen cierre de negocio. Alto conocimiento en la línea de flotas y Camiones.`	`Launching Your Music Career.Data Science.Music and Art.Articulate your Unique Selling Proposition.. Use the Business Model Canvas to determine the core functions required to effectively manage your portfolio career.. Complete a comprehensive growth and recruitment plan for your teaching studio and identify the competitive landscape.. Seek out and book performance opportunities in a variety of settings.`	`2`
`Reportando a Mánager ventasLograr un crecimiento sostenible de los ingresos mediante la negociación, cierre, implementación y cumplimiento de acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y apertura de nuevos clientes a nivel LATAM . Entender requerimientos y saber asesorar de la mejor manera para un buen cierre de negocio. Alto conocimiento en la línea de flotas y Camiones.`	`Robotics.Data Science.Electrical Engineering.Motion Planning. Matlab. Estimation`	`2`
`Reportando a Mánager ventasLograr un crecimiento sostenible de los ingresos mediante la negociación, cierre, implementación y cumplimiento de acuerdos con los diferentes clientes.Encargado de realizar la búsqueda y apertura de nuevos clientes a nivel LATAM . Entender requerimientos y saber asesorar de la mejor manera para un buen cierre de negocio. Alto conocimiento en la línea de flotas y Camiones.`	`Core Java.Data Science.Software Development.Learn the basic syntax and functions of the Java programming language. Apply object-oriented programming techniques to building classes, creating objects, and understanding how solutions are packaged in Java.. Learn how to implement inheritance and polymorphism in Java.. Use selected parts of the vast Java SE class library to enhance your Java programming techniques.`	`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",
}