sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:500 loss:SoftmaxLoss arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 500 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                           | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | int                                                                |
  | details | <ul><li>min: 85 tokens</li><li>mean: 85.0 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 65.22 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~10.80%</li><li>1: ~13.20%</li><li>2: ~76.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                       | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                  | label          |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>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.</code> | <code>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.</code>         | <code>2</code> |
  | <code>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.</code> | <code>Robotics.Data Science.Electrical Engineering.Motion Planning. Matlab. Estimation</code>                                                                                                                                                                                                                                                                                                                                              | <code>2</code> |
  | <code>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.</code> | <code>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.</code> | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Training Hyperparameters

#### 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`: 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

</details>

### 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
```bibtex
@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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