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---
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](https://www.SBERT.net) model finetuned from [saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/saraleivam/GURU-paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 0f16d34e08fc583b71c922dc18d3b14eba17983c -->
- **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/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]
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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: 77 tokens</li><li>mean: 77.0 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 64.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~17.00%</li><li>1: ~25.00%</li><li>2: ~58.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>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.</code> | <code>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</code> | <code>0</code> |
| <code>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.</code> | <code>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.</code> | <code>1</code> |
| <code>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.</code> | <code>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. </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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