|
--- |
|
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. |
|
|
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## 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 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **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}) |
|
) |
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``` |
|
|
|
## 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) |
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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><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 500 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | label | |
|
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
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| 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 |
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- `per_device_train_batch_size`: 8 |
|
- `per_device_eval_batch_size`: 8 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `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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