README.md

---
base_model: saraleivam/GURU2-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:100
- loss:SoftmaxLoss
widget:
- source_sentence: ilustracion,diseño ux uidiseño ux
  sentences:
  - Create AI-generated art using NightCafe. Apply styles and techniques to customize
    artwork  .  Produce a digital art portfolio showcasing AI creativity
  - The nature of discrete-time signals. Discrete-time signals are vectors in a vector
    space. Discrete-time signals can be analyzed in the frequency domain via the Fourier
    transform
  - Describe software engineering, Software Development Lifecycle (SDLC), and software
    development tools, technologies and stacks.  . List different types of programming
    languages and create basic programming constructs such as loops and conditions
    using Python.   . Outline approaches to application architecture and design, patterns,
    and deployment architectures.   . Summarize the skills required in software engineering
    and describe the career options it provides.
- source_sentence: profesional
  sentences:
  - Create a  Facebook Prophet Machine learning model & Forecast the price of Bitcoin
    for the future 30 days. Learn to Visualize Bitcoin using Plotly Express. Learn
    to Extract Financial Data and Analyze it using Google Sheets
  - What Industry 4.0 is and what factors have enabled the IIoT.. Key skills to develop
    to be employed in the IIoT space.. What platforms are, and also market information
    on Software and Services.. What the top application areas are (examples include
    manufacturing and oil & gas).
  - Writing
- source_sentence: ilustracion,diseño ux uidiseño ux
  sentences:
  - Creativity, Problem Solving, Writing
  - Anatomy of the Upper and Lower Extremities
  - Evaluate the performance of a classifier using visual diagnostic tools from Yellowbrick.
    Diagnose and handle class imbalance problems
- source_sentence: Maestría en educación
  sentences:
  - Explain the seminal ideas leading to the birth of AI, the major difficulties and
    how the international community overtook them.. Describe what AI is today in terms
    of goals, scientific community, companies’ interests. Describe the taxonomy of
    the know-how on AI in terms of techniques, software and hardware methodologies.
    . Explain the need for national strategies on AI and identify the major Italian
    and European players on AI
  - How a hardware component can be adapted at runtime to better respond to users/environment
    needs using FPGAs
  - A framework to evaluate DeFi risk; Environmental implications of cryptocurrency;
    and winners and losers in the future of finance.
- source_sentence: ilustracion,diseño ux uidiseño ux
  sentences:
  - Planning
  - 1.     Transform numbers between number bases and perform arithmetic in number
    bases . 2. Identify, describe and compute sequences of numbers and their sums.
    . 3. Represent and describe space numerically using coordinates and graphs.. 4.
    Study, represent and describe variations of quantities via functions and their
    graphs.
  - Create PivotTables to assess specific relationships within the data.. Create line,
    bar, and pie charts to present the information from the PivotTables.. Compose
    a dashboard with the charts and tables created to present a global picture of
    the data.
---

# SentenceTransformer based on saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/saraleivam/GURU2-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/GURU2-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision a73bbb48c69aae3d4ddfec208a2b666c7f5978c8 -->
- **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-train-paraphrase-multilingual-MiniLM-L12-v2")
# Run inference
sentences = [
    'ilustracion,diseño ux uidiseño ux',
    'Create PivotTables to assess specific relationships within the data.. Create line, bar, and pie charts to present the information from the PivotTables.. Compose a dashboard with the charts and tables created to present a global picture of the data.',
    '1.     Transform numbers between number bases and perform arithmetic in number bases . 2. Identify, describe and compute sequences of numbers and their sums. . 3. Represent and describe space numerically using coordinates and graphs.. 4. Study, represent and describe variations of quantities via functions and their graphs.',
]
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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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 100 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: 3 tokens</li><li>mean: 12.47 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 46.48 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~39.00%</li><li>1: ~24.00%</li><li>2: ~37.00%</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                           | label          |
  |:--------------------------------------------------|:----------------------------------------------------|:---------------|
  | <code>ilustracion,diseño ux ui, sdiseño ux</code> | <code>The Ancient Greeks</code>                     | <code>2</code> |
  | <code>Marketing digital, Bachiller</code>         | <code>The Modern and the Postmodern (Part 1)</code> | <code>2</code> |
  | <code>profesional</code>                          | <code>Writing</code>                                | <code>1</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: 2.3.0+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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