---
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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### 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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100 training samples
* Columns: sentence1, sentence2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | - min: 3 tokens
- mean: 12.47 tokens
- max: 26 tokens
| - min: 3 tokens
- mean: 46.48 tokens
- max: 128 tokens
| - 0: ~39.00%
- 1: ~24.00%
- 2: ~37.00%
|
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------|:----------------------------------------------------|:---------------|
| ilustracion,diseño ux ui, sdiseño ux | The Ancient Greeks | 2 |
| Marketing digital, Bachiller | The Modern and the Postmodern (Part 1) | 2 |
| profesional | Writing | 1 |
* Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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