GURU-paraphrase-multilingual-MiniLM-L12-v2

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from 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 <!-- 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
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

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>

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

@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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