paraphrase-spanish-distilroberta-finetuned-chatbot

ModernBERT Embed base Legal Matryoshka

This is a sentence-transformers model finetuned from somosnlp-hackathon-2022/paraphrase-spanish-distilroberta on the json dataset. It maps sentences & paragraphs to a 768-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: somosnlp-hackathon-2022/paraphrase-spanish-distilroberta <!-- at revision 5ed9fdaabd705e7bd88029a3f08ce7397a666d6a -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: es
• License: apache-2.0

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, 'architecture': 'RobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("benja-d/paraphrase-spanish-distilroberta-finetuned-chatbot")
# Run inference
sentences = [
    ' ¿Qué es la transferencia Autofact?\nEs un servicio 100% online que permite el traspaso de dominio de un vehículo usado de forma digital, sin necesidad de acudir a oficinas ni gestionar documentos adicionales.\nTiene la misma validez legal que el Notario o ir al Registro Civil.\nPuedes transferir un vehículo las 24 hrs del día, los 7 días de la semana.\n\n\n',
    '¿Qué información se necesita para realizar una transferencia Autofact?',
    '¿Existen restricciones o requisitos especiales para transferir un auto heredado?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7707, 0.0574],
#         [0.7707, 1.0001, 0.0791],
#         [0.0574, 0.0791, 1.0000]])

<!-- ### 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.* -->

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 768
  }
  

Information Retrieval

• Dataset: dim_512
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 512
  }
  

Information Retrieval

• Dataset: dim_256
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 256
  }
  

Information Retrieval

• Dataset: dim_128
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 128
  }
  

Information Retrieval

• Dataset: dim_64
• Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 64
  }
  

<!-- ## 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

json

• Dataset: json
• Size: 168 training samples
• Columns: <code>positive</code> and <code>anchor</code>
• Approximate statistics based on the first 168 samples:

  	positive	anchor
  type	string	string
  details	<ul><li>min: 46 tokens</li><li>mean: 98.52 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.61 tokens</li><li>max: 32 tokens</li></ul>

• Samples:

  positive	anchor
  <code> ¿Que precio o costo tiene la transferencia de un vehículo en Autofact?<br>Al transferir un vehículo con Autofact pagas los mismos costos que un proceso de transferencia habitual.<br>El arancel de la institución oficial estatal: $36.030 pesos.<br>El valor del servicio de transferencia Autofact es de $59.990 e incluye el certificado de anotaciones (CAV) del vehículo.<br>El impuesto a la transferencia: 1,5% del valor de compra del vehículo o 1,5% de la tasación fiscal del vehículo (se cobra el mayor valor). Por ejemplo, si tu auto tiene una tasación fiscal de $5.000.000 y se vende a $6.000.000, tendrás que pagar $90.000 de impuestos (1,5% del precio de venta). En caso que el precio de venta fuese $4.000.000, tendrás que pagar $75.000 de impuestos (1,5% de la tasación fiscal).<br>En total, debes sumar los siguientes montos:<br>36.030 + 59.990 + 1,5% del valor mayor entre el precio del vehículo o la tasación del mismo. <br>Si eres el comprador, puedes agregar el servicio de TAG a domicilio a tu transferencia,...</code>	<code>¿Cuál es el costo de transferir un vehículo a través de Autofact?</code>
  <code> ¿Que documentos necesito para transferir de un vehículo en Autofact?<br>Al hacer el cambio de propietario de un auto o moto, necesitas algunos documentos para poder llevar a cabo el trámite de la transferencia de dominio vehicular. En el caso de Autofact, se requieren los siguientes:<br><br>Cédulas de identidad al día de comprador/es y vendedor/es.<br>Último permiso de circulación pagado. (no importa si esta atrasado)<br>Si una o ambas partes es extranjera, debes considerar lo siguiente:<br><br>La cédula de identidad debe estar vigente. Si está vencida, se requiere haber ingresado a trámite una solicitud de cambio o prórroga de visación de residente o permanencia definitiva ante el departamento de Extranjería y Migración del Ministerio del Interior y Seguridad Pública.<br>No es posible firmar con pasaporte. Si no se tiene carnet de identidad, es necesario poseer un RUT de inversionista del Servicio de Impuestos Internos (SII).<br><br>Si el cliente es diplomático, puede comprar o vender sin problemas y debe firmar ...</code>	<code> ¿Que documentos necesito para transferir de un vehículo en Autofact?</code>
  <code> ¿Se puede transferir con poder notarial?<br>Autofact puede gestionar sin problemas contratos en los que una persona firme en representación del propietario que vende o del nuevo propietario que compra.<br>Ambos casos será requerido un poder notarial donde el comprador o vendedor otorgue la facultad a su representante de realizar el proceso. <br><br></code>	<code>¿Cuál es el procedimiento para transferir un vehículo?</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 2
• gradient_accumulation_steps: 2
• learning_rate: 2e-05
• num_train_epochs: 13
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 2
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-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: 13
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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: True
• 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_fused
• 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: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• 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
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.0.0
• Transformers: 4.53.2
• PyTorch: 2.7.1+cu126
• Accelerate: 1.9.0
• Datasets: 4.0.0
• Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

@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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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