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benja-d/
paraphrase-spanish-distilroberta-finetuned-chatbot

Name: paraphrase-spanish-distilroberta-finetuned-chatbot
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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
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.

python

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]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.8667
cosine_accuracy@3	0.9
cosine_accuracy@5	0.9
cosine_accuracy@10	1.0
cosine_precision@1	0.8667
cosine_precision@3	0.8778
cosine_precision@5	0.86
cosine_precision@10	0.56
cosine_recall@1	0.1603
cosine_recall@3	0.4754
cosine_recall@5	0.7516
cosine_recall@10	0.9611
cosine_ndcg@10	0.9287
cosine_mrr@10	0.8976
cosine_map@100	0.9276

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.8667
cosine_accuracy@3	0.9
cosine_accuracy@5	0.9
cosine_accuracy@10	1.0
cosine_precision@1	0.8667
cosine_precision@3	0.8778
cosine_precision@5	0.86
cosine_precision@10	0.55
cosine_recall@1	0.1603
cosine_recall@3	0.4754
cosine_recall@5	0.7516
cosine_recall@10	0.9486
cosine_ndcg@10	0.9207
cosine_mrr@10	0.8962
cosine_map@100	0.9247

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.8667
cosine_accuracy@3	0.9
cosine_accuracy@5	0.9
cosine_accuracy@10	1.0
cosine_precision@1	0.8667
cosine_precision@3	0.8778
cosine_precision@5	0.86
cosine_precision@10	0.55
cosine_recall@1	0.1603
cosine_recall@3	0.4754
cosine_recall@5	0.7516
cosine_recall@10	0.9486
cosine_ndcg@10	0.9207
cosine_mrr@10	0.8962
cosine_map@100	0.9247

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.9
cosine_accuracy@3	0.9333
cosine_accuracy@5	0.9667
cosine_accuracy@10	1.0
cosine_precision@1	0.9
cosine_precision@3	0.9111
cosine_precision@5	0.9067
cosine_precision@10	0.5767
cosine_recall@1	0.1659
cosine_recall@3	0.4921
cosine_recall@5	0.7877
cosine_recall@10	0.9847
cosine_ndcg@10	0.9594
cosine_mrr@10	0.9298
cosine_map@100	0.9545

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.8667
cosine_accuracy@3	0.9
cosine_accuracy@5	0.9333
cosine_accuracy@10	1.0
cosine_precision@1	0.8667
cosine_precision@3	0.8778
cosine_precision@5	0.8733
cosine_precision@10	0.5667
cosine_recall@1	0.1603
cosine_recall@3	0.4754
cosine_recall@5	0.7627
cosine_recall@10	0.9722
cosine_ndcg@10	0.9376
cosine_mrr@10	0.9012
cosine_map@100	0.9327

Training Details

Training Dataset

json

Dataset: json
Size: 168 training samples
Columns: positive and anchor
Approximate statistics based on the first 168 samples:
positive anchor
type string string
details
min: 46 tokens
mean: 98.52 tokens
max: 128 tokens
min: 6 tokens
mean: 14.61 tokens
max: 32 tokens

Samples:

positive	anchor
¿Que precio o costo tiene la transferencia de un vehículo en Autofact?[object Object]Al transferir un vehículo con Autofact pagas los mismos costos que un proceso de transferencia habitual.[object Object]El arancel de la institución oficial estatal: $36.030 pesos.[object Object]El valor del servicio de transferencia Autofact es de $59.990 e incluye el certificado de anotaciones (CAV) del vehículo.[object Object]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).[object Object]En total, debes sumar los siguientes montos:[object Object]36.030 + 59.990 + 1,5% del valor mayor entre el precio del vehículo o la tasación del mismo. [object Object]Si eres el comprador, puedes agregar el servicio de TAG a domicilio a tu transferencia,...	`¿Cuál es el costo de transferir un vehículo a través de Autofact?`
¿Que documentos necesito para transferir de un vehículo en Autofact?[object Object]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:[object Object][object Object]Cédulas de identidad al día de comprador/es y vendedor/es.[object Object]Último permiso de circulación pagado. (no importa si esta atrasado)[object Object]Si una o ambas partes es extranjera, debes considerar lo siguiente:[object Object][object Object]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.[object Object]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).[object Object][object Object]Si el cliente es diplomático, puede comprar o vender sin problemas y debe firmar ...	`¿Que documentos necesito para transferir de un vehículo en Autofact?`
`¿Se puede transferir con poder notarial?[object Object]Autofact puede gestionar sin problemas contratos en los que una persona firme en representación del propietario que vende o del nuevo propietario que compra.[object Object]Ambos casos será requerido un poder notarial donde el comprador o vendedor otorgue la facultad a su representante de realizar el proceso. [object Object][object Object]`	`¿Cuál es el procedimiento para transferir un vehículo?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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

Click to expand

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: {}

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.4762	10	2.6476	-	-	-	-	-
0.9524	20	3.2114	-	-	-	-	-
1.0	21	-	0.6559	0.6317	0.5960	0.5775	0.5818
1.4286	30	0.9787	-	-	-	-	-
1.9048	40	0.8942	-	-	-	-	-
2.0	42	-	0.7643	0.7643	0.7643	0.7643	0.7207
2.3810	50	0.0919	-	-	-	-	-
2.8571	60	0.2104	-	-	-	-	-
3.0	63	-	0.8242	0.8242	0.7968	0.7760	0.7760
3.3333	70	0.0221	-	-	-	-	-
3.8095	80	0.4657	-	-	-	-	-
4.0	84	-	0.8641	0.8641	0.8641	0.8641	0.8367
4.2857	90	0.2159	-	-	-	-	-
4.7619	100	0.0667	-	-	-	-	-
5.0	105	-	0.8367	0.8367	0.8367	0.8367	0.8373
5.2381	110	0.0563	-	-	-	-	-
5.7143	120	0.0276	-	-	-	-	-
6.0	126	-	0.8492	0.8492	0.8492	0.8725	0.8396
6.1905	130	0.0221	-	-	-	-	-
6.6667	140	0.0311	-	-	-	-	-
7.0	147	-	0.8870	0.8999	0.8999	0.9496	0.9191
7.1429	150	0.0013	-	-	-	-	-
7.6190	160	0.0855	-	-	-	-	-
8.0	168	-	0.9079	0.8999	0.8999	0.9415	0.9191
8.0952	170	0.0191	-	-	-	-	-
8.5714	180	0.028	-	-	-	-	-
9.0	189	-	0.8870	0.8999	0.9207	0.9415	0.9376
9.0476	190	0.0186	-	-	-	-	-
9.5238	200	0.0006	-	-	-	-	-
10.0	210	0.0038	0.9079	0.9207	0.9207	0.9594	0.9376
10.4762	220	0.0386	-	-	-	-	-
10.9524	230	0.0034	-	-	-	-	-
11.0	231	-	0.9287	0.9207	0.9207	0.9594	0.9376
11.4286	240	0.0016	-	-	-	-	-
11.9048	250	0.0378	-	-	-	-	-
12.0	252	-	0.9287	0.9207	0.9207	0.9594	0.9376
12.3810	260	0.0558	-	-	-	-	-
12.8571	270	0.0022	-	-	-	-	-
13.0	273	-	0.9287	0.9207	0.9207	0.9594	0.9376

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

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",
}

MatryoshkaLoss

bibtex

@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

bibtex

@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}
}

	positive	anchor
type	string	string
details	min: 46 tokens mean: 98.52 tokens max: 128 tokens	min: 6 tokens mean: 14.61 tokens max: 32 tokens