Upload sentence-transformers model

517ae59 verified 5 months ago

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:29248
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
  - source_sentence: Cómo prevenir hackeos en redes sociales
    sentences:
      - Me preocupa que UTE me cobre de más, estafas posibles?
      - ¿Cuán seguro es almacenarlo todo en la nube?
      - Cómo prevenir hackeos en redes sociales
  - source_sentence: ¿Qué es un ataque de spoofing?
    sentences:
      - Me hackearon Instagram, qué hago ahora
      - ¿Qué tanto extracto de datos hace una app común?
      - ¿Qué es un ataque de spoofing?
  - source_sentence: ¿Qué cuidados debo tener al realizar compras online?
    sentences:
      - ¿Qué cuidados debo tener al realizar compras online?
      - ¿Qué beneficios ofrece el yoga para mejorar la flexibilidad?
      - ¿Cómo proteger mis datos personales en el DGI?
  - source_sentence: >-
      - ¿En qué sectores es fundamental el comercio exterior en la economía
      uruguaya?
    sentences:
      - ¿Qué tanto afecta el malware a las empresas?
      - >-
        - ¿En qué sectores es fundamental el comercio exterior en la economía
        uruguaya?
      - '- ¿Qué cautiva a los turistas del Parque Nacional Santa Teresa?'
  - source_sentence: ¿Qué implicancias tiene un ataque DDoS en una empresa uruguaya?
    sentences:
      - '- ¿Cuáles son los secretos para un flan con dulce de leche perfecto?'
      - ¿Uso un segundo factor de autenticación?
      - ¿Qué implicancias tiene un ataque DDoS en una empresa uruguaya?
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 'XLMRobertaModel'})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    '¿Qué implicancias tiene un ataque DDoS en una empresa uruguaya?',
    '¿Qué implicancias tiene un ataque DDoS en una empresa uruguaya?',
    '¿Uso un segundo factor de autenticación?',
]
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, 1.0000, 0.9875],
#         [1.0000, 1.0000, 0.9875],
#         [0.9875, 0.9875, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 29,248 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 16.39 tokens
max: 128 tokens

min: 3 tokens
mean: 16.39 tokens
max: 128 tokens

min: 0.0
mean: 0.67
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 16.39 tokens max: 128 tokens	min: 3 tokens mean: 16.39 tokens max: 128 tokens	min: 0.0 mean: 0.67 max: 1.0

Samples:

sentence_0	sentence_1	label
`Cuántos routers tienen hoy en casa?`	`Cuántos routers tienen hoy en casa?`	`1.0`
`¿Cómo verificar si un archivo es seguro antes de abrirlo?`	`¿Cómo verificar si un archivo es seguro antes de abrirlo?`	`1.0`
`Me apareció un banner de la UTE en un sitio web, ¿es legítimo?`	`Me apareció un banner de la UTE en un sitio web, ¿es legítimo?`	`1.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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
num_train_epochs: 4
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
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}
parallelism_config: 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
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.2735	500	0.2642
0.5470	1000	0.077
0.8206	1500	0.0135
1.0941	2000	0.0103
1.3676	2500	0.009
1.6411	3000	0.0091
1.9147	3500	0.0084
2.1882	4000	0.0058
2.4617	4500	0.0046
2.7352	5000	0.0051
3.0088	5500	0.0054
3.2823	6000	0.0037
3.5558	6500	0.003
3.8293	7000	0.0024

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu126
Accelerate: 1.11.0
Datasets: 4.0.0
Tokenizers: 0.22.1

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",
}