This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1. 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: nomic-ai/nomic-embed-text-v1
Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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})
  (2): Normalize()
)

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("ptpedroVortal/nomic_vortal_v3.2")
# Run inference
sentences = [
    'Collect the details that are associated with product \'\' \'Macbook Air 13" com processador M1/M2 e 8 GB de RAM (Telado PT-PT)\', with quantity 1, unit UN',
    'Apresenta -se de seguida a configuração financeira para a fornecimento  dos produtos \\nrequeridos , mediante opções por cor e diferentes características:\\nNOTA:  Valores válidos  até 23 de Fevereiro e mediante adjudicação de 2 ou mais \\nequipamentos  portáteis (excluindo Teclado)\\nPART-NUMBER QTD. DESCRIÇÃOVALOR\\nUNITÁRIOVALOR\\nTOTAL\\nMLY03PO/A 1Apple Macbook AIR  13,6"  (Disco 512GB SSD; 10 core) 1 545,08 €                 1 545,08 €\\nMLXY3PO/A 1Apple Macbook AIR  13,6" (Disco 256GB SSD, 8 core) 1 227,48 €                 1 227,48 €',
    'LOTE 5\n1 MESA APOIO MESA DE APOIO EM INOX AISI 304 2,0 279,000 23,0 558,000\nMesa com 4 rodas , 2 com travão\nTabuleiro inferior\nDimens: C 700 x L 500 x A 800mm\nPrateleira inferior - profundidade 250mm\nFabrico Nacional e por medida\nTotal do do lote 5: 558,00€ Quinhentos e cinquenta e oito euros',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 222 training samples
Columns: query and correct_node
Approximate statistics based on the first 222 samples:
query correct_node
type string string
details
min: 15 tokens
mean: 55.17 tokens
max: 154 tokens
min: 22 tokens
mean: 109.22 tokens
max: 2920 tokens

Samples:

query	correct_node
`Collect the details that are associated with Lot 4 product '' 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit`	`LOTE 4[object Object]1 MESA DE MAYO 82JM 10.ME.1831 2,000 842,00000 23 1 684,00[object Object]oitocentos e quarenta e dois euros[object Object]Origem : Nacional[object Object]Marca : MOBIT[object Object]Prazo de entrega: 30 dias[object Object]Garantia: 2 anos[object Object]Transporte`
`Collect the details that are associated with Lot 7 product '' 'Carro transporte de roupa suja ', with quantity 1, unit US`	`Lote 7 nan nan nan nan nan\nRef. Description Qt. Un. Un. Price Total\n9856 Carros para Transporte de Roupa Suja e Limpa 1 US 16.23 16.23</code>`
`Collect the details that are associated with product '' '2202000014 - FIO SUT. SEDA NÃO ABS. 2/0 MULTIF. SEM AGULHA (CART.)', with quantity 72, unit UN`	`2202000014 - FIO SUT. SEDA NÃO ABS. 2/0 MULTIF. SEM AGULHA (CART.) 0.36`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 27 evaluation samples
Columns: query and correct_node
Approximate statistics based on the first 27 samples:
query correct_node
type string string
details
min: 17 tokens
mean: 56.85 tokens
max: 121 tokens
min: 40 tokens
mean: 228.15 tokens
max: 2963 tokens

Samples:

query	correct_node
`Collect the details that are associated with product '' '2202000055 - FIO SUT. POLIAMIDA NÃO ABS. 2/0 MONOF. AG. LANC. 39 MM 3/8 C (CART.)', with quantity 1656, unit UN`	`2202000055 - FIO SUT. POLIAMIDA NÃO ABS. 2/0 MONOF. AG. LANC. 39 MM 3/8 C (CART.) 1.28`
`Collect the details that are associated with Lot 3 product 'Portaria do Parque Coberto dos Olhos de Água' 'Vigilância e segurança humana contínua - Olhos de Água - período de 3 meses - todos os dias da semana, incluindo feriados, total estimado de 2754H', with quantity 1, unit UN`
`Collect the details that are associated with Lot 3 product 'Portaria do Parque Coberto dos Olhos de Água' 'Vigilância e segurança humana contínua - Olhos de Água - período de 3 meses - todos os dias da semana, incluindo feriados, total estimado de 2754H', with quantity 1, unit UN`	`Lote 3:\nPreço Unitário: 10,00€ (dez euros) /hora\nPreço Total: 27.540,00€ (vinte sete mil quinhentos e quarenta euros)`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
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: 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: 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
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
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
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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.3.1
Transformers: 4.47.0.dev0
PyTorch: 2.5.1+cu121
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.4

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",
}

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}
}

	query	correct_node
type	string	string
details	min: 15 tokens mean: 55.17 tokens max: 154 tokens	min: 22 tokens mean: 109.22 tokens max: 2920 tokens