metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2382
  - loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1
widget:
  - source_sentence: >-
      Collect the details that are associated with product '- Com espessura
      constante de' '- 0,04 m', with quantity 1900, unit M2
    sentences:
      - >-
        Item Description: UNKNOWN PRODUCT, priced at 949.00 EUR, Origin:
        National
      - 'Product: UNKNOWN PRODUCT, Estimated Value: 514.00 EUR'
      - >-
        Details for 'MacBook Pro 14" Processador M2/3 16GB/18GB RAM | SSD 512GB
        | Teclado Es-Es', with quantity 1, unit UN:
          - LOTE 31
          - Price: 656.00 EUR
  - source_sentence: >-
      Collect the details that are associated with Lot 14 product '' 'Monitor de
      Sinais Vitais ', with quantity 2, unit Subcontracting Unit
    sentences:
      - >-
        Details for 'Monitor de Sinais Vitais ', with quantity 2, unit
        Subcontracting Unit:
          - LOTE 60
          - Price: 564.00 EUR
      - |-
        Details for UNKNOWN PRODUCT:
          - LOTE 90
          - Price: 658.00 EUR
      - 'Item Description: UNKNOWN PRODUCT, priced at 90.00 EUR, Origin: National'
  - source_sentence: >-
      Collect the details that are associated with product '' '2202000270 - FIO
      SUT. AC. POLIGLIC. ABS. RÁPIDA 4/0 MULTIF AG. CILIND. 17 MM 1/2 C (UNID)',
      with quantity 288, unit UN
    sentences:
      - >-
        Item Description: '2202000270 - FIO SUT. AC. POLIGLIC. ABS. RÁPIDA 4/0
        MULTIF AG. CILIND. 17 MM 1/2 C (UNID)', with quantity 288, unit UN,
        priced at 66.00 EUR, Origin: National
      - >-
        Product: '2202000285 - FIO SUT. POLIPROPI. NÃO ABS. 4/0 MONOF. AG. LANC.
        16 MM 3/8 (UNID)', with quantity 468, unit UN, Estimated Value: 619.00
        EUR
      - >-
        Item Description: 'Carro transporte de roupa limpa/roupa suja', with
        quantity 1, unit Subcontracting Unit, priced at 574.00 EUR, Origin:
        National
  - source_sentence: >-
      Collect the details that are associated with product '' '2202000006 - FIO
      SUT. SEDA NÃO ABS. 0 MULTIF. SEM AGULHA (CART.)', with quantity 72, unit
      UN
    sentences:
      - >-
        Item Description: '2202000309 - FIO SUT. ABS. MÉDIO PRAZO 2/0 MONOF.
        BARBADO, C/ AG. CILIND. 30MM 1/2C, 23CM (CART.)', with quantity 24, unit
        UN, priced at 206.00 EUR, Origin: National
      - >-
        Details for '2202000006 - FIO SUT. SEDA NÃO ABS. 0 MULTIF. SEM AGULHA
        (CART.)', with quantity 72, unit UN:
          - LOTE 82
          - Price: 854.00 EUR
      - >-
        LOTE 10

        Description: 'Mesas apoio (anestesia e circulante)', with quantity 4,
        unit Subcontracting Unit

        Price: 117.00 EUR
  - source_sentence: >-
      Collect the details that are associated with product '' '2202000251 - FIO
      SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM 1/2C 90CM (CART.)', with
      quantity 144, unit UN
    sentences:
      - |-
        Details for UNKNOWN PRODUCT:
          - LOTE 34
          - Price: 477.00 EUR
      - >-
        Details for '2202000251 - FIO SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM
        1/2C 90CM (CART.)', with quantity 144, unit UN:
          - LOTE 73
          - Price: 644.00 EUR
      - >-
        Item Description: 'Mesas de Mayo', with quantity 2, unit Subcontracting
        Unit, priced at 651.00 EUR, Origin: National
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine

SentenceTransformer based on nomic-ai/nomic-embed-text-v1

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("ptpedroVortal/nomic_vortal_v3.4")
# Run inference
sentences = [
    "Collect the details that are associated with product '' '2202000251 - FIO SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM 1/2C 90CM (CART.)', with quantity 144, unit UN",
    "Details for '2202000251 - FIO SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM 1/2C 90CM (CART.)', with quantity 144, unit UN:\n  - LOTE 73\n  - Price: 644.00 EUR",
    "Item Description: 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit, priced at 651.00 EUR, Origin: National",
]
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]

Evaluation

Metrics

Semantic Similarity

Evaluated with main.CustomEvaluator

Metric	Value
pearson_cosine	nan
spearman_cosine	nan

Training Details

Training Dataset

Unnamed Dataset

Size: 2,382 training samples
Columns: query, correct_node, and score
Approximate statistics based on the first 1000 samples:
query correct_node score
type string string int
details
min: 15 tokens
mean: 56.3 tokens
max: 154 tokens

min: 15 tokens
mean: 49.65 tokens
max: 1729 tokens

1: 100.00%

	query	correct_node	score
type	string	string	int
details	min: 15 tokens mean: 56.3 tokens max: 154 tokens	min: 15 tokens mean: 49.65 tokens max: 1729 tokens	1: 100.00%

Samples:

query	correct_node	score
`Collect the details that are associated with product '' '2202000275 - FIO SUT. POLIAMIDA NÃO ABS. 2/0 MONOF AG. CILIND. 30MM 1/2 LOOP (UNID)', with quantity 216, unit UN`	`LOTE 98 Description: '2202000275 - FIO SUT. POLIAMIDA NÃO ABS. 2/0 MONOF AG. CILIND. 30MM 1/2 LOOP (UNID)', with quantity 216, unit UN Price: 940.00 EUR`	`1`
`Collect the details that are associated with product '' '2202000294 - FIO SUT. AC. POLIGLIC. ABS. 2/0 MULTIF SEM AGULHA PRÉ CORTADO (UNID)', with quantity 324, unit UN`	`Product: '2202000294 - FIO SUT. AC. POLIGLIC. ABS. 2/0 MULTIF SEM AGULHA PRÉ CORTADO (UNID)', with quantity 324, unit UN, Estimated Value: 696.00 EUR`	`1`
`Collect the details that are associated with Lot 4 product '' 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit`	`LOTE 44 Description: 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit Price: 542.00 EUR`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 297 evaluation samples
Columns: query, correct_node, and score
Approximate statistics based on the first 297 samples:
query correct_node score
type string string int
details
min: 15 tokens
mean: 55.37 tokens
max: 154 tokens

min: 15 tokens
mean: 46.58 tokens
max: 435 tokens

1: 100.00%

	query	correct_node	score
type	string	string	int
details	min: 15 tokens mean: 55.37 tokens max: 154 tokens	min: 15 tokens mean: 46.58 tokens max: 435 tokens	1: 100.00%

Samples:

query	correct_node	score
`Collect the details that are associated with Lot 7 product '' 'Carro transporte de roupa suja', with quantity 1, unit Subcontracting Unit`	`Item Description: 'Carro transporte de roupa suja', with quantity 1, unit Subcontracting Unit, priced at 628.00 EUR, Origin: National`	`1`
`Collect the details that are associated with Lot 10 product '' 'Mesas para cirurgia', with quantity 2, unit Subcontracting Unit`	`Details for 'Mesas para cirurgia', with quantity 2, unit Subcontracting Unit: - LOTE 83 - Price: 940.00 EUR`	`1`
`Collect the details that are associated with Lot 1 product '' 'PAINEL MULTIPLO ALERGENOS RESPIRATORIOS ', with quantity 1152, unit UND`	`Product: 'PAINEL MULTIPLO ALERGENOS RESPIRATORIOS ', with quantity 1152, unit UND, Estimated Value: 714.00 EUR`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
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: 10
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	spearman_cosine
0.6711	100	0.6485	0.4410	nan
1.3356	200	0.5026	0.4399	nan
2.0067	300	0.491	0.4175	nan
2.6711	400	0.442	0.4409	nan
3.3356	500	0.3999	0.4421	nan
4.0067	600	0.367	0.6182	nan
4.6711	700	0.3743	0.6104	nan
5.3356	800	0.1972	0.6115	nan

The bold row denotes the saved checkpoint.

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

@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

@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}
}