sentence-transformers safetensors nomic_bert sentence-similarity feature-extraction generated_from_trainer dataset_size:2382 loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:1705.00652 base_model:nomic-ai/nomic-embed-text-v1 base_model:finetune:nomic-ai/nomic-embed-text-v1 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: nomic-ai/nomic-embed-text-v1
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2382
- loss:MultipleNegativesRankingLoss
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:\n  - LOTE 31\n  - 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:\n  - LOTE 60\n  - Price: 564.00 EUR"
  - "Details for UNKNOWN PRODUCT:\n  - LOTE 90\n  - 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:\n  - LOTE 82\n  - 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:\n  - LOTE 34\n  - 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:\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'
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](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co/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](https://huggingface.co/nomic-ai/nomic-embed-text-v1) <!-- at revision 720244025c1a7e15661a174c63cce63c8218e52b -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
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_v3.4")
# 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]
```

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You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with <code>__main__.CustomEvaluator</code>

| Metric              | Value   |
|:--------------------|:--------|
| pearson_cosine      | nan     |
| **spearman_cosine** | **nan** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 2,382 training samples
* Columns: <code>query</code>, <code>correct_node</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                              | correct_node                                                                         | score                        |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                             | string                                                                               | int                          |
  | details | <ul><li>min: 15 tokens</li><li>mean: 56.3 tokens</li><li>max: 154 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 49.65 tokens</li><li>max: 1729 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | query                                                                                                                                                                                  | correct_node                                                                                                                                                                | score          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>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</code> | <code>LOTE 98<br>Description: '2202000275 - FIO SUT. POLIAMIDA NÃO ABS. 2/0 MONOF AG. CILIND. 30MM 1/2 LOOP (UNID)', with quantity 216, unit UN<br>Price: 940.00 EUR</code> | <code>1</code> |
  | <code>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</code>   | <code>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</code>          | <code>1</code> |
  | <code>Collect the details that are associated with Lot 4 product '' 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit</code>                                                  | <code>LOTE 44<br>Description: 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit<br>Price: 542.00 EUR</code>                                                        | <code>1</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 297 evaluation samples
* Columns: <code>query</code>, <code>correct_node</code>, and <code>score</code>
* Approximate statistics based on the first 297 samples:
  |         | query                                                                               | correct_node                                                                        | score                        |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                              | string                                                                              | int                          |
  | details | <ul><li>min: 15 tokens</li><li>mean: 55.37 tokens</li><li>max: 154 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 46.58 tokens</li><li>max: 435 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | query                                                                                                                                                  | correct_node                                                                                                                                       | score          |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Collect the details that are associated with Lot 7 product '' 'Carro transporte de roupa suja', with quantity 1, unit Subcontracting Unit</code> | <code>Item Description: 'Carro transporte de roupa suja', with quantity 1, unit Subcontracting Unit, priced at 628.00 EUR, Origin: National</code> | <code>1</code> |
  | <code>Collect the details that are associated with Lot 10 product '' 'Mesas para cirurgia', with quantity 2, unit Subcontracting Unit</code>           | <code>Details for 'Mesas para cirurgia', with quantity 2, unit Subcontracting Unit:<br>  - LOTE 83<br>  - Price: 940.00 EUR</code>                 | <code>1</code> |
  | <code>Collect the details that are associated with Lot 1 product '' 'PAINEL MULTIPLO ALERGENOS RESPIRATORIOS ', with quantity 1152, unit UND</code>    | <code>Product: 'PAINEL MULTIPLO ALERGENOS RESPIRATORIOS ', with quantity 1152, unit UND, Estimated Value: 714.00 EUR</code>                        | <code>1</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "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
<details><summary>Click to expand</summary>

- `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

</details>

### 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
```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}
}
```

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