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---
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:2382
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- loss:MultipleNegativesRankingLoss
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base_model: nomic-ai/nomic-embed-text-v1
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widget:
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- source_sentence: Collect the details that are associated with product '- Com espessura
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constante de' '- 0,04 m', with quantity 1900, unit M2
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sentences:
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- 'Item Description: UNKNOWN PRODUCT, priced at 949.00 EUR, Origin: National'
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- 'Product: UNKNOWN PRODUCT, Estimated Value: 514.00 EUR'
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- "Details for 'MacBook Pro 14\" Processador M2/3 16GB/18GB RAM | SSD 512GB | Teclado\
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\ Es-Es', with quantity 1, unit UN:\n - LOTE 31\n - Price: 656.00 EUR"
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- source_sentence: Collect the details that are associated with Lot 14 product ''
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'Monitor de Sinais Vitais ', with quantity 2, unit Subcontracting Unit
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sentences:
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- "Details for 'Monitor de Sinais Vitais ', with quantity 2, unit Subcontracting\
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\ Unit:\n - LOTE 60\n - Price: 564.00 EUR"
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- "Details for UNKNOWN PRODUCT:\n - LOTE 90\n - Price: 658.00 EUR"
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- 'Item Description: UNKNOWN PRODUCT, priced at 90.00 EUR, Origin: National'
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- source_sentence: Collect the details that are associated with product '' '2202000270
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- FIO SUT. AC. POLIGLIC. ABS. RÁPIDA 4/0 MULTIF AG. CILIND. 17 MM 1/2 C (UNID)',
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with quantity 288, unit UN
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sentences:
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- 'Item Description: ''2202000270 - FIO SUT. AC. POLIGLIC. ABS. RÁPIDA 4/0 MULTIF
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AG. CILIND. 17 MM 1/2 C (UNID)'', with quantity 288, unit UN, priced at 66.00
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EUR, Origin: National'
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- 'Product: ''2202000285 - FIO SUT. POLIPROPI. NÃO ABS. 4/0 MONOF. AG. LANC. 16
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MM 3/8 (UNID)'', with quantity 468, unit UN, Estimated Value: 619.00 EUR'
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- 'Item Description: ''Carro transporte de roupa limpa/roupa suja'', with quantity
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1, unit Subcontracting Unit, priced at 574.00 EUR, Origin: National'
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- source_sentence: Collect the details that are associated with product '' '2202000006
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- FIO SUT. SEDA NÃO ABS. 0 MULTIF. SEM AGULHA (CART.)', with quantity 72, unit
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UN
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sentences:
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- 'Item Description: ''2202000309 - FIO SUT. ABS. MÉDIO PRAZO 2/0 MONOF. BARBADO,
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C/ AG. CILIND. 30MM 1/2C, 23CM (CART.)'', with quantity 24, unit UN, priced at
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206.00 EUR, Origin: National'
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- "Details for '2202000006 - FIO SUT. SEDA NÃO ABS. 0 MULTIF. SEM AGULHA (CART.)',\
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\ with quantity 72, unit UN:\n - LOTE 82\n - Price: 854.00 EUR"
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- 'LOTE 10
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Description: ''Mesas apoio (anestesia e circulante)'', with quantity 4, unit Subcontracting
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Unit
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Price: 117.00 EUR'
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- source_sentence: Collect the details that are associated with product '' '2202000251
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- FIO SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM 1/2C 90CM (CART.)', with quantity
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144, unit UN
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sentences:
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- "Details for UNKNOWN PRODUCT:\n - LOTE 34\n - Price: 477.00 EUR"
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- "Details for '2202000251 - FIO SUT. ABS. LONGA 1 MONOF. AG. CILIND. 48 MM 1/2C\
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\ 90CM (CART.)', with quantity 144, unit UN:\n - LOTE 73\n - Price: 644.00 EUR"
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- 'Item Description: ''Mesas de Mayo'', with quantity 2, unit Subcontracting Unit,
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priced at 651.00 EUR, Origin: National'
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- pearson_cosine
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- spearman_cosine
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model-index:
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- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1
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results:
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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dataset:
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name: Unknown
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type: unknown
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metrics:
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- type: pearson_cosine
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value: .nan
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name: Pearson Cosine
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- type: spearman_cosine
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value: .nan
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name: Spearman Cosine
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---
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# SentenceTransformer based on nomic-ai/nomic-embed-text-v1
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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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [nomic-ai/nomic-embed-text-v1](https://huggingface.co./nomic-ai/nomic-embed-text-v1) <!-- at revision 720244025c1a7e15661a174c63cce63c8218e52b -->
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- **Maximum Sequence Length:** 8192 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
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(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})
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(2): Normalize()
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("ptpedroVortal/nomic_vortal_v3.4")
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# Run inference
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sentences = [
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"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",
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"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",
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"Item Description: 'Mesas de Mayo', with quantity 2, unit Subcontracting Unit, priced at 651.00 EUR, Origin: National",
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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## Evaluation
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### Metrics
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#### Semantic Similarity
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* Evaluated with <code>__main__.CustomEvaluator</code>
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| Metric | Value |
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|:--------------------|:--------|
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| pearson_cosine | nan |
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| **spearman_cosine** | **nan** |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### Unnamed Dataset
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* Size: 2,382 training samples
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* Columns: <code>query</code>, <code>correct_node</code>, and <code>score</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | correct_node | score |
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|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------|
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| type | string | string | int |
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| 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> |
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* Samples:
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| query | correct_node | score |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Evaluation Dataset
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#### Unnamed Dataset
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* Size: 297 evaluation samples
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* Columns: <code>query</code>, <code>correct_node</code>, and <code>score</code>
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* Approximate statistics based on the first 297 samples:
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| | query | correct_node | score |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------|
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| type | string | string | int |
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| 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> |
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* Samples:
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| query | correct_node | score |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `num_train_epochs`: 10
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- `warmup_ratio`: 0.1
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- `bf16`: True
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- `load_best_model_at_end`: True
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- `batch_sampler`: no_duplicates
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|
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 5e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 10
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: True
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
|
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
|
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
|
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- `dataloader_prefetch_factor`: None
|
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- `past_index`: -1
|
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- `disable_tqdm`: False
|
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- `remove_unused_columns`: True
|
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- `label_names`: None
|
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- `load_best_model_at_end`: True
|
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- `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}
|
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- `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}
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- `deepspeed`: None
|
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- `label_smoothing_factor`: 0.0
|
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- `optim`: adamw_torch
|
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- `optim_args`: None
|
|
- `adafactor`: False
|
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- `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
|
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- `prompts`: None
|
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
|
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|
|
</details>
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### Training Logs
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| Epoch | Step | Training Loss | Validation Loss | spearman_cosine |
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|:----------:|:-------:|:-------------:|:---------------:|:---------------:|
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| 0.6711 | 100 | 0.6485 | 0.4410 | nan |
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| 1.3356 | 200 | 0.5026 | 0.4399 | nan |
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| **2.0067** | **300** | **0.491** | **0.4175** | **nan** |
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| 2.6711 | 400 | 0.442 | 0.4409 | nan |
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| 3.3356 | 500 | 0.3999 | 0.4421 | nan |
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| 4.0067 | 600 | 0.367 | 0.6182 | nan |
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| 4.6711 | 700 | 0.3743 | 0.6104 | nan |
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| 5.3356 | 800 | 0.1972 | 0.6115 | nan |
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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- Python: 3.10.14
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- Sentence Transformers: 3.3.1
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- Transformers: 4.47.0.dev0
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- PyTorch: 2.5.1+cu121
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- Accelerate: 1.1.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.4
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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|
year = "2019",
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|
publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### MultipleNegativesRankingLoss
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```bibtex
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@misc{henderson2017efficient,
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title={Efficient Natural Language Response Suggestion for Smart Reply},
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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},
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|
year={2017},
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|
eprint={1705.00652},
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archivePrefix={arXiv},
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|
primaryClass={cs.CL}
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|
}
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|
```
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|
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