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annazdr
/
nace-pl-v1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:6413
loss:BatchAllTripletLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community
annazdr commited on
Commit
0384969
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1 Parent(s): 5613fdd

Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +348 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +64 -0
  12. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ language: []
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+ library_name: sentence-transformers
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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:6413
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+ - loss:BatchAllTripletLoss
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ datasets: []
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+ widget:
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+ - source_sentence: manufacture of charging stations for cars
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+ sentences:
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+ - ' education in public and private universities provided at the level of: short-cycle
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+ studies, usually based on practical learning appropriate to the performance of
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+ a given profession and preparing students for entry into the labour market or
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+ other higher education programmes'
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+ - g
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+ - Działalność agentów zajmujących się sprzedażą hurtową płodów rolnych, żywych zwierząt,
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+ surowców dla przemysłu tekstylnego i półproduktów
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+ - source_sentence: manufacture of carbon and graphite fibres and products (except
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+ electrodes and electrical applications)
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+ sentences:
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+ - educational testing evaluation activities
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+ - Obróbka i wykończanie produktów z tworzyw sztucznych
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+ - ' manufacture of all electric motors and transformers: ac, dc and ac/dc'
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+ - source_sentence: ' retail sale of sports goods, including fishing gear, weapons
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+ and ammunitions, camping goods, etc'
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+ sentences:
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+ - anise, badian and fennel as spice or aromatic plant
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+ - ' transformers)'
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+ - retail sale of knitting yarn
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+ - source_sentence: community and neighbourhood activities
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+ sentences:
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+ - manufacture of glass eyes
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+ - uprawa trzciny cukrowej
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+ - ' revenue for the intermediation activities can include other sources of income,
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+ e'
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+ - source_sentence: dating and other speed networking activities
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+ sentences:
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+ - sprzedaż detaliczna prowadzona na straganach i targowiskach ryb, owoców morza,
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+ produktów z owoców morza i ich przetworów, sprzedaż detaliczna prowadzona na straganach
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+ i targowiskach alg i wodorostów
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+ - ' pressure, pushbutton, snap, tumbler switches)'
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+ - ' dializy, chemioterapia, insulinoterapia, radioterapia'
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+ pipeline_tag: sentence-similarity
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Output Dimensionality:** 384 tokens
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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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+
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+ ### Model Sources
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+
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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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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, '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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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("annazdr/nace-pl-v1")
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+ # Run inference
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+ sentences = [
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+ 'dating and other speed networking activities',
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+ ' pressure, pushbutton, snap, tumbler switches)',
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+ ' dializy, chemioterapia, insulinoterapia, radioterapia',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 384]
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+
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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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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 6,413 training samples
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+ * Columns: <code>sentence_0</code> and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | type | string | int |
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+ | details | <ul><li>min: 2 tokens</li><li>mean: 17.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~0.10%</li><li>1: ~0.30%</li><li>2: ~0.40%</li><li>3: ~0.20%</li><li>4: ~0.40%</li><li>5: ~0.10%</li><li>6: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.20%</li><li>10: ~0.10%</li><li>11: ~0.40%</li><li>12: ~0.30%</li><li>13: ~0.10%</li><li>14: ~0.10%</li><li>15: ~0.20%</li><li>16: ~0.10%</li><li>17: ~0.30%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.40%</li><li>21: ~0.30%</li><li>22: ~0.10%</li><li>23: ~0.30%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.10%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.20%</li><li>31: ~0.10%</li><li>34: ~0.20%</li><li>36: ~0.10%</li><li>37: ~0.30%</li><li>39: ~0.20%</li><li>40: ~0.60%</li><li>41: ~0.10%</li><li>42: ~0.30%</li><li>43: ~0.20%</li><li>44: ~0.60%</li><li>45: ~0.50%</li><li>46: ~0.20%</li><li>47: ~0.10%</li><li>48: ~0.10%</li><li>49: ~0.20%</li><li>50: ~0.20%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: 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~0.10%</li><li>420: ~0.20%</li><li>422: ~0.50%</li><li>423: ~0.10%</li><li>425: ~0.40%</li><li>426: ~0.10%</li><li>427: ~0.10%</li><li>428: ~0.40%</li><li>429: ~0.20%</li><li>430: ~0.10%</li><li>431: ~0.10%</li><li>432: ~0.10%</li><li>433: ~0.20%</li><li>434: ~0.30%</li><li>435: ~0.20%</li><li>436: ~0.40%</li><li>437: ~0.10%</li><li>438: ~0.40%</li><li>440: ~0.80%</li><li>441: ~0.20%</li><li>442: ~0.50%</li><li>443: ~0.20%</li><li>444: ~0.30%</li><li>445: ~0.30%</li><li>446: ~0.10%</li><li>447: ~0.20%</li><li>450: ~0.30%</li><li>451: ~0.20%</li><li>452: ~0.20%</li><li>453: ~0.10%</li><li>454: ~0.20%</li><li>455: ~0.30%</li><li>456: ~0.10%</li><li>457: ~0.10%</li><li>458: ~0.20%</li><li>459: ~0.20%</li><li>460: ~0.10%</li><li>461: ~0.10%</li><li>462: ~0.10%</li><li>463: ~0.40%</li><li>464: ~0.30%</li><li>467: ~0.10%</li><li>469: ~0.10%</li><li>470: ~0.10%</li><li>472: ~0.10%</li><li>475: ~0.50%</li><li>476: ~0.30%</li><li>478: ~0.10%</li><li>479: ~0.20%</li><li>480: ~0.10%</li><li>482: ~0.30%</li><li>483: ~0.50%</li><li>484: ~0.30%</li><li>485: ~0.40%</li><li>486: ~0.20%</li><li>487: ~0.20%</li><li>489: ~0.10%</li><li>490: ~0.20%</li><li>491: ~0.10%</li><li>492: ~0.40%</li><li>493: ~0.40%</li><li>495: ~0.10%</li><li>497: ~0.10%</li><li>498: ~0.10%</li><li>499: ~0.30%</li><li>501: ~0.20%</li><li>502: ~0.20%</li><li>503: ~0.10%</li><li>504: ~0.30%</li><li>505: ~0.10%</li><li>506: ~0.10%</li><li>507: ~0.10%</li><li>508: ~0.20%</li><li>509: ~0.10%</li><li>510: ~0.10%</li><li>511: ~0.10%</li><li>512: ~0.50%</li><li>514: ~0.20%</li><li>517: ~0.40%</li><li>518: ~0.10%</li><li>519: ~0.60%</li><li>520: ~0.90%</li><li>521: ~0.60%</li><li>522: ~0.10%</li><li>523: ~0.10%</li><li>524: ~0.10%</li><li>525: ~0.10%</li><li>526: ~0.10%</li><li>527: ~0.10%</li><li>528: ~0.30%</li><li>529: ~0.40%</li><li>530: ~0.60%</li><li>531: ~0.20%</li><li>532: ~0.10%</li><li>533: ~0.30%</li><li>535: ~0.50%</li><li>537: ~0.20%</li><li>540: ~0.10%</li><li>541: ~0.10%</li><li>542: ~0.20%</li><li>543: ~0.30%</li><li>544: ~0.20%</li><li>545: ~0.50%</li><li>546: ~0.30%</li><li>547: ~0.10%</li><li>548: ~0.50%</li><li>549: ~0.10%</li><li>550: ~0.30%</li><li>551: ~0.30%</li><li>552: ~0.10%</li><li>554: ~0.60%</li><li>555: ~0.20%</li><li>556: ~0.10%</li><li>557: ~0.10%</li><li>560: ~0.20%</li><li>561: ~0.10%</li><li>563: ~0.20%</li><li>564: ~0.20%</li><li>565: ~0.20%</li><li>567: ~0.10%</li><li>568: ~0.20%</li><li>570: ~0.20%</li><li>571: ~0.10%</li><li>572: ~0.20%</li><li>573: ~0.20%</li><li>576: ~0.20%</li><li>579: ~0.10%</li></ul> |
164
+ * Samples:
165
+ | sentence_0 | label |
166
+ |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
167
+ | <code>retail sale of wooden, cork and wickerwork goods</code> | <code>202</code> |
168
+ | <code>e</code> | <code>298</code> |
169
+ | <code>produkcję maszyn do obróbki miękkiej gumy lub tworzyw sztucznych oraz wytwarzania wyrobów z tych materiałów: wytłaczarek, maszyn do formowania, maszyn do produkcji lub bieżnikowania opon pneumatycznych oraz pozostałych maszyn do produkcji wyrobów z gumy lub tworzyw sztucznych</code> | <code>79</code> |
170
+ * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
171
+
172
+ ### Training Hyperparameters
173
+ #### Non-Default Hyperparameters
174
+
175
+ - `per_device_train_batch_size`: 256
176
+ - `per_device_eval_batch_size`: 256
177
+ - `num_train_epochs`: 4
178
+ - `multi_dataset_batch_sampler`: round_robin
179
+
180
+ #### All Hyperparameters
181
+ <details><summary>Click to expand</summary>
182
+
183
+ - `overwrite_output_dir`: False
184
+ - `do_predict`: False
185
+ - `eval_strategy`: no
186
+ - `prediction_loss_only`: True
187
+ - `per_device_train_batch_size`: 256
188
+ - `per_device_eval_batch_size`: 256
189
+ - `per_gpu_train_batch_size`: None
190
+ - `per_gpu_eval_batch_size`: None
191
+ - `gradient_accumulation_steps`: 1
192
+ - `eval_accumulation_steps`: None
193
+ - `learning_rate`: 5e-05
194
+ - `weight_decay`: 0.0
195
+ - `adam_beta1`: 0.9
196
+ - `adam_beta2`: 0.999
197
+ - `adam_epsilon`: 1e-08
198
+ - `max_grad_norm`: 1
199
+ - `num_train_epochs`: 4
200
+ - `max_steps`: -1
201
+ - `lr_scheduler_type`: linear
202
+ - `lr_scheduler_kwargs`: {}
203
+ - `warmup_ratio`: 0.0
204
+ - `warmup_steps`: 0
205
+ - `log_level`: passive
206
+ - `log_level_replica`: warning
207
+ - `log_on_each_node`: True
208
+ - `logging_nan_inf_filter`: True
209
+ - `save_safetensors`: True
210
+ - `save_on_each_node`: False
211
+ - `save_only_model`: False
212
+ - `restore_callback_states_from_checkpoint`: False
213
+ - `no_cuda`: False
214
+ - `use_cpu`: False
215
+ - `use_mps_device`: False
216
+ - `seed`: 42
217
+ - `data_seed`: None
218
+ - `jit_mode_eval`: False
219
+ - `use_ipex`: False
220
+ - `bf16`: False
221
+ - `fp16`: False
222
+ - `fp16_opt_level`: O1
223
+ - `half_precision_backend`: auto
224
+ - `bf16_full_eval`: False
225
+ - `fp16_full_eval`: False
226
+ - `tf32`: None
227
+ - `local_rank`: 0
228
+ - `ddp_backend`: None
229
+ - `tpu_num_cores`: None
230
+ - `tpu_metrics_debug`: False
231
+ - `debug`: []
232
+ - `dataloader_drop_last`: False
233
+ - `dataloader_num_workers`: 0
234
+ - `dataloader_prefetch_factor`: None
235
+ - `past_index`: -1
236
+ - `disable_tqdm`: False
237
+ - `remove_unused_columns`: True
238
+ - `label_names`: None
239
+ - `load_best_model_at_end`: False
240
+ - `ignore_data_skip`: False
241
+ - `fsdp`: []
242
+ - `fsdp_min_num_params`: 0
243
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
244
+ - `fsdp_transformer_layer_cls_to_wrap`: None
245
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
246
+ - `deepspeed`: None
247
+ - `label_smoothing_factor`: 0.0
248
+ - `optim`: adamw_torch
249
+ - `optim_args`: None
250
+ - `adafactor`: False
251
+ - `group_by_length`: False
252
+ - `length_column_name`: length
253
+ - `ddp_find_unused_parameters`: None
254
+ - `ddp_bucket_cap_mb`: None
255
+ - `ddp_broadcast_buffers`: False
256
+ - `dataloader_pin_memory`: True
257
+ - `dataloader_persistent_workers`: False
258
+ - `skip_memory_metrics`: True
259
+ - `use_legacy_prediction_loop`: False
260
+ - `push_to_hub`: False
261
+ - `resume_from_checkpoint`: None
262
+ - `hub_model_id`: None
263
+ - `hub_strategy`: every_save
264
+ - `hub_private_repo`: False
265
+ - `hub_always_push`: False
266
+ - `gradient_checkpointing`: False
267
+ - `gradient_checkpointing_kwargs`: None
268
+ - `include_inputs_for_metrics`: False
269
+ - `eval_do_concat_batches`: True
270
+ - `fp16_backend`: auto
271
+ - `push_to_hub_model_id`: None
272
+ - `push_to_hub_organization`: None
273
+ - `mp_parameters`:
274
+ - `auto_find_batch_size`: False
275
+ - `full_determinism`: False
276
+ - `torchdynamo`: None
277
+ - `ray_scope`: last
278
+ - `ddp_timeout`: 1800
279
+ - `torch_compile`: False
280
+ - `torch_compile_backend`: None
281
+ - `torch_compile_mode`: None
282
+ - `dispatch_batches`: None
283
+ - `split_batches`: None
284
+ - `include_tokens_per_second`: False
285
+ - `include_num_input_tokens_seen`: False
286
+ - `neftune_noise_alpha`: None
287
+ - `optim_target_modules`: None
288
+ - `batch_eval_metrics`: False
289
+ - `batch_sampler`: batch_sampler
290
+ - `multi_dataset_batch_sampler`: round_robin
291
+
292
+ </details>
293
+
294
+ ### Framework Versions
295
+ - Python: 3.10.12
296
+ - Sentence Transformers: 3.0.1
297
+ - Transformers: 4.41.2
298
+ - PyTorch: 2.3.0+cu121
299
+ - Accelerate: 0.31.0
300
+ - Datasets: 2.20.0
301
+ - Tokenizers: 0.19.1
302
+
303
+ ## Citation
304
+
305
+ ### BibTeX
306
+
307
+ #### Sentence Transformers
308
+ ```bibtex
309
+ @inproceedings{reimers-2019-sentence-bert,
310
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
311
+ author = "Reimers, Nils and Gurevych, Iryna",
312
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
313
+ month = "11",
314
+ year = "2019",
315
+ publisher = "Association for Computational Linguistics",
316
+ url = "https://arxiv.org/abs/1908.10084",
317
+ }
318
+ ```
319
+
320
+ #### BatchAllTripletLoss
321
+ ```bibtex
322
+ @misc{hermans2017defense,
323
+ title={In Defense of the Triplet Loss for Person Re-Identification},
324
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
325
+ year={2017},
326
+ eprint={1703.07737},
327
+ archivePrefix={arXiv},
328
+ primaryClass={cs.CV}
329
+ }
330
+ ```
331
+
332
+ <!--
333
+ ## Glossary
334
+
335
+ *Clearly define terms in order to be accessible across audiences.*
336
+ -->
337
+
338
+ <!--
339
+ ## Model Card Authors
340
+
341
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
342
+ -->
343
+
344
+ <!--
345
+ ## Model Card Contact
346
+
347
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
348
+ -->
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