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annazdr
/
nace-pl-v3

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:12826
loss:BatchAllTripletLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community
annazdr commited on
Commit
b3901e2
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verified ·
1 Parent(s): b9f14c4

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 +343 -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
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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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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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+ pipeline_tag: sentence-similarity
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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:12826
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+ - loss:BatchAllTripletLoss
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+ widget:
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+ - source_sentence: ' '
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+ sentences:
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+ - ' derrick erection in situ, repairing and dismantling'
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+ - Działalność usługowa związana z przeprowadzkami
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+ - provision of intermediation services for the purchase of land, water or air transportation
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+ for passengers
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+ - source_sentence: g
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+ sentences:
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+ - crushing, cleaning and sorting of other waste, e
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+ - ' students, scientists, staff, members as well as operation of government archives-
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+ activities related to the acquisition or collection of archives'
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+ - ' fundraising organisation services on a contract or fee basis- court reporting
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+ or stenotype recording services- public stenography services- real-time (i'
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+ - source_sentence: g
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+ sentences:
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+ - the programming may be of a general or specialised nature (e
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+ - ), słodkich lub solonych
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+ - composite diagnostic or laboratory reagents
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+ - source_sentence: palliative nursing care for out-patients
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+ sentences:
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+ - ' pre-sorting, addressing'
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+ - retail sale of recording media players and recorders
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+ - manufacture of gaiters, leggings and similar articles
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+ - source_sentence: ' this class also includes: laboratory doping control activities'
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+ sentences:
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+ - ' freight transport on mainline rail networks as well as short line freight railroads'
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+ - telecommunication carrier equipment
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+ - manufacture of toner cartridges
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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-v3")
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+ # Run inference
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+ sentences = [
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+ ' this class also includes: laboratory doping control activities',
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+ 'manufacture of toner cartridges',
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+ 'telecommunication carrier equipment',
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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: 12,826 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: 18.09 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>1: ~0.10%</li><li>2: ~0.30%</li><li>3: ~0.10%</li><li>4: ~0.40%</li><li>7: ~0.20%</li><li>8: ~0.10%</li><li>9: ~0.20%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.10%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.10%</li><li>21: ~0.20%</li><li>22: ~0.10%</li><li>23: ~0.20%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.10%</li><li>28: ~0.10%</li><li>29: ~0.10%</li><li>31: ~0.10%</li><li>32: ~0.10%</li><li>33: ~0.10%</li><li>34: ~0.20%</li><li>35: ~0.10%</li><li>37: ~0.20%</li><li>39: ~0.20%</li><li>40: ~0.40%</li><li>41: ~0.40%</li><li>42: ~0.10%</li><li>44: ~0.60%</li><li>45: ~0.10%</li><li>46: ~0.20%</li><li>48: ~0.20%</li><li>49: ~0.10%</li><li>51: ~0.10%</li><li>52: ~0.30%</li><li>54: ~0.20%</li><li>55: ~0.40%</li><li>56: ~0.10%</li><li>57: ~0.40%</li><li>58: ~0.20%</li><li>59: ~0.30%</li><li>60: ~0.20%</li><li>61: ~0.30%</li><li>62: ~0.20%</li><li>63: ~0.40%</li><li>64: ~0.60%</li><li>65: ~0.50%</li><li>66: ~1.00%</li><li>67: ~0.10%</li><li>69: ~0.50%</li><li>70: ~0.20%</li><li>71: ~0.10%</li><li>72: ~0.40%</li><li>73: ~0.60%</li><li>74: ~0.20%</li><li>75: ~0.10%</li><li>76: ~0.30%</li><li>77: ~0.10%</li><li>78: ~0.10%</li><li>80: ~0.10%</li><li>81: ~0.10%</li><li>82: ~0.80%</li><li>83: ~0.50%</li><li>84: ~0.10%</li><li>85: ~0.10%</li><li>86: ~0.30%</li><li>87: ~0.10%</li><li>88: ~0.30%</li><li>89: ~0.30%</li><li>90: ~0.60%</li><li>91: ~0.30%</li><li>92: ~0.10%</li><li>93: ~0.30%</li><li>94: ~0.20%</li><li>95: ~0.20%</li><li>96: ~0.20%</li><li>97: ~0.80%</li><li>98: ~0.20%</li><li>99: ~0.70%</li><li>100: ~0.70%</li><li>101: ~0.50%</li><li>102: ~0.60%</li><li>103: ~0.20%</li><li>104: ~0.10%</li><li>105: ~0.10%</li><li>106: ~0.20%</li><li>108: ~0.10%</li><li>109: ~0.40%</li><li>110: ~0.20%</li><li>111: ~0.30%</li><li>114: ~0.10%</li><li>115: ~0.10%</li><li>118: ~0.20%</li><li>120: ~0.10%</li><li>122: ~0.20%</li><li>123: ~0.10%</li><li>125: ~0.20%</li><li>126: ~0.30%</li><li>127: ~0.60%</li><li>128: ~0.80%</li><li>129: ~0.10%</li><li>130: ~0.10%</li><li>133: ~0.20%</li><li>135: ~0.20%</li><li>136: ~0.30%</li><li>137: ~0.20%</li><li>139: ~0.40%</li><li>140: ~0.20%</li><li>143: ~0.30%</li><li>144: ~0.10%</li><li>150: ~0.10%</li><li>151: ~0.50%</li><li>152: ~0.10%</li><li>153: ~0.10%</li><li>154: ~0.60%</li><li>155: ~0.20%</li><li>156: ~0.10%</li><li>157: ~0.10%</li><li>158: ~0.30%</li><li>161: ~0.20%</li><li>162: ~0.30%</li><li>164: ~0.10%</li><li>165: ~0.20%</li><li>168: ~0.10%</li><li>170: ~0.20%</li><li>172: ~0.10%</li><li>174: ~0.20%</li><li>175: ~0.10%</li><li>177: ~0.10%</li><li>178: ~0.10%</li><li>181: ~0.20%</li><li>182: ~0.30%</li><li>183: ~0.20%</li><li>184: ~0.40%</li><li>185: ~0.30%</li><li>186: ~0.20%</li><li>187: ~0.40%</li><li>188: ~0.10%</li><li>189: ~0.10%</li><li>190: ~0.20%</li><li>191: ~0.60%</li><li>192: ~0.10%</li><li>193: ~0.20%</li><li>197: ~0.10%</li><li>199: ~0.10%</li><li>200: ~0.30%</li><li>201: ~0.20%</li><li>202: ~0.10%</li><li>205: ~0.20%</li><li>208: ~0.10%</li><li>209: ~0.10%</li><li>210: ~0.10%</li><li>211: ~0.10%</li><li>212: ~0.10%</li><li>213: ~0.20%</li><li>215: ~0.20%</li><li>216: ~0.30%</li><li>217: ~0.20%</li><li>218: ~0.20%</li><li>219: ~0.10%</li><li>220: ~0.20%</li><li>222: ~0.10%</li><li>223: ~0.10%</li><li>225: ~0.10%</li><li>226: ~0.10%</li><li>227: ~0.20%</li><li>228: ~0.10%</li><li>229: ~0.10%</li><li>230: ~0.30%</li><li>231: ~0.20%</li><li>232: ~0.10%</li><li>233: ~0.10%</li><li>235: ~0.20%</li><li>236: ~0.20%</li><li>237: ~0.20%</li><li>241: ~0.40%</li><li>242: ~0.10%</li><li>243: ~0.30%</li><li>244: ~0.10%</li><li>245: ~0.40%</li><li>246: ~0.10%</li><li>247: ~0.30%</li><li>249: ~0.40%</li><li>253: ~0.20%</li><li>254: ~0.10%</li><li>255: ~0.10%</li><li>256: ~0.20%</li><li>257: ~0.30%</li><li>258: ~0.10%</li><li>259: ~0.10%</li><li>260: ~0.40%</li><li>264: ~0.10%</li><li>265: ~0.10%</li><li>266: ~0.10%</li><li>268: 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~0.20%</li><li>333: ~0.10%</li><li>334: ~0.60%</li><li>335: ~0.30%</li><li>336: ~0.30%</li><li>337: ~0.30%</li><li>339: ~0.10%</li><li>341: ~0.30%</li><li>342: ~0.10%</li><li>343: ~0.10%</li><li>344: ~0.20%</li><li>345: ~0.10%</li><li>346: ~0.20%</li><li>347: ~0.20%</li><li>348: ~0.30%</li><li>349: ~0.40%</li><li>351: ~0.40%</li><li>352: ~0.10%</li><li>353: ~0.10%</li><li>354: ~0.30%</li><li>356: ~0.30%</li><li>357: ~0.10%</li><li>358: ~0.10%</li><li>359: ~0.10%</li><li>360: ~0.40%</li><li>361: ~0.30%</li><li>364: ~0.20%</li><li>366: ~0.50%</li><li>367: ~0.30%</li><li>368: ~0.40%</li><li>369: ~0.30%</li><li>370: ~0.10%</li><li>372: ~0.10%</li><li>373: ~0.30%</li><li>374: ~0.10%</li><li>375: ~0.50%</li><li>376: ~0.10%</li><li>377: ~0.40%</li><li>378: ~0.10%</li><li>379: ~0.40%</li><li>380: ~0.30%</li><li>381: ~0.30%</li><li>382: ~0.10%</li><li>383: ~0.20%</li><li>384: ~0.20%</li><li>385: ~0.40%</li><li>387: ~0.20%</li><li>390: ~0.30%</li><li>391: ~0.10%</li><li>392: ~0.50%</li><li>393: ~0.20%</li><li>394: ~0.10%</li><li>395: ~0.20%</li><li>397: ~0.10%</li><li>398: ~0.10%</li><li>399: ~0.30%</li><li>400: ~0.20%</li><li>401: ~0.10%</li><li>405: ~0.20%</li><li>407: ~0.30%</li><li>408: ~0.10%</li><li>411: ~0.20%</li><li>412: ~0.10%</li><li>413: ~0.10%</li><li>414: ~0.40%</li><li>415: ~0.30%</li><li>416: ~0.10%</li><li>418: ~0.40%</li><li>420: ~0.30%</li><li>421: ~0.40%</li><li>422: ~0.30%</li><li>423: ~0.40%</li><li>425: ~0.50%</li><li>426: ~0.10%</li><li>427: ~0.20%</li><li>428: ~0.20%</li><li>429: ~0.10%</li><li>430: ~0.20%</li><li>432: ~0.10%</li><li>433: ~0.10%</li><li>434: ~0.30%</li><li>435: ~0.20%</li><li>436: ~0.50%</li><li>437: ~0.30%</li><li>438: ~0.40%</li><li>440: ~0.40%</li><li>442: ~0.10%</li><li>444: ~0.40%</li><li>445: ~0.10%</li><li>446: ~0.20%</li><li>447: ~0.10%</li><li>448: ~0.10%</li><li>449: ~0.20%</li><li>450: ~0.20%</li><li>451: ~0.10%</li><li>452: ~0.20%</li><li>453: ~0.30%</li><li>454: ~0.20%</li><li>455: ~0.20%</li><li>456: ~0.20%</li><li>457: ~0.30%</li><li>458: ~0.10%</li><li>459: ~0.30%</li><li>460: ~0.10%</li><li>463: ~0.40%</li><li>464: ~0.20%</li><li>465: ~0.20%</li><li>468: ~0.30%</li><li>469: ~0.10%</li><li>470: ~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: ~0.10%</li><li>483: ~0.40%</li><li>484: ~0.60%</li><li>485: ~0.70%</li><li>487: ~0.30%</li><li>488: ~0.20%</li><li>489: ~0.10%</li><li>491: ~0.60%</li><li>492: ~0.80%</li><li>493: ~0.50%</li><li>498: ~0.20%</li><li>499: ~0.20%</li><li>500: ~0.20%</li><li>501: ~0.20%</li><li>502: ~0.10%</li><li>503: ~0.20%</li><li>504: ~0.10%</li><li>505: ~0.20%</li><li>506: ~0.10%</li><li>508: ~0.30%</li><li>511: ~0.20%</li><li>512: ~0.30%</li><li>514: ~0.10%</li><li>516: ~0.20%</li><li>517: ~0.40%</li><li>519: ~0.90%</li><li>520: ~0.80%</li><li>521: ~1.00%</li><li>522: ~0.10%</li><li>523: ~0.20%</li><li>524: ~0.20%</li><li>525: ~0.30%</li><li>528: ~0.10%</li><li>529: ~0.10%</li><li>531: ~0.10%</li><li>532: ~0.10%</li><li>533: ~0.20%</li><li>535: ~0.10%</li><li>536: ~0.10%</li><li>539: ~0.10%</li><li>540: ~0.20%</li><li>541: ~0.10%</li><li>543: ~0.30%</li><li>544: ~0.20%</li><li>545: ~0.40%</li><li>546: ~0.50%</li><li>547: ~0.20%</li><li>548: ~0.30%</li><li>549: ~0.10%</li><li>550: ~0.20%</li><li>551: ~0.70%</li><li>552: ~0.30%</li><li>554: ~0.30%</li><li>555: ~0.30%</li><li>556: ~0.10%</li><li>558: ~0.10%</li><li>559: ~0.40%</li><li>561: ~0.10%</li><li>562: ~0.10%</li><li>564: ~0.10%</li><li>565: ~0.10%</li><li>566: ~0.20%</li><li>567: ~0.20%</li><li>568: ~0.20%</li><li>570: ~0.20%</li><li>572: ~0.10%</li><li>573: ~0.10%</li><li>574: ~0.10%</li><li>578: ~0.30%</li></ul> |
158
+ * Samples:
159
+ | sentence_0 | label |
160
+ |:---------------------------------------------------------------|:-----------------|
161
+ | <code>)</code> | <code>306</code> |
162
+ | <code> transport of freight over sea and coastal waters</code> | <code>278</code> |
163
+ | <code> </code> | <code>319</code> |
164
+ * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
165
+
166
+ ### Training Hyperparameters
167
+ #### Non-Default Hyperparameters
168
+
169
+ - `per_device_train_batch_size`: 256
170
+ - `per_device_eval_batch_size`: 256
171
+ - `num_train_epochs`: 4
172
+ - `multi_dataset_batch_sampler`: round_robin
173
+
174
+ #### All Hyperparameters
175
+ <details><summary>Click to expand</summary>
176
+
177
+ - `overwrite_output_dir`: False
178
+ - `do_predict`: False
179
+ - `eval_strategy`: no
180
+ - `prediction_loss_only`: True
181
+ - `per_device_train_batch_size`: 256
182
+ - `per_device_eval_batch_size`: 256
183
+ - `per_gpu_train_batch_size`: None
184
+ - `per_gpu_eval_batch_size`: None
185
+ - `gradient_accumulation_steps`: 1
186
+ - `eval_accumulation_steps`: None
187
+ - `learning_rate`: 5e-05
188
+ - `weight_decay`: 0.0
189
+ - `adam_beta1`: 0.9
190
+ - `adam_beta2`: 0.999
191
+ - `adam_epsilon`: 1e-08
192
+ - `max_grad_norm`: 1
193
+ - `num_train_epochs`: 4
194
+ - `max_steps`: -1
195
+ - `lr_scheduler_type`: linear
196
+ - `lr_scheduler_kwargs`: {}
197
+ - `warmup_ratio`: 0.0
198
+ - `warmup_steps`: 0
199
+ - `log_level`: passive
200
+ - `log_level_replica`: warning
201
+ - `log_on_each_node`: True
202
+ - `logging_nan_inf_filter`: True
203
+ - `save_safetensors`: True
204
+ - `save_on_each_node`: False
205
+ - `save_only_model`: False
206
+ - `restore_callback_states_from_checkpoint`: False
207
+ - `no_cuda`: False
208
+ - `use_cpu`: False
209
+ - `use_mps_device`: False
210
+ - `seed`: 42
211
+ - `data_seed`: None
212
+ - `jit_mode_eval`: False
213
+ - `use_ipex`: False
214
+ - `bf16`: False
215
+ - `fp16`: False
216
+ - `fp16_opt_level`: O1
217
+ - `half_precision_backend`: auto
218
+ - `bf16_full_eval`: False
219
+ - `fp16_full_eval`: False
220
+ - `tf32`: None
221
+ - `local_rank`: 0
222
+ - `ddp_backend`: None
223
+ - `tpu_num_cores`: None
224
+ - `tpu_metrics_debug`: False
225
+ - `debug`: []
226
+ - `dataloader_drop_last`: False
227
+ - `dataloader_num_workers`: 0
228
+ - `dataloader_prefetch_factor`: None
229
+ - `past_index`: -1
230
+ - `disable_tqdm`: False
231
+ - `remove_unused_columns`: True
232
+ - `label_names`: None
233
+ - `load_best_model_at_end`: False
234
+ - `ignore_data_skip`: False
235
+ - `fsdp`: []
236
+ - `fsdp_min_num_params`: 0
237
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
238
+ - `fsdp_transformer_layer_cls_to_wrap`: None
239
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
240
+ - `deepspeed`: None
241
+ - `label_smoothing_factor`: 0.0
242
+ - `optim`: adamw_torch
243
+ - `optim_args`: None
244
+ - `adafactor`: False
245
+ - `group_by_length`: False
246
+ - `length_column_name`: length
247
+ - `ddp_find_unused_parameters`: None
248
+ - `ddp_bucket_cap_mb`: None
249
+ - `ddp_broadcast_buffers`: False
250
+ - `dataloader_pin_memory`: True
251
+ - `dataloader_persistent_workers`: False
252
+ - `skip_memory_metrics`: True
253
+ - `use_legacy_prediction_loop`: False
254
+ - `push_to_hub`: False
255
+ - `resume_from_checkpoint`: None
256
+ - `hub_model_id`: None
257
+ - `hub_strategy`: every_save
258
+ - `hub_private_repo`: False
259
+ - `hub_always_push`: False
260
+ - `gradient_checkpointing`: False
261
+ - `gradient_checkpointing_kwargs`: None
262
+ - `include_inputs_for_metrics`: False
263
+ - `eval_do_concat_batches`: True
264
+ - `fp16_backend`: auto
265
+ - `push_to_hub_model_id`: None
266
+ - `push_to_hub_organization`: None
267
+ - `mp_parameters`:
268
+ - `auto_find_batch_size`: False
269
+ - `full_determinism`: False
270
+ - `torchdynamo`: None
271
+ - `ray_scope`: last
272
+ - `ddp_timeout`: 1800
273
+ - `torch_compile`: False
274
+ - `torch_compile_backend`: None
275
+ - `torch_compile_mode`: None
276
+ - `dispatch_batches`: None
277
+ - `split_batches`: None
278
+ - `include_tokens_per_second`: False
279
+ - `include_num_input_tokens_seen`: False
280
+ - `neftune_noise_alpha`: None
281
+ - `optim_target_modules`: None
282
+ - `batch_eval_metrics`: False
283
+ - `eval_on_start`: False
284
+ - `batch_sampler`: batch_sampler
285
+ - `multi_dataset_batch_sampler`: round_robin
286
+
287
+ </details>
288
+
289
+ ### Framework Versions
290
+ - Python: 3.10.12
291
+ - Sentence Transformers: 3.0.1
292
+ - Transformers: 4.42.4
293
+ - PyTorch: 2.4.0+cu121
294
+ - Accelerate: 0.32.1
295
+ - Datasets: 2.21.0
296
+ - Tokenizers: 0.19.1
297
+
298
+ ## Citation
299
+
300
+ ### BibTeX
301
+
302
+ #### Sentence Transformers
303
+ ```bibtex
304
+ @inproceedings{reimers-2019-sentence-bert,
305
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
306
+ author = "Reimers, Nils and Gurevych, Iryna",
307
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
308
+ month = "11",
309
+ year = "2019",
310
+ publisher = "Association for Computational Linguistics",
311
+ url = "https://arxiv.org/abs/1908.10084",
312
+ }
313
+ ```
314
+
315
+ #### BatchAllTripletLoss
316
+ ```bibtex
317
+ @misc{hermans2017defense,
318
+ title={In Defense of the Triplet Loss for Person Re-Identification},
319
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
320
+ year={2017},
321
+ eprint={1703.07737},
322
+ archivePrefix={arXiv},
323
+ primaryClass={cs.CV}
324
+ }
325
+ ```
326
+
327
+ <!--
328
+ ## Glossary
329
+
330
+ *Clearly define terms in order to be accessible across audiences.*
331
+ -->
332
+
333
+ <!--
334
+ ## Model Card Authors
335
+
336
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
337
+ -->
338
+
339
+ <!--
340
+ ## Model Card Contact
341
+
342
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
343
+ -->
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