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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:152913 |
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- loss:BatchAllTripletLoss |
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base_model: Almawave/Velvet-2B |
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widget: |
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- source_sentence: La crisi non tocca il mercato del “luxury food”, che continua a |
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crescere |
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sentences: |
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- Avviso aggiornamento (1.55) |
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- Il fotoritocco sui social per fingere di essere al Mic |
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- Turchia, Biberovic giocherà da passaportato nella finestra FIBA |
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- source_sentence: 'Miller stagione finita, Pagani: «Rimpiazzo? Valutiamo il mercato |
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europeo»' |
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sentences: |
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- Beautiful, le trame della settimana dal 2 al 7 dicembre |
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- 'Corte dei conti: concorso per 8 funzionari a tempo indeterminato' |
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- 'Leonardo, AD: incontrato stamani numero uno Airbus su alleanza satellitare' |
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- source_sentence: 'Il segreto di Jalen Hurts in una foto sullo smartphone: così ha |
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vinto e sconfitto gli scettici' |
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sentences: |
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- Scarcerato il boss Ernesto Fazzalari, era il latitante più ricercato dopo Messina |
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Denaro |
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- 'UniVdA: ecco il nuovo master in Psicologia dello sport' |
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- 'Gran Turismo 7: arrivano quattro nuove auto con l’update 1.55 [VIDEO]' |
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- source_sentence: 'San Vito al Torre, recuperato un antico monumento funerario romano |
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dal fiume: la scoperta' |
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sentences: |
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- Charlotte Casiraghi in pubblico dopo le voci di divorzio |
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- Mr. Bezos, la sua non è imparzialità ma viltà |
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- 'Elisa Di Francisca a La Talpa: “Sono troppo vera per tenere segreti”' |
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- source_sentence: 'NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto: |
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l''idea è correre' |
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sentences: |
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- 'Picasso a Milano: al Mudec la mostra sulle metamorfosi del maestro' |
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- Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria |
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- 'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in |
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un centro penale' |
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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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- average_precision |
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- f1 |
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- precision |
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- recall |
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- threshold |
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model-index: |
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- name: SentenceTransformer based on Almawave/Velvet-2B |
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results: |
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- task: |
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type: paraphrase-mining |
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name: Paraphrase Mining |
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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: average_precision |
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value: 0.5283532795784699 |
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name: Average Precision |
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- type: f1 |
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value: 0.5502357974952371 |
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name: F1 |
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- type: precision |
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value: 0.5567564151181899 |
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name: Precision |
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- type: recall |
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value: 0.5438661480521084 |
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name: Recall |
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- type: threshold |
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value: 0.9310455322265625 |
|
name: Threshold |
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--- |
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|
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# SentenceTransformer based on Almawave/Velvet-2B |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B). It maps sentences & paragraphs to a 2048-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:** [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B) <!-- at revision 3b864694ae4d80923ac39cee130c5eeb7d8808b6 --> |
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- **Maximum Sequence Length:** 32768 tokens |
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- **Output Dimensionality:** 2048 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': 32768, 'do_lower_case': False}) with Transformer model: MistralModel |
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(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, '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("ancc/Velvet-2B-embedding-news") |
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# Run inference |
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sentences = [ |
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"NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto: l'idea è correre", |
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'Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria', |
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'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in un centro penale', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 2048] |
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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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#### Paraphrase Mining |
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* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) |
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| Metric | Value | |
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|:----------------------|:-----------| |
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| **average_precision** | **0.5284** | |
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| f1 | 0.5502 | |
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| precision | 0.5568 | |
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| recall | 0.5439 | |
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| threshold | 0.931 | |
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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: 152,913 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| type | string | int | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 19.14 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.20%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.20%</li><li>9: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.20%</li><li>17: ~0.30%</li><li>18: ~0.50%</li><li>19: ~0.40%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.30%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.40%</li><li>29: ~0.50%</li><li>30: ~0.20%</li><li>31: ~0.30%</li><li>32: ~0.40%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>36: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.30%</li><li>41: ~0.20%</li><li>43: ~0.50%</li><li>44: ~0.50%</li><li>45: ~0.50%</li><li>46: ~0.40%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.50%</li><li>50: ~0.30%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.20%</li><li>56: ~0.50%</li><li>57: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.40%</li><li>62: ~0.50%</li><li>63: ~0.20%</li><li>64: ~0.50%</li><li>65: ~0.40%</li><li>66: ~0.50%</li><li>67: ~0.40%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.20%</li><li>72: ~0.50%</li><li>73: ~0.20%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.30%</li><li>79: ~0.50%</li><li>80: ~0.40%</li><li>81: ~0.30%</li><li>82: ~0.20%</li><li>83: ~0.40%</li><li>84: ~0.30%</li><li>85: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.20%</li><li>90: ~0.50%</li><li>91: ~0.20%</li><li>92: ~0.50%</li><li>93: ~0.40%</li><li>94: ~0.50%</li><li>95: ~0.50%</li><li>97: ~0.50%</li><li>98: ~0.40%</li><li>99: ~0.50%</li><li>100: ~0.30%</li><li>101: ~0.50%</li><li>103: ~0.20%</li><li>104: ~0.50%</li><li>106: ~0.40%</li><li>107: ~0.20%</li><li>108: ~0.40%</li><li>109: ~0.30%</li><li>110: ~0.50%</li><li>111: ~0.40%</li><li>112: ~0.50%</li><li>113: ~0.30%</li><li>115: ~0.30%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.50%</li><li>122: ~0.20%</li><li>123: ~0.40%</li><li>124: ~0.30%</li><li>125: ~0.40%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.40%</li><li>133: ~0.50%</li><li>134: ~0.30%</li><li>135: ~0.50%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.20%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.30%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.50%</li><li>148: ~0.30%</li><li>149: ~0.20%</li><li>150: ~0.50%</li><li>151: ~0.30%</li><li>152: ~0.20%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.40%</li><li>156: ~0.20%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.40%</li><li>162: ~0.20%</li><li>163: ~0.40%</li><li>164: ~0.20%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.20%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.20%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.40%</li><li>175: ~0.50%</li><li>176: ~0.30%</li><li>178: ~0.50%</li><li>180: ~0.20%</li><li>182: ~0.50%</li><li>183: ~0.50%</li><li>184: ~0.30%</li><li>185: ~0.50%</li><li>187: ~0.20%</li><li>188: ~0.50%</li><li>189: ~0.50%</li><li>191: ~0.50%</li><li>192: ~0.50%</li><li>193: ~0.50%</li><li>194: ~0.20%</li><li>195: ~0.30%</li><li>196: ~0.50%</li><li>197: ~0.50%</li><li>199: ~0.50%</li><li>200: ~0.50%</li><li>201: ~0.20%</li><li>202: ~0.50%</li><li>203: ~0.50%</li><li>204: ~0.30%</li><li>205: ~0.50%</li><li>206: ~0.20%</li><li>207: ~0.40%</li><li>208: ~0.50%</li><li>209: ~0.30%</li><li>210: ~0.30%</li><li>211: ~0.50%</li><li>212: ~0.20%</li><li>213: ~0.50%</li><li>214: ~0.50%</li><li>215: ~0.40%</li><li>216: ~0.50%</li><li>217: ~0.40%</li><li>218: ~0.50%</li><li>219: ~0.50%</li><li>220: ~0.20%</li><li>221: ~0.50%</li><li>222: ~0.40%</li><li>223: ~0.50%</li><li>224: ~0.30%</li><li>225: ~0.40%</li><li>227: ~0.20%</li><li>228: ~0.30%</li><li>230: ~0.40%</li><li>231: ~0.40%</li><li>232: ~0.50%</li><li>233: ~0.50%</li><li>234: ~0.50%</li><li>235: ~0.50%</li><li>236: ~0.40%</li><li>237: ~0.50%</li><li>238: ~0.40%</li><li>239: ~0.50%</li><li>240: ~0.50%</li><li>241: ~0.50%</li><li>242: ~0.30%</li><li>243: ~0.50%</li><li>244: ~0.50%</li><li>245: ~0.50%</li><li>246: ~0.50%</li><li>247: ~0.20%</li><li>248: ~0.50%</li><li>249: ~0.50%</li><li>250: ~0.50%</li><li>251: ~0.50%</li><li>252: ~0.20%</li><li>253: ~0.50%</li><li>254: ~0.50%</li><li>255: ~0.30%</li><li>256: ~0.50%</li><li>257: ~0.50%</li><li>258: ~0.20%</li><li>259: ~0.50%</li><li>260: ~0.30%</li><li>261: ~0.50%</li><li>262: ~0.50%</li><li>263: ~0.20%</li><li>264: ~0.50%</li><li>265: ~0.20%</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:-------------------------------------------------------------------------------------------|:---------------| |
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| <code>MERCATO LBA - Treviso, Giofrè: "Mercato in continua osservazione, vedremo..."</code> | <code>0</code> | |
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| <code>Ky Bowman: Non sono soddisfatto delle mie performance</code> | <code>0</code> | |
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| <code>LBA - Treviso, Giofrè: "Sabato la Reggiana, dobbiamo vincere. Punto"</code> | <code>0</code> | |
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* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
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|
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### Evaluation Dataset |
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|
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#### Unnamed Dataset |
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* Size: 9,310 evaluation samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| type | string | int | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 19.04 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.30%</li><li>2: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.20%</li><li>6: ~0.30%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>12: ~0.40%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.40%</li><li>16: ~0.20%</li><li>17: ~0.50%</li><li>18: ~0.40%</li><li>19: ~0.40%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.20%</li><li>32: ~0.30%</li><li>33: ~0.50%</li><li>34: ~0.20%</li><li>35: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.20%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.20%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.50%</li><li>45: ~0.30%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.30%</li><li>49: ~0.40%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.50%</li><li>53: ~0.20%</li><li>54: ~0.30%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.20%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.20%</li><li>65: ~0.50%</li><li>67: ~0.40%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.20%</li><li>71: ~0.40%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.30%</li><li>77: ~0.20%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.20%</li><li>81: ~0.50%</li><li>82: ~0.20%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>89: ~0.20%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.30%</li><li>93: ~0.40%</li><li>94: ~0.50%</li><li>95: ~0.30%</li><li>96: ~0.20%</li><li>97: ~0.50%</li><li>98: ~0.20%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.50%</li><li>102: ~0.50%</li><li>103: ~0.30%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~0.50%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: 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~0.50%</li><li>276: ~0.40%</li><li>277: ~0.30%</li><li>278: ~0.30%</li></ul> | |
|
* Samples: |
|
| sentence | label | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
|
| <code>Supplenze: come funzionano i contratti fino al 31 dicembre 2024 e il calcolo del punteggio?</code> | <code>0</code> | |
|
| <code>Docente non abilitato assunto a tempo determinato da concorso PNRR1: in quale scuola "andrò a finire" se nella mia si perde un posto?</code> | <code>0</code> | |
|
| <code>Docenti non abilitati nominati dopo il 31 agosto da graduatorie pubblicate prima: otterranno sede di titolarità all’esito delle operazioni di mobilità [Chiarimenti]</code> | <code>0</code> | |
|
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `per_device_train_batch_size`: 32 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.25 |
|
- `seed`: 17 |
|
- `data_seed`: 17 |
|
- `bf16`: True |
|
- `batch_sampler`: group_by_label |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: no |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 8 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 5e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.25 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 17 |
|
- `data_seed`: 17 |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: group_by_label |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | Validation Loss | average_precision | |
|
|:------:|:-----:|:-------------:|:---------------:|:-----------------:| |
|
| 0.0002 | 1 | 0.0521 | - | - | |
|
| 0.8002 | 3824 | 0.0195 | - | - | |
|
| 1.7732 | 7648 | 0.0071 | - | - | |
|
| 2.7462 | 11472 | 0.0052 | - | - | |
|
| 3.7192 | 15296 | 0.0046 | - | - | |
|
| 3.8272 | 15812 | - | 0.0011 | 0.5284 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.12.8 |
|
- Sentence Transformers: 3.4.1 |
|
- Transformers: 4.48.3 |
|
- PyTorch: 2.6.0+cu124 |
|
- Accelerate: 1.3.0 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### BatchAllTripletLoss |
|
```bibtex |
|
@misc{hermans2017defense, |
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
year={2017}, |
|
eprint={1703.07737}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
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