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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:2240
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- loss:TripletLoss
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- loss:CosineSimilarityLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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widget:
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- source_sentence: The groundwater residence time in the Chashma-Mianwali area was
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estimated to be in the range of _____ years based on Tracer Lump Parameter Model
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(LPM) and Chlorofluorocarbons (CFCs) data.
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sentences:
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- 14–59
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- point scale; radar data
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- dissolved oxygen
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- source_sentence: The Grid-to-Grid hydrological model (G2G) was primarily developed
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to simulate river flows at a national scale but can also output simulated depth-integrated
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soil moisture on a _____ grid.
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sentences:
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- 89%; 11%
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- 1 km
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- catchment
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- source_sentence: The prime driver of flood magnitude variation in the Yellow River
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Basin was identified as the consecutive __________ total precipitation.
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sentences:
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- 144-day
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- '48'
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- hydrologic engineering design and operation
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- source_sentence: Inter-basin water transfers (IBWTs) can potentially save water
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through changes in ________ efficiency.
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sentences:
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- spatially-constrained ordinary kriging (SC-OK)
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- water use
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- 2.10–3.6 °C
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- source_sentence: Flood statistics often face high uncertainty in quantile estimates
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due to __________ observation periods.
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sentences:
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- short
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- convective
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- abstraction
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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- **Maximum Sequence Length:** 256 tokens
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- **Output Dimensionality:** 384 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': 256, '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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(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("HydroEmbed/HydroEmbed-FITB-MiniLM-DualLoss")
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# Run inference
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sentences = [
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'Flood statistics often face high uncertainty in quantile estimates due to __________ observation periods.',
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'short',
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'abstraction',
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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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# 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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<!--
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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 Datasets
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#### Unnamed Dataset
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* Size: 1,120 training samples
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence_0 | sentence_1 | sentence_2 |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
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| type | string | string | string |
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| details | <ul><li>min: 16 tokens</li><li>mean: 36.69 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.61 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.58 tokens</li><li>max: 47 tokens</li></ul> |
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* Samples:
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| sentence_0 | sentence_1 | sentence_2 |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|:-----------------------|
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| <code>Mixed physical barriers (MPB), which combine a cutoff wall and a subsurface dam, are utilized to prevent __________ in coastal aquifers.</code> | <code>seawater intrusion (SWI)</code> | <code>air</code> |
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| <code>The use of __________ in hydrology has been enhanced by graph neural networks to capture the spatial dependence of precipitation infiltration and groundwater propagation.</code> | <code>graph neural networks (GNN)</code> | <code>recharge</code> |
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| <code>The analysis of flood frequency in North Africa revealed that the trend analysis results indicated no significant changes in flood frequency or magnitude at the regional level, with only a few spurious trends due to isolated extreme or clustered events. This was based on a new database of daily river discharge data compiled for how many river basins?</code> | <code>98 river basins</code> | <code>Hjulstrom</code> |
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* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
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```json
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{
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"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
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"triplet_margin": 5
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}
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```
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#### Unnamed Dataset
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* Size: 1,120 training samples
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence_0 | sentence_1 | label |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 16 tokens</li><li>mean: 36.91 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.66 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence_0 | sentence_1 | label |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------|:-----------------|
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| <code>The main objective of employing machine learning techniques in flood susceptibility assessment is to analyze the relationship between various __________ and historical flood events.</code> | <code>predictors</code> | <code>1.0</code> |
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| <code>Soil salinization is primarily influenced by climatic factors such as annual precipitation and maximum temperature, as well as topographical features like __________ and __________.</code> | <code>digital elevation model; slope</code> | <code>1.0</code> |
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| <code>The __________ coefficient is a critical parameter used to represent hydraulic roughness in numerical hydrodynamic models.</code> | <code>Strickler</code> | <code>1.0</code> |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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```json
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{
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"loss_fct": "torch.nn.modules.loss.MSELoss"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 32
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- `per_device_eval_batch_size`: 32
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- `num_train_epochs`: 15
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- `fp16`: True
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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: no
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 32
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- `per_device_eval_batch_size`: 32
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 5e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1
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- `num_train_epochs`: 15
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.0
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: True
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `tp_size`: 0
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: None
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `include_for_metrics`: []
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `use_liger_kernel`: False
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: None
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- `batch_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: round_robin
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</details>
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### Training Logs
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| Epoch | Step | Training Loss |
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|:-------:|:----:|:-------------:|
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| 7.1429 | 500 | 2.4211 |
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| 14.2857 | 1000 | 2.2952 |
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### Framework Versions
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- Python: 3.11.1
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- Sentence Transformers: 4.1.0
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- Transformers: 4.51.3
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- PyTorch: 2.7.0+cu118
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- Accelerate: 1.6.0
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- Datasets: 3.5.1
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- Tokenizers: 0.21.1
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### TripletLoss
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```bibtex
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@misc{hermans2017defense,
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title={In Defense of the Triplet Loss for Person Re-Identification},
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
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year={2017},
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eprint={1703.07737},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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