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---
base_model: intfloat/multilingual-e5-small
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1030
- loss:ContrastiveLoss
widget:
- source_sentence: First climber to reach the summit of Everest
sentences:
- How to create a podcast?
- How to cook sushi rice?
- Who was the first person to climb Mount Everest?
- source_sentence: What methods are used to measure a nation's GDP?
sentences:
- How is the GDP of a country measured?
- How do I sign out of my email account?
- How does digital marketing differ from traditional marketing?
- source_sentence: Steps to sign up for a new account
sentences:
- How to grow tomatoes in a garden?
- What is the process for creating a new account?
- What is the GDP of India?
- source_sentence: Name of the tallest building in New York
sentences:
- What are the symptoms of anxiety?
- What is the tallest building in New York?
- Who was the first female Prime Minister of the UK?
- source_sentence: How do you make a paper boat?
sentences:
- What are the benefits of using solar energy?
- Where can I buy a new phone?
- How do you make a paper airplane?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-small
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class dev
type: pair-class-dev
metrics:
- type: cosine_accuracy
value: 0.9478260869565217
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6633322238922119
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9558823529411764
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6633322238922119
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9154929577464789
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 0.9777355464218691
name: Cosine Ap
- type: dot_accuracy
value: 0.9478260869565217
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.6633322238922119
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9558823529411764
name: Dot F1
- type: dot_f1_threshold
value: 0.6633322238922119
name: Dot F1 Threshold
- type: dot_precision
value: 0.9154929577464789
name: Dot Precision
- type: dot_recall
value: 1.0
name: Dot Recall
- type: dot_ap
value: 0.9777355464218691
name: Dot Ap
- type: manhattan_accuracy
value: 0.9391304347826087
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 9.603110313415527
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9489051094890512
name: Manhattan F1
- type: manhattan_f1_threshold
value: 12.660685539245605
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9027777777777778
name: Manhattan Precision
- type: manhattan_recall
value: 1.0
name: Manhattan Recall
- type: manhattan_ap
value: 0.975614621691024
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9478260869565217
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.8205450773239136
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9558823529411764
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.8205450773239136
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.9154929577464789
name: Euclidean Precision
- type: euclidean_recall
value: 1.0
name: Euclidean Recall
- type: euclidean_ap
value: 0.9777355464218691
name: Euclidean Ap
- type: max_accuracy
value: 0.9478260869565217
name: Max Accuracy
- type: max_accuracy_threshold
value: 9.603110313415527
name: Max Accuracy Threshold
- type: max_f1
value: 0.9558823529411764
name: Max F1
- type: max_f1_threshold
value: 12.660685539245605
name: Max F1 Threshold
- type: max_precision
value: 0.9154929577464789
name: Max Precision
- type: max_recall
value: 1.0
name: Max Recall
- type: max_ap
value: 0.9777355464218691
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class test
type: pair-class-test
metrics:
- type: cosine_accuracy
value: 0.9478260869565217
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7873066663742065
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9558823529411764
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6542514562606812
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9154929577464789
name: Cosine Precision
- type: cosine_recall
value: 1.0
name: Cosine Recall
- type: cosine_ap
value: 0.9776721343444097
name: Cosine Ap
- type: dot_accuracy
value: 0.9478260869565217
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.7873067259788513
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9558823529411764
name: Dot F1
- type: dot_f1_threshold
value: 0.6542515158653259
name: Dot F1 Threshold
- type: dot_precision
value: 0.9154929577464789
name: Dot Precision
- type: dot_recall
value: 1.0
name: Dot Recall
- type: dot_ap
value: 0.9776721343444097
name: Dot Ap
- type: manhattan_accuracy
value: 0.9478260869565217
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 11.123205184936523
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9558823529411764
name: Manhattan F1
- type: manhattan_f1_threshold
value: 12.862250328063965
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.9154929577464789
name: Manhattan Precision
- type: manhattan_recall
value: 1.0
name: Manhattan Recall
- type: manhattan_ap
value: 0.9774497925836063
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9478260869565217
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.652188777923584
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9558823529411764
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.8315430879592896
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.9154929577464789
name: Euclidean Precision
- type: euclidean_recall
value: 1.0
name: Euclidean Recall
- type: euclidean_ap
value: 0.9776721343444097
name: Euclidean Ap
- type: max_accuracy
value: 0.9478260869565217
name: Max Accuracy
- type: max_accuracy_threshold
value: 11.123205184936523
name: Max Accuracy Threshold
- type: max_f1
value: 0.9558823529411764
name: Max F1
- type: max_f1_threshold
value: 12.862250328063965
name: Max F1 Threshold
- type: max_precision
value: 0.9154929577464789
name: Max Precision
- type: max_recall
value: 1.0
name: Max Recall
- type: max_ap
value: 0.9776721343444097
name: Max Ap
---
# SentenceTransformer based on intfloat/multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_2")
# Run inference
sentences = [
'How do you make a paper boat?',
'How do you make a paper airplane?',
'What are the benefits of using solar energy?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `pair-class-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9478 |
| cosine_accuracy_threshold | 0.6633 |
| cosine_f1 | 0.9559 |
| cosine_f1_threshold | 0.6633 |
| cosine_precision | 0.9155 |
| cosine_recall | 1.0 |
| cosine_ap | 0.9777 |
| dot_accuracy | 0.9478 |
| dot_accuracy_threshold | 0.6633 |
| dot_f1 | 0.9559 |
| dot_f1_threshold | 0.6633 |
| dot_precision | 0.9155 |
| dot_recall | 1.0 |
| dot_ap | 0.9777 |
| manhattan_accuracy | 0.9391 |
| manhattan_accuracy_threshold | 9.6031 |
| manhattan_f1 | 0.9489 |
| manhattan_f1_threshold | 12.6607 |
| manhattan_precision | 0.9028 |
| manhattan_recall | 1.0 |
| manhattan_ap | 0.9756 |
| euclidean_accuracy | 0.9478 |
| euclidean_accuracy_threshold | 0.8205 |
| euclidean_f1 | 0.9559 |
| euclidean_f1_threshold | 0.8205 |
| euclidean_precision | 0.9155 |
| euclidean_recall | 1.0 |
| euclidean_ap | 0.9777 |
| max_accuracy | 0.9478 |
| max_accuracy_threshold | 9.6031 |
| max_f1 | 0.9559 |
| max_f1_threshold | 12.6607 |
| max_precision | 0.9155 |
| max_recall | 1.0 |
| **max_ap** | **0.9777** |
#### Binary Classification
* Dataset: `pair-class-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9478 |
| cosine_accuracy_threshold | 0.7873 |
| cosine_f1 | 0.9559 |
| cosine_f1_threshold | 0.6543 |
| cosine_precision | 0.9155 |
| cosine_recall | 1.0 |
| cosine_ap | 0.9777 |
| dot_accuracy | 0.9478 |
| dot_accuracy_threshold | 0.7873 |
| dot_f1 | 0.9559 |
| dot_f1_threshold | 0.6543 |
| dot_precision | 0.9155 |
| dot_recall | 1.0 |
| dot_ap | 0.9777 |
| manhattan_accuracy | 0.9478 |
| manhattan_accuracy_threshold | 11.1232 |
| manhattan_f1 | 0.9559 |
| manhattan_f1_threshold | 12.8623 |
| manhattan_precision | 0.9155 |
| manhattan_recall | 1.0 |
| manhattan_ap | 0.9774 |
| euclidean_accuracy | 0.9478 |
| euclidean_accuracy_threshold | 0.6522 |
| euclidean_f1 | 0.9559 |
| euclidean_f1_threshold | 0.8315 |
| euclidean_precision | 0.9155 |
| euclidean_recall | 1.0 |
| euclidean_ap | 0.9777 |
| max_accuracy | 0.9478 |
| max_accuracy_threshold | 11.1232 |
| max_f1 | 0.9559 |
| max_f1_threshold | 12.8623 |
| max_precision | 0.9155 |
| max_recall | 1.0 |
| **max_ap** | **0.9777** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,030 training samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence2 | sentence1 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~49.60%</li><li>1: ~50.40%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.27 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.9 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| label | sentence2 | sentence1 |
|:---------------|:------------------------------------------------------|:-----------------------------------------------------------------------|
| <code>1</code> | <code>Speed of sound in air</code> | <code>What is the speed of sound?</code> |
| <code>1</code> | <code>World's most popular tourist destination</code> | <code>What is the most visited tourist attraction in the world?</code> |
| <code>1</code> | <code>How do I write a resume?</code> | <code>How do I create a resume?</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.6,
"size_average": true
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 115 evaluation samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | label | sentence2 | sentence1 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~43.48%</li><li>1: ~56.52%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.04 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
| label | sentence2 | sentence1 |
|:---------------|:--------------------------------------------------------------|:---------------------------------------------------|
| <code>0</code> | <code>What methods are used to measure a nation's GDP?</code> | <code>How is the GDP of a country measured?</code> |
| <code>0</code> | <code>What is the currency of Japan?</code> | <code>What is the currency of China?</code> |
| <code>1</code> | <code>Steps to cultivate tomatoes at home</code> | <code>How to grow tomatoes in a garden?</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.6,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `weight_decay`: 0.01
- `num_train_epochs`: 8
- `lr_scheduler_type`: reduce_lr_on_plateau
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: reduce_lr_on_plateau
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `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`: True
- `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_fused
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0 | 0 | - | - | 0.7625 | - |
| 0.6061 | 10 | 0.0417 | - | - | - |
| 0.9697 | 16 | - | 0.0119 | 0.9695 | - |
| 1.2121 | 20 | 0.0189 | - | - | - |
| 1.8182 | 30 | 0.0148 | - | - | - |
| 2.0 | 33 | - | 0.0102 | 0.9741 | - |
| 2.4242 | 40 | 0.0114 | - | - | - |
| 2.9697 | 49 | - | 0.0098 | 0.9752 | - |
| 3.0303 | 50 | 0.009 | - | - | - |
| 3.6364 | 60 | 0.008 | - | - | - |
| 4.0 | 66 | - | 0.0095 | 0.9778 | - |
| 4.2424 | 70 | 0.0065 | - | - | - |
| 4.8485 | 80 | 0.0056 | - | - | - |
| 4.9697 | 82 | - | 0.0092 | 0.9749 | - |
| 5.4545 | 90 | 0.0056 | - | - | - |
| 6.0 | 99 | - | 0.0088 | 0.9766 | - |
| 6.0606 | 100 | 0.0045 | - | - | - |
| 6.6667 | 110 | 0.0044 | - | - | - |
| **6.9697** | **115** | **-** | **0.0087** | **0.9777** | **-** |
| 7.2727 | 120 | 0.0038 | - | - | - |
| 7.7576 | 128 | - | 0.0090 | 0.9777 | 0.9777 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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