---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:21470
- loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-small
widget:
- source_sentence: This positive resistance model is a different way of analyzing
    feedback oscillator operation.
  sentences:
  - This positive resistance model is a different way of analyzing feedback oscillator
    operation.
  - This negative resistance model is an alternate way of analyzing feedback oscillator
    operation.
  - I am BE 8th sem. CSE student. Which path should I choose as a career or which
    course I should do to get a good job in future within my country?
- source_sentence: Danny Danny Kortchmar played guitar , Charles Larkey played bass
    and Gordon played drums producing with Lou Adler .
  sentences:
  - What is the main reason for all the problems within India?
  - Gordon played guitar , Danny Kortchmar played bass and Lou Adler played drums
    with Charles Larkey producing .
  - Danny Danny Kortchmar played guitar , Charles Larkey played bass and Gordon played
    drums producing with Lou Adler .
- source_sentence: The Ngage isn't still lacking in earbuds.
  sentences:
  - What is Queen's University's acceptance rate for international students on campus?
  - The Ngage is still lacking in earbuds.
  - The Ngage isn't still lacking in earbuds.
- source_sentence: Previously reported figures were consistently revised down.
  sentences:
  - Previously reported figures were consistently revised down.
  - What are the side effects for using Proactiv on the face? How are the side effects
    treated?
  - Previously reported numbers were infrequently revised down.
- source_sentence: What is the fastest way to get a PAN card within India?
  sentences:
  - He has also used the OpenMusic software (designed at IRCAM ) to create computer-generated
    music.
  - What is the fastest way to get a PAN card outside India?
  - What is the fastest way to get a PAN card within India?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on thenlper/gte-small
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.28
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.58
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15999999999999998
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10400000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.057999999999999996
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.28
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.48
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.52
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.58
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4281391945817123
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3795238095238095
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.39018847344323304
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.32
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.66
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.74
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.32
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.132
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07400000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.55
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.61
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.68
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5108521344166539
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4791904761904762
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.452598225251627
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.30000000000000004
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.54
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5900000000000001
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6599999999999999
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.30000000000000004
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.066
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29000000000000004
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.515
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.565
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.63
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4694956644991831
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4293571428571429
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.42139334934743
      name: Cosine Map@100
---

# SentenceTransformer based on thenlper/gte-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-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:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **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/huggingface/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': 128, 'do_lower_case': False, 'architecture': '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("redis/unified-negatives")
# Run inference
sentences = [
    'What is the fastest way to get a PAN card within India?',
    'What is the fastest way to get a PAN card within India?',
    'What is the fastest way to get a PAN card outside India?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.2943],
#         [1.0000, 1.0000, 0.2943],
#         [0.2943, 0.2943, 1.0000]])
```

<!--
### 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.*
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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `NanoMSMARCO` and `NanoNQ`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | NanoMSMARCO | NanoNQ     |
|:--------------------|:------------|:-----------|
| cosine_accuracy@1   | 0.28        | 0.32       |
| cosine_accuracy@3   | 0.48        | 0.6        |
| cosine_accuracy@5   | 0.52        | 0.66       |
| cosine_accuracy@10  | 0.58        | 0.74       |
| cosine_precision@1  | 0.28        | 0.32       |
| cosine_precision@3  | 0.16        | 0.2        |
| cosine_precision@5  | 0.104       | 0.132      |
| cosine_precision@10 | 0.058       | 0.074      |
| cosine_recall@1     | 0.28        | 0.3        |
| cosine_recall@3     | 0.48        | 0.55       |
| cosine_recall@5     | 0.52        | 0.61       |
| cosine_recall@10    | 0.58        | 0.68       |
| **cosine_ndcg@10**  | **0.4281**  | **0.5109** |
| cosine_mrr@10       | 0.3795      | 0.4792     |
| cosine_map@100      | 0.3902      | 0.4526     |

#### Nano BEIR

* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
  ```json
  {
      "dataset_names": [
          "msmarco",
          "nq"
      ],
      "dataset_id": "lightonai/NanoBEIR-en"
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.3        |
| cosine_accuracy@3   | 0.54       |
| cosine_accuracy@5   | 0.59       |
| cosine_accuracy@10  | 0.66       |
| cosine_precision@1  | 0.3        |
| cosine_precision@3  | 0.18       |
| cosine_precision@5  | 0.118      |
| cosine_precision@10 | 0.066      |
| cosine_recall@1     | 0.29       |
| cosine_recall@3     | 0.515      |
| cosine_recall@5     | 0.565      |
| cosine_recall@10    | 0.63       |
| **cosine_ndcg@10**  | **0.4695** |
| cosine_mrr@10       | 0.4294     |
| cosine_map@100      | 0.4214     |

<!--
## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 21,470 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.91 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.91 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.91 tokens</li><li>max: 101 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                     | positive                                                                                                                   | negative                                                                                                                           |
  |:---------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
  | <code>The pale coloration provides camouflage for the beetle on the light sand.</code>                                     | <code>The pale coloration provides camouflage for the beetle on the light sand.</code>                                     | <code>The pale coloration helps the beetle stand out on the light sand.</code>                                                     |
  | <code>It is found from Fennoscandinavia to the Pyrenees , Italy and Greece and from Britain to Russia and Ukraine .</code> | <code>It is found from Fennoscandinavia to the Pyrenees , Italy and Greece and from Britain to Russia and Ukraine .</code> | <code>It is located from Fennoscandinavia to the Pyrenees , Great Britain and Greece and from Italy to Russia and Ukraine .</code> |
  | <code>Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated in Chicago?</code>               | <code>Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated in Chicago?</code>               | <code>Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated outside Chicago?</code>                  |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 7.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 2,386 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 19.42 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.42 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.41 tokens</li><li>max: 74 tokens</li></ul> |
* Samples:
  | anchor                                                                                                         | positive                                                                                                       | negative                                                                                                       |
  |:---------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
  | <code>He died at Fort Edward on August 18 , 1861 , and was buried at the Union Cemetery in Sandy Hill .</code> | <code>He died at Fort Edward on August 18 , 1861 , and was buried at the Union Cemetery in Sandy Hill .</code> | <code>He died at Sandy Hill on August 18 , 1861 , and was buried at the Union Cemetery in Fort Edward .</code> |
  | <code>It was this cooperation which led to the development of the satellite AIS system.</code>                 | <code>It was this cooperation which led to the development of the satellite AIS system.</code>                 | <code>It was this cooperation which led to the halting of development of the satellite AIS system.</code>      |
  | <code>What is the best field of engineering on campus?</code>                                                  | <code>What is the best field of engineering on campus?</code>                                                  | <code>What is the best field of engineering off campus?</code>                                                 |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 7.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 1e-06
- `weight_decay`: 0.001
- `max_steps`: 3000
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/unified-negatives
- `eval_on_start`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: 1e-06
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 3000
- `lr_scheduler_type`: linear
- `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
- `bf16`: False
- `fp16`: True
- `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`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: redis/unified-negatives
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch   | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:-------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
| 0       | 0    | -             | 3.6734          | 0.6259                     | 0.6583                | 0.6421                       |
| 1.4970  | 250  | 3.8677        | 3.3900          | 0.6334                     | 0.6510                | 0.6422                       |
| 2.9940  | 500  | 3.188         | 1.8654          | 0.5772                     | 0.6252                | 0.6012                       |
| 4.4910  | 750  | 1.4714        | 0.6890          | 0.4032                     | 0.5437                | 0.4735                       |
| 5.9880  | 1000 | 0.8535        | 0.5511          | 0.3617                     | 0.5197                | 0.4407                       |
| 7.4850  | 1250 | 0.7547        | 0.5268          | 0.3469                     | 0.5346                | 0.4407                       |
| 8.9820  | 1500 | 0.716         | 0.5123          | 0.3684                     | 0.5223                | 0.4454                       |
| 10.4790 | 1750 | 0.6939        | 0.5039          | 0.3846                     | 0.5179                | 0.4512                       |
| 11.9760 | 2000 | 0.6789        | 0.4986          | 0.4120                     | 0.5280                | 0.4700                       |
| 13.4731 | 2250 | 0.6681        | 0.4953          | 0.4148                     | 0.5189                | 0.4669                       |
| 14.9701 | 2500 | 0.662         | 0.4918          | 0.4224                     | 0.5109                | 0.4666                       |
| 16.4671 | 2750 | 0.6575        | 0.4905          | 0.4224                     | 0.5109                | 0.4666                       |
| 17.9641 | 3000 | 0.6555        | 0.4900          | 0.4281                     | 0.5109                | 0.4695                       |


### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.1+cu128
- Accelerate: 1.12.0
- Datasets: 2.21.0
- Tokenizers: 0.22.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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