README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:111468
- loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-small
widget:
- source_sentence: What is something you do (or don’t do), even though you feel conflicted
    about it?
  sentences:
  - What is something you do (or don’t do), even though you feel conflicted about
    it?
  - Is it worth buying the iPhone 7?
  - 'Hypothetical scenarios: King Henry VIII loses his battle with James IV in 1513
    & dies; Pope Julius II doesn''t die in 1513. How''s the world different?'
- source_sentence: Exams for a mechanical engineer?
  sentences:
  - Exams for a mechanical engineer?
  - Can you prefer any website or ideas by which I can understand antenna subject
    practically in b.tech?
  - Mackenzie is a writer-in-residence at the 2B Theatre in Halifax and teaches at
    the National Theatre School of Canada in Montreal .
- source_sentence: What will a Christian wife do if her husband left her for years?
  sentences:
  - How many United States Presidents have there been?
  - What is planning without words?
  - What will a Christian wife do if her husband left her for years?
- source_sentence: How do I research for MUN?
  sentences:
  - How do I research for MUN?
  - What is the best way to be an investment banker?
  - What is the best way to do an MUN research?
- source_sentence: I am poor, ugly, untalented, 20 years old, and have big dreams.
    How can I succeed in life?
  sentences:
  - What app can I use taking notes?
  - Am I too old to succeed in my life at age 32?
  - I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed
    in life?
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: 0.3
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.58
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.068
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.58
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.68
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4950369328373354
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.43527777777777776
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4475531768839056
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.26
      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: 0.64
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16666666666666663
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.066
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.24
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.45
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.49
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4279054208986469
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3892142857142856
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3750113241088494
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.28
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.53
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.56
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11400000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.067
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.27
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.515
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5449999999999999
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.64
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.46147117686799116
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4122460317460317
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4112822504963775
      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/model-b-structured")
# Run inference
sentences = [
    'I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed in life?',
    'I am poor, ugly, untalented, 20 years old, and have big dreams. How can I succeed in life?',
    'Am I too old to succeed in my life at age 32?',
]
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, 0.3917],
#         [1.0000, 1.0000, 0.3917],
#         [0.3917, 0.3917, 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.*
-->

## 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   | 0.3         | 0.26       |
| cosine_accuracy@3   | 0.58        | 0.48       |
| cosine_accuracy@5   | 0.6         | 0.52       |
| cosine_accuracy@10  | 0.68        | 0.64       |
| cosine_precision@1  | 0.3         | 0.26       |
| cosine_precision@3  | 0.1933      | 0.1667     |
| cosine_precision@5  | 0.12        | 0.108      |
| cosine_precision@10 | 0.068       | 0.066      |
| cosine_recall@1     | 0.3         | 0.24       |
| cosine_recall@3     | 0.58        | 0.45       |
| cosine_recall@5     | 0.6         | 0.49       |
| cosine_recall@10    | 0.68        | 0.6        |
| **cosine_ndcg@10**  | **0.495**   | **0.4279** |
| cosine_mrr@10       | 0.4353      | 0.3892     |
| cosine_map@100      | 0.4476      | 0.375      |

#### 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   | 0.28       |
| cosine_accuracy@3   | 0.53       |
| cosine_accuracy@5   | 0.56       |
| cosine_accuracy@10  | 0.66       |
| cosine_precision@1  | 0.28       |
| cosine_precision@3  | 0.18       |
| cosine_precision@5  | 0.114      |
| cosine_precision@10 | 0.067      |
| cosine_recall@1     | 0.27       |
| cosine_recall@3     | 0.515      |
| cosine_recall@5     | 0.545      |
| cosine_recall@10    | 0.64       |
| **cosine_ndcg@10**  | **0.4615** |
| cosine_mrr@10       | 0.4122     |
| cosine_map@100      | 0.4113     |

<!--
## 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.*
-->

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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: 111,468 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: 6 tokens</li><li>mean: 16.11 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.16 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.35 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | anchor                                                                                | positive                                                                              | negative                                                                                 |
  |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
  | <code>How many grams of protein should I eat a day?</code>                            | <code>How much protein should I eat per day?</code>                                   | <code>How does hypokalemia lead to polyuria in primary aldosteronism?</code>             |
  | <code>Who said to get out of economic crisis we need to buy more?</code>              | <code>Who said to get out of economic crisis we need to buy more?</code>              | <code>What are some good IT certifications that don't require programming skills?</code> |
  | <code>What is the difference between Chinese and western culture within China?</code> | <code>What is the difference between Chinese and western culture within China?</code> | <code>What is the difference between Chinese and western culture outside China?</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: 12,386 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: 16.22 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.28 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.39 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                              | positive                                                                                                                            | negative                                                                     |
  |:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|
  | <code>What is it about novels that allow them to deal with deep themes that short stories, drama, and poetry cannot achieve?</code> | <code>What is it about novels that allow them to deal with deep themes that short stories, drama, and poetry cannot achieve?</code> | <code>What are films that deal with themes like death and letting go?</code> |
  | <code>If alien civilizations are thought to be much more advanced than us, why haven't they made contact with us yet?</code>        | <code>If there are super intelligent alien beings somewhere in the Galaxy why haven't they tried to contact us yet?</code>          | <code>What's not so good about Aston Martin cars?</code>                     |
  | <code>How can you determine the Lewis dot structure for sulfur trioxide?</code>                                                     | <code>How can you determine the Lewis dot structure for sulfur trioxide?</code>                                                     | <code>How can you determine the Lewis dot structure for sulfur?</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`: 2e-05
- `weight_decay`: 0.0001
- `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/model-b-structured
- `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`: 2e-05
- `weight_decay`: 0.0001
- `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/model-b-structured
- `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    | -             | 3.6560          | 0.6259                     | 0.6583                | 0.6421                       |
| 0.2874 | 250  | 2.1436        | 0.4823          | 0.5264                     | 0.5634                | 0.5449                       |
| 0.5747 | 500  | 0.5891        | 0.4299          | 0.5280                     | 0.5051                | 0.5165                       |
| 0.8621 | 750  | 0.5393        | 0.4123          | 0.5246                     | 0.4755                | 0.5001                       |
| 1.1494 | 1000 | 0.5173        | 0.4027          | 0.5068                     | 0.4549                | 0.4809                       |
| 1.4368 | 1250 | 0.5022        | 0.3954          | 0.5055                     | 0.4513                | 0.4784                       |
| 1.7241 | 1500 | 0.4958        | 0.3909          | 0.5033                     | 0.4466                | 0.4749                       |
| 2.0115 | 1750 | 0.4908        | 0.3890          | 0.4897                     | 0.4416                | 0.4656                       |
| 2.2989 | 2000 | 0.4824        | 0.3859          | 0.4912                     | 0.4359                | 0.4636                       |
| 2.5862 | 2250 | 0.4797        | 0.3847          | 0.4987                     | 0.4387                | 0.4687                       |
| 2.8736 | 2500 | 0.4728        | 0.3834          | 0.4969                     | 0.4256                | 0.4613                       |
| 3.1609 | 2750 | 0.4721        | 0.3824          | 0.4863                     | 0.4279                | 0.4571                       |
| 3.4483 | 3000 | 0.4694        | 0.3822          | 0.4950                     | 0.4279                | 0.4615                       |


### 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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