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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:314315
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
- sentence-transformers/stsb
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- 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
widget:
- source_sentence: The pitcher is pitching the ball in a game of baseball.
sentences:
- the lady digs into the ground
- A group of people are sitting at tables.
- The pitcher throws the ball.
- source_sentence: People are conversing at a dining table under a canopy.
sentences:
- A canine is using his legs.
- The people are creative.
- People at a party are seated for dinner on the lawn.
- source_sentence: Two teenage girls conversing next to lockers.
sentences:
- Girls talking about their problems next to lockers.
- A group of people play in the ocean.
- The man is testing the bike.
- source_sentence: A young boy in a hoodie climbs a red slide sitting on a red and
green checkered background.
sentences:
- People are buying food from a street vendor.
- A boy is playing.
- A dog outside digging.
- source_sentence: A professional swimmer spits water out after surfacing while grabbing
the hand of someone helping him back to land.
sentences:
- A group of people wait in a line.
- A tourist has his picture taken on Easter Island.
- The swimmer almost drowned after being sucked under a fast current.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.7641416788909702
name: Pearson Cosine
- type: spearman_cosine
value: 0.763668633314844
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7808845626705342
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.783960481366303
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7714319160162553
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7750607015673249
name: Spearman Euclidean
- type: pearson_dot
value: 0.587659176024498
name: Pearson Dot
- type: spearman_dot
value: 0.6010467058509925
name: Spearman Dot
- type: pearson_max
value: 0.7808845626705342
name: Pearson Max
- type: spearman_max
value: 0.783960481366303
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.6773826673743271
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.5830236673355103
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7209834880077135
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5085207223892212
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6137273007079102
name: Cosine Precision
- type: cosine_recall
value: 0.873667299547247
name: Cosine Recall
- type: cosine_ap
value: 0.7219177301725319
name: Cosine Ap
- type: dot_accuracy
value: 0.6389415421942528
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 45.1016845703125
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7090406632451638
name: Dot F1
- type: dot_f1_threshold
value: 32.459449768066406
name: Dot F1 Threshold
- type: dot_precision
value: 0.5775450202131569
name: Dot Precision
- type: dot_recall
value: 0.9180663064115671
name: Dot Recall
- type: dot_ap
value: 0.6795197111227502
name: Dot Ap
- type: manhattan_accuracy
value: 0.6625217984684206
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 158.29489135742188
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7041269465332466
name: Manhattan F1
- type: manhattan_f1_threshold
value: 178.5047607421875
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5921131248755228
name: Manhattan Precision
- type: manhattan_recall
value: 0.8684095224185775
name: Manhattan Recall
- type: manhattan_ap
value: 0.7054112942825768
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.6578967321252559
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 7.951424598693848
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7015471831817645
name: Euclidean F1
- type: euclidean_f1_threshold
value: 9.045232772827148
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5888767720828789
name: Euclidean Precision
- type: euclidean_recall
value: 0.8675332262304659
name: Euclidean Recall
- type: euclidean_ap
value: 0.7024193897121154
name: Euclidean Ap
- type: max_accuracy
value: 0.6773826673743271
name: Max Accuracy
- type: max_accuracy_threshold
value: 158.29489135742188
name: Max Accuracy Threshold
- type: max_f1
value: 0.7209834880077135
name: Max F1
- type: max_f1_threshold
value: 178.5047607421875
name: Max F1 Threshold
- type: max_precision
value: 0.6137273007079102
name: Max Precision
- type: max_recall
value: 0.9180663064115671
name: Max Recall
- type: max_ap
value: 0.7219177301725319
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v3-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. It maps sentences & paragraphs to a 768-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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **Language:** en
<!-- - **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: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAll")
# Run inference
sentences = [
'A professional swimmer spits water out after surfacing while grabbing the hand of someone helping him back to land.',
'The swimmer almost drowned after being sucked under a fast current.',
'A group of people wait in a line.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7641 |
| **spearman_cosine** | **0.7637** |
| pearson_manhattan | 0.7809 |
| spearman_manhattan | 0.784 |
| pearson_euclidean | 0.7714 |
| spearman_euclidean | 0.7751 |
| pearson_dot | 0.5877 |
| spearman_dot | 0.601 |
| pearson_max | 0.7809 |
| spearman_max | 0.784 |
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6774 |
| cosine_accuracy_threshold | 0.583 |
| cosine_f1 | 0.721 |
| cosine_f1_threshold | 0.5085 |
| cosine_precision | 0.6137 |
| cosine_recall | 0.8737 |
| cosine_ap | 0.7219 |
| dot_accuracy | 0.6389 |
| dot_accuracy_threshold | 45.1017 |
| dot_f1 | 0.709 |
| dot_f1_threshold | 32.4594 |
| dot_precision | 0.5775 |
| dot_recall | 0.9181 |
| dot_ap | 0.6795 |
| manhattan_accuracy | 0.6625 |
| manhattan_accuracy_threshold | 158.2949 |
| manhattan_f1 | 0.7041 |
| manhattan_f1_threshold | 178.5048 |
| manhattan_precision | 0.5921 |
| manhattan_recall | 0.8684 |
| manhattan_ap | 0.7054 |
| euclidean_accuracy | 0.6579 |
| euclidean_accuracy_threshold | 7.9514 |
| euclidean_f1 | 0.7015 |
| euclidean_f1_threshold | 9.0452 |
| euclidean_precision | 0.5889 |
| euclidean_recall | 0.8675 |
| euclidean_ap | 0.7024 |
| max_accuracy | 0.6774 |
| max_accuracy_threshold | 158.2949 |
| max_f1 | 0.721 |
| max_f1_threshold | 178.5048 |
| max_precision | 0.6137 |
| max_recall | 0.9181 |
| **max_ap** | **0.7219** |
<!--
## 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
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 314,315 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>0</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1,
"prior_layers_weight": 1,
"kl_div_weight": 1.2,
"kl_temperature": 1.2
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1,
"prior_layers_weight": 1,
"kl_div_weight": 1.2,
"kl_temperature": 1.2
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 5e-06
- `weight_decay`: 1e-07
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln
- `hub_strategy`: checkpoint
- `batch_sampler`: no_duplicates
#### 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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-06
- `weight_decay`: 1e-07
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.33
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: 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`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln
- `hub_strategy`: checkpoint
- `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 | max_ap | spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:------:|:---------------:|
| None | 0 | - | 5.4171 | - | 0.4276 |
| 0.1501 | 1474 | 4.9879 | - | - | - |
| 0.3000 | 2947 | - | 2.6463 | 0.6840 | - |
| 0.3001 | 2948 | 3.2669 | - | - | - |
| 0.4502 | 4422 | 2.6363 | - | - | - |
| 0.6000 | 5894 | - | 1.8436 | 0.7014 | - |
| 0.6002 | 5896 | 2.192 | - | - | - |
| 0.7503 | 7370 | 0.8208 | - | - | - |
| 0.9000 | 8841 | - | 1.5551 | 0.7065 | - |
| 0.9003 | 8844 | 0.6161 | - | - | - |
| 1.0504 | 10318 | 1.0301 | - | - | - |
| 1.2000 | 11788 | - | 1.1883 | 0.7131 | - |
| 1.2004 | 11792 | 1.8209 | - | - | - |
| 1.3505 | 13266 | 1.6887 | - | - | - |
| 1.5001 | 14735 | - | 1.1067 | 0.7119 | - |
| 1.5006 | 14740 | 1.6114 | - | - | - |
| 1.6506 | 16214 | 1.0691 | - | - | - |
| 1.8001 | 17682 | - | 1.0872 | 0.7183 | - |
| 1.8007 | 17688 | 0.3982 | - | - | - |
| 1.9507 | 19162 | 0.3659 | - | - | - |
| 2.1001 | 20629 | - | 0.9642 | 0.7221 | - |
| 2.1008 | 20636 | 1.1702 | - | - | - |
| 2.2508 | 22110 | 1.4984 | - | - | - |
| 2.4001 | 23576 | - | 0.9437 | 0.7200 | - |
| 2.4009 | 23584 | 1.4609 | - | - | - |
| 2.5510 | 25058 | 1.4477 | - | - | - |
| 2.7001 | 26523 | - | 0.9428 | 0.7216 | - |
| 2.7010 | 26532 | 0.5802 | - | - | - |
| 2.8511 | 28006 | 0.3297 | - | - | - |
| 3.0 | 29469 | - | 0.9532 | 0.7219 | - |
| None | 0 | - | 2.4079 | 0.7219 | 0.7637 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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",
}
```
#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### 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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