| --- |
| base_model: microsoft/mpnet-base |
| datasets: |
| - mertcobanov/all-nli-triplets-turkish |
| language: |
| - en |
| - tr |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:120781 |
| - loss:MultipleNegativesRankingLoss |
| widget: |
| - source_sentence: Bir köpek sahibi, evcil hayvanıyla birlikte koşuyor ve evcil hayvan |
| bir parkurda engellerden kaçınıyor. |
| sentences: |
| - Bazı bitkilerin önünde mavi bir kano. |
| - Bir adam köpeğinin yanında koşuyor. |
| - Adam bir kediyle birlikte. |
| - source_sentence: Parlamenter bölümünün patronunun ev hizmetiyle bağlantılı bir politikacı, |
| 0-609-3459812 numaralı cep telefonuna sahip ve mizah anlayışının olmamasıyla tanınıyor, |
| 'Hayran' adlı birinden gelen 'En iyi kürek dilekleri' mesajını pek iyi karşılamadı. |
| sentences: |
| - Doktor Perennial, kötü niyetli çavuş uyandığında ayakta duruyordu. |
| - Politikacı, patronunun ev hizmetini aradığında, bir 'hayran'dan gelen bir mesaja |
| pek hoş karşılamadı. |
| - Mesajı aldığı için o kadar minnettardı ki, gönderen kişiye bir demet çiçek gönderdi. |
| - source_sentence: Bankanın kasalarında. |
| sentences: |
| - Ayakta duran bir insan |
| - Banka kasasında. |
| - Bankadaki kasa. |
| - source_sentence: Bir grup Asyalı erkek, birlikte bir yemek yedikten sonra büyük |
| bir masanın etrafında poz veriyor. |
| sentences: |
| - Bir grup Asyalı erkek birlikte bir yemek yedi. |
| - Pazarlar, kaplıcalar ve kayak pistleri burada bulunan diğer cazibe merkezlerinden |
| bazılarını oluşturuyor. |
| - Bir grup Asyalı erkek futbol oynuyor. |
| - source_sentence: Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa, |
| bu yardımcı olur. |
| sentences: |
| - Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için. |
| - Adamın gömleği, kot pantolonundan farklı bir renkte. |
| - Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın. |
| model-index: |
| - name: SentenceTransformer based on microsoft/mpnet-base |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: all nli dev turkish |
| type: all-nli-dev-turkish |
| metrics: |
| - type: cosine_accuracy |
| value: 0.7764277035236938 |
| name: Cosine Accuracy |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: all nli test turkish |
| type: all-nli-test-turkish |
| metrics: |
| - type: cosine_accuracy |
| value: 0.7740959297927069 |
| name: Cosine Accuracy |
| --- |
| |
| # SentenceTransformer based on microsoft/mpnet-base |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) 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/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) |
| - **Languages:** en, tr |
| <!-- - **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: MPNetModel |
| (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("mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx") |
| # Run inference |
| sentences = [ |
| 'Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa, bu yardımcı olur.', |
| 'Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.', |
| 'Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.', |
| ] |
| 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 |
|
|
| #### Triplet |
|
|
| * Datasets: `all-nli-dev-turkish` and `all-nli-test-turkish` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
|
|
| | Metric | all-nli-dev-turkish | all-nli-test-turkish | |
| |:--------------------|:--------------------|:---------------------| |
| | **cosine_accuracy** | **0.7764** | **0.7741** | |
| |
| <!-- |
| ## 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 |
| |
| #### all-nli-triplets-turkish |
| |
| * Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [13554fd](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/13554fdb2675c44f84a8dccc1afb51cee8a1e4ba) |
| * Size: 120,781 training samples |
| * Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor_translated | positive_translated | negative_translated | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 11.77 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.1 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 12.41 tokens</li><li>max: 44 tokens</li></ul> | |
| * Samples: |
| | anchor_translated | positive_translated | negative_translated | |
| |:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------| |
| | <code>Bir kişi, bir atın üzerinde, bozulmuş bir uçağın üzerinden atlıyor.</code> | <code>Bir kişi dışarıda, bir atın üzerinde.</code> | <code>Bir kişi bir lokantada omlet siparişi veriyor.</code> | |
| | <code>Bir Küçük Lig takımı, bir oyuncunun bir üsse kayarak girmeye çalıştığı sırada onu yakalamaya çalışıyor.</code> | <code>Bir takım bir koşucuyu dışarı atmaya çalışıyor.</code> | <code>Bir takım Satürn'de beyzbol oynuyor.</code> | |
| | <code>Kadın beyaz giyiyor.</code> | <code>Beyaz bir ceket giymiş bir kadın bir tekerlekli sandalyeyi itiyor.</code> | <code>Siyah giyinmiş bir adam, siyah giyinmiş bir kadını kucaklıyor.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### all-nli-triplets-turkish |
| |
| * Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [13554fd](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/13554fdb2675c44f84a8dccc1afb51cee8a1e4ba) |
| * Size: 6,584 evaluation samples |
| * Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor_translated | positive_translated | negative_translated | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 2 tokens</li><li>mean: 22.3 tokens</li><li>max: 135 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 10.92 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.81 tokens</li><li>max: 34 tokens</li></ul> | |
| * Samples: |
| | anchor_translated | positive_translated | negative_translated | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------| |
| | <code>Ayrıca, bu özel tüketim vergileri, diğer vergiler gibi, hükümetin ödeme zorunluluğunu sağlama yetkisini kullanarak belirlenir.</code> | <code>Hükümetin ödeme zorlaması, özel tüketim vergilerinin nasıl hesaplandığını belirler.</code> | <code>Özel tüketim vergileri genel kuralın bir istisnasıdır ve aslında GSYİH payına dayalı olarak belirlenir.</code> | |
| | <code>Gri bir sweatshirt giymiş bir sanatçı, canlı renklerde bir kasaba tablosu üzerinde çalışıyor.</code> | <code>Bir ressam gri giysiler içinde bir kasabanın resmini yapıyor.</code> | <code>Bir kişi bir beyzbol sopası tutuyor ve gelen bir atış için planda bekliyor.</code> | |
| | <code>İmkansız.</code> | <code>Yapılamaz.</code> | <code>Tamamen mümkün.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 10 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `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 |
| - `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`: 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 |
| - `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 |
| - `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 |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | all-nli-dev-turkish_cosine_accuracy | all-nli-test-turkish_cosine_accuracy | |
| |:------:|:----:|:-------------:|:---------------:|:-----------------------------------:|:------------------------------------:| |
| | 0 | 0 | - | - | 0.5729 | - | |
| | 0.2119 | 100 | 6.6103 | 4.5154 | 0.6970 | - | |
| | 0.4237 | 200 | 5.1602 | 3.7328 | 0.7195 | - | |
| | 0.6356 | 300 | 4.4533 | 3.3389 | 0.7372 | - | |
| | 0.8475 | 400 | 3.4465 | 3.6044 | 0.7187 | - | |
| | 1.0572 | 500 | 2.6977 | 3.3043 | 0.7418 | - | |
| | 1.2691 | 600 | 3.8142 | 3.2066 | 0.7512 | - | |
| | 1.4809 | 700 | 3.4333 | 3.0716 | 0.7508 | - | |
| | 1.6928 | 800 | 3.1488 | 2.9590 | 0.7553 | - | |
| | 1.9047 | 900 | 1.8677 | 3.2416 | 0.7442 | - | |
| | 2.1144 | 1000 | 2.2034 | 2.9323 | 0.7634 | - | |
| | 2.3263 | 1100 | 2.9834 | 2.9406 | 0.7669 | - | |
| | 2.5381 | 1200 | 2.6785 | 2.8607 | 0.7672 | - | |
| | 2.75 | 1300 | 2.5096 | 2.8939 | 0.7684 | - | |
| | 2.9619 | 1400 | 0.876 | 3.2539 | 0.7416 | - | |
| | 3.1716 | 1500 | 2.3355 | 2.7503 | 0.7758 | - | |
| | 3.3835 | 1600 | 2.4666 | 2.7920 | 0.7707 | - | |
| | 3.5953 | 1700 | 2.2691 | 2.7860 | 0.7729 | - | |
| | 3.8072 | 1800 | 1.8024 | 2.9899 | 0.7571 | - | |
| | 4.0169 | 1900 | 0.6443 | 3.0993 | 0.7456 | - | |
| | 4.2288 | 2000 | 2.3976 | 2.7792 | 0.7811 | - | |
| | 4.4407 | 2100 | 2.1145 | 2.7968 | 0.7728 | - | |
| | 4.6525 | 2200 | 1.9788 | 2.7243 | 0.7751 | - | |
| | 4.8644 | 2300 | 1.1676 | 2.9885 | 0.7567 | - | |
| | 5.0742 | 2400 | 1.0009 | 2.7374 | 0.7767 | - | |
| | 5.2860 | 2500 | 2.1276 | 2.7822 | 0.7767 | - | |
| | 5.4979 | 2600 | 1.8459 | 2.7822 | 0.7760 | - | |
| | 5.7097 | 2700 | 1.7659 | 2.7322 | 0.7766 | - | |
| | 5.9216 | 2800 | 0.5916 | 3.0191 | 0.7596 | - | |
| | 6.1314 | 2900 | 1.3908 | 2.6973 | 0.7772 | - | |
| | 6.3432 | 3000 | 1.9257 | 2.7585 | 0.7763 | - | |
| | 6.5551 | 3100 | 1.6558 | 2.7350 | 0.7760 | - | |
| | 6.7669 | 3200 | 1.5368 | 2.7903 | 0.7722 | - | |
| | 6.9788 | 3300 | 0.1968 | 3.0849 | 0.7479 | - | |
| | 7.1886 | 3400 | 1.8044 | 2.6626 | 0.7825 | - | |
| | 7.4004 | 3500 | 1.7048 | 2.7380 | 0.7790 | - | |
| | 7.6123 | 3600 | 1.5666 | 2.7250 | 0.7796 | - | |
| | 7.8242 | 3700 | 1.0954 | 2.9620 | 0.7629 | - | |
| | 8.0339 | 3800 | 0.487 | 2.8900 | 0.7641 | - | |
| | 8.2458 | 3900 | 1.8398 | 2.7186 | 0.7796 | - | |
| | 8.4576 | 4000 | 1.5659 | 2.7259 | 0.7778 | - | |
| | 8.6695 | 4100 | 1.4825 | 2.7007 | 0.7760 | - | |
| | 8.8814 | 4200 | 0.7019 | 2.9050 | 0.7675 | - | |
| | 9.0911 | 4300 | 0.9278 | 2.7606 | 0.7731 | - | |
| | 9.3030 | 4400 | 1.766 | 2.6978 | 0.7787 | - | |
| | 9.5148 | 4500 | 1.4699 | 2.7114 | 0.7801 | - | |
| | 9.7267 | 4600 | 1.4647 | 2.7096 | 0.7799 | - | |
| | 9.9386 | 4700 | 0.3321 | 2.7418 | 0.7764 | - | |
| | 9.9809 | 4720 | - | - | - | 0.7741 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.14 |
| - Sentence Transformers: 3.3.1 |
| - Transformers: 4.46.3 |
| - PyTorch: 2.4.0 |
| - Accelerate: 0.27.2 |
| - Datasets: 3.1.0 |
| - Tokenizers: 0.20.3 |
| |
| ## 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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