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mmlw-roberta-base-klej-dyk-v0.1

Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
dataset_size:1K<n<10K
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
base_model: sdadas/mmlw-roberta-base
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Żywot św. Stanisława
sentences:
- czym różni się Żywot św. Stanisława od Legendy św. Stanisława?
- w którym kraju w noc sylwestrową je się oliebollen?
- Pierwsze bloki mieszkalne powstały pod koniec lat 80.
- source_sentence: Herkules na rozstajach
sentences:
- jak zinterpretować wymowę obrazu Herkules na rozstajach?
- gdzie zginął przedwojenny minister Antoni Olszewski?
- kiedy konsekrowano katedrę św. Teresy z Avili w Požedze?
- source_sentence: gdzie rośnie bokkonia?
sentences:
- gdzie występuje rogownica szerokolistna?
- Ochrzcił w sierpniu 1982 ich syna księcia Wilhelma.
- Pośmiertnie został odznaczony Krzyżem Virtuti Militari.
- source_sentence: czym jest Kompas Sztuki?
sentences:
- ' Projekt Kompas Sztuki: Galeria m2 (m kwadrat).'
- 'Do rodzaju Caraipa zaliczanych jest ok. 55 gatunków:'
- kto jest aktualnym rekordzistą Chorwacji w skoku w dal?
- source_sentence: Dalsze losy relikwii
sentences:
- Losy relikwii świętego
- czemu gra The Saboteur wywołała wiele kontrowersji?
- kto jest pierwszym rosyjskim kierowcą wyścigowym startującym w Formule 1?
model-index:
- name: mmlw-roberta-base-klej-dyk-v0.1
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.18990384615384615
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5865384615384616
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7692307692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8533653846153846
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18990384615384615
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1955128205128205
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15384615384615383
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08533653846153846
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18990384615384615
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5865384615384616
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7692307692307693
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8533653846153846
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5204892782178483
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4127814026251526
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.418150211843158
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.1875
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5889423076923077
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7596153846153846
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8629807692307693
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1875
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19631410256410253
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15192307692307688
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08629807692307694
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1875
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5889423076923077
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7596153846153846
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8629807692307693
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5204340563935984
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4100885225885227
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4147514658961434
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.19471153846153846
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5649038461538461
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7451923076923077
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8461538461538461
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.19471153846153846
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18830128205128205
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1490384615384615
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08461538461538462
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19471153846153846
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5649038461538461
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7451923076923077
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8461538461538461
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5144907264607753
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4078373015873016
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.413093644747221
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.18269230769230768
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5192307692307693
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7163461538461539
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8293269230769231
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18269230769230768
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17307692307692307
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14326923076923076
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08293269230769229
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18269230769230768
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5192307692307693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7163461538461539
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8293269230769231
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4955346842225082
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.38889652014651993
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39396452853345754
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.1778846153846154
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4831730769230769
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6514423076923077
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7740384615384616
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1778846153846154
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16105769230769232
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13028846153846152
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07740384615384614
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1778846153846154
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4831730769230769
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6514423076923077
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7740384615384616
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4639263641936578
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36540083180708166
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3728380879103276
name: Cosine Map@100
---
# mmlw-roberta-base-klej-dyk-v0.1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sdadas/mmlw-roberta-base](https://huggingface.co/sdadas/mmlw-roberta-base). 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:** [sdadas/mmlw-roberta-base](https://huggingface.co/sdadas/mmlw-roberta-base) <!-- at revision 57e19d8314b983137ebe25ce734880af0dc98a9e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Dalsze losy relikwii',
'Losy relikwii świętego',
'czemu gra The Saboteur wywołała wiele kontrowersji?',
]
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
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1899 |
| cosine_accuracy@3 | 0.5865 |
| cosine_accuracy@5 | 0.7692 |
| cosine_accuracy@10 | 0.8534 |
| cosine_precision@1 | 0.1899 |
| cosine_precision@3 | 0.1955 |
| cosine_precision@5 | 0.1538 |
| cosine_precision@10 | 0.0853 |
| cosine_recall@1 | 0.1899 |
| cosine_recall@3 | 0.5865 |
| cosine_recall@5 | 0.7692 |
| cosine_recall@10 | 0.8534 |
| cosine_ndcg@10 | 0.5205 |
| cosine_mrr@10 | 0.4128 |
| **cosine_map@100** | **0.4182** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1875 |
| cosine_accuracy@3 | 0.5889 |
| cosine_accuracy@5 | 0.7596 |
| cosine_accuracy@10 | 0.863 |
| cosine_precision@1 | 0.1875 |
| cosine_precision@3 | 0.1963 |
| cosine_precision@5 | 0.1519 |
| cosine_precision@10 | 0.0863 |
| cosine_recall@1 | 0.1875 |
| cosine_recall@3 | 0.5889 |
| cosine_recall@5 | 0.7596 |
| cosine_recall@10 | 0.863 |
| cosine_ndcg@10 | 0.5204 |
| cosine_mrr@10 | 0.4101 |
| **cosine_map@100** | **0.4148** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1947 |
| cosine_accuracy@3 | 0.5649 |
| cosine_accuracy@5 | 0.7452 |
| cosine_accuracy@10 | 0.8462 |
| cosine_precision@1 | 0.1947 |
| cosine_precision@3 | 0.1883 |
| cosine_precision@5 | 0.149 |
| cosine_precision@10 | 0.0846 |
| cosine_recall@1 | 0.1947 |
| cosine_recall@3 | 0.5649 |
| cosine_recall@5 | 0.7452 |
| cosine_recall@10 | 0.8462 |
| cosine_ndcg@10 | 0.5145 |
| cosine_mrr@10 | 0.4078 |
| **cosine_map@100** | **0.4131** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.1827 |
| cosine_accuracy@3 | 0.5192 |
| cosine_accuracy@5 | 0.7163 |
| cosine_accuracy@10 | 0.8293 |
| cosine_precision@1 | 0.1827 |
| cosine_precision@3 | 0.1731 |
| cosine_precision@5 | 0.1433 |
| cosine_precision@10 | 0.0829 |
| cosine_recall@1 | 0.1827 |
| cosine_recall@3 | 0.5192 |
| cosine_recall@5 | 0.7163 |
| cosine_recall@10 | 0.8293 |
| cosine_ndcg@10 | 0.4955 |
| cosine_mrr@10 | 0.3889 |
| **cosine_map@100** | **0.394** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1779 |
| cosine_accuracy@3 | 0.4832 |
| cosine_accuracy@5 | 0.6514 |
| cosine_accuracy@10 | 0.774 |
| cosine_precision@1 | 0.1779 |
| cosine_precision@3 | 0.1611 |
| cosine_precision@5 | 0.1303 |
| cosine_precision@10 | 0.0774 |
| cosine_recall@1 | 0.1779 |
| cosine_recall@3 | 0.4832 |
| cosine_recall@5 | 0.6514 |
| cosine_recall@10 | 0.774 |
| cosine_ndcg@10 | 0.4639 |
| cosine_mrr@10 | 0.3654 |
| **cosine_map@100** | **0.3728** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 3,738 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 50.1 tokens</li><li>max: 466 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.62 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
| <code>Zespół Blaua (zespół Jabsa, ang. Blau syndrome, BS) – rzadka choroba genetyczna o dziedziczeniu autosomalnym dominującym, charakteryzująca się ziarniniakowym zapaleniem stawów o wczesnym początku, zapaleniem jagodówki (uveitis) i wysypką skórną, a także kamptodaktylią.</code> | <code>jakie choroby genetyczne dziedziczą się autosomalnie dominująco?</code> |
| <code>Gorgippia Gorgippia – starożytne miasto bosporańskie nad Morzem Czarnym, którego pozostałości znajdują się obecnie pod współczesną zabudową centralnej części miasta Anapa w Kraju Krasnodarskim w Rosji.</code> | <code>gdzie obecnie znajduje się starożytne miasto Gorgippia?</code> |
| <code>Ulubionym dystansem Rücker było 400 metrów i to na nim notowała największe indywidualne sukcesy : srebrny medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) 6. miejsce w Pucharze Świata w Lekkoatletyce (Hawana 1992) 5. miejsce na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) srebro podczas Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) złota medalistka mistrzostw Niemiec Duże sukcesy odnosiła także w sztafecie 4 x 400 metrów : złoto Mistrzostw Europy juniorów w lekkoatletyce (Varaždin 1989) złoty medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) brąz na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) brązowy medal podczas Igrzysk Olimpijskich (Atlanta 1996) brąz na Halowych Mistrzostwach Świata w Lekkoatletyce (Paryż 1997) złoto Mistrzostw Świata w Lekkoatletyce (Ateny 1997) brązowy medal Mistrzostw Świata w Lekkoatletyce (Sewilla 1999)</code> | <code>kto zaprojektował medale, które będą wręczane podczas tegorocznych mistrzostw Europy juniorów w lekkoatletyce?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0 | 0 | - | 0.3475 | 0.3675 | 0.3753 | 0.2982 | 0.3798 |
| 0.0171 | 1 | 2.6683 | - | - | - | - | - |
| 0.0342 | 2 | 3.2596 | - | - | - | - | - |
| 0.0513 | 3 | 3.4541 | - | - | - | - | - |
| 0.0684 | 4 | 2.4201 | - | - | - | - | - |
| 0.0855 | 5 | 3.5911 | - | - | - | - | - |
| 0.1026 | 6 | 3.0902 | - | - | - | - | - |
| 0.1197 | 7 | 2.5999 | - | - | - | - | - |
| 0.1368 | 8 | 2.892 | - | - | - | - | - |
| 0.1538 | 9 | 2.8722 | - | - | - | - | - |
| 0.1709 | 10 | 2.3703 | - | - | - | - | - |
| 0.1880 | 11 | 2.6833 | - | - | - | - | - |
| 0.2051 | 12 | 1.9814 | - | - | - | - | - |
| 0.2222 | 13 | 1.6643 | - | - | - | - | - |
| 0.2393 | 14 | 1.8493 | - | - | - | - | - |
| 0.2564 | 15 | 1.5136 | - | - | - | - | - |
| 0.2735 | 16 | 1.9726 | - | - | - | - | - |
| 0.2906 | 17 | 1.1505 | - | - | - | - | - |
| 0.3077 | 18 | 1.3834 | - | - | - | - | - |
| 0.3248 | 19 | 1.2244 | - | - | - | - | - |
| 0.3419 | 20 | 1.2107 | - | - | - | - | - |
| 0.3590 | 21 | 0.8936 | - | - | - | - | - |
| 0.3761 | 22 | 0.8144 | - | - | - | - | - |
| 0.3932 | 23 | 0.8353 | - | - | - | - | - |
| 0.4103 | 24 | 1.572 | - | - | - | - | - |
| 0.4274 | 25 | 0.9257 | - | - | - | - | - |
| 0.4444 | 26 | 0.8405 | - | - | - | - | - |
| 0.4615 | 27 | 0.5621 | - | - | - | - | - |
| 0.4786 | 28 | 0.4241 | - | - | - | - | - |
| 0.4957 | 29 | 0.6171 | - | - | - | - | - |
| 0.5128 | 30 | 0.5989 | - | - | - | - | - |
| 0.5299 | 31 | 0.2767 | - | - | - | - | - |
| 0.5470 | 32 | 0.5599 | - | - | - | - | - |
| 0.5641 | 33 | 0.5964 | - | - | - | - | - |
| 0.5812 | 34 | 0.9778 | - | - | - | - | - |
| 0.5983 | 35 | 0.772 | - | - | - | - | - |
| 0.6154 | 36 | 1.0341 | - | - | - | - | - |
| 0.6325 | 37 | 0.3503 | - | - | - | - | - |
| 0.6496 | 38 | 0.8229 | - | - | - | - | - |
| 0.6667 | 39 | 0.969 | - | - | - | - | - |
| 0.6838 | 40 | 1.7993 | - | - | - | - | - |
| 0.7009 | 41 | 0.5542 | - | - | - | - | - |
| 0.7179 | 42 | 1.332 | - | - | - | - | - |
| 0.7350 | 43 | 1.1516 | - | - | - | - | - |
| 0.7521 | 44 | 1.3183 | - | - | - | - | - |
| 0.7692 | 45 | 1.0865 | - | - | - | - | - |
| 0.7863 | 46 | 0.6204 | - | - | - | - | - |
| 0.8034 | 47 | 0.7541 | - | - | - | - | - |
| 0.8205 | 48 | 0.9362 | - | - | - | - | - |
| 0.8376 | 49 | 0.3979 | - | - | - | - | - |
| 0.8547 | 50 | 0.7187 | - | - | - | - | - |
| 0.8718 | 51 | 0.9217 | - | - | - | - | - |
| 0.8889 | 52 | 0.4866 | - | - | - | - | - |
| 0.9060 | 53 | 0.355 | - | - | - | - | - |
| 0.9231 | 54 | 0.7172 | - | - | - | - | - |
| 0.9402 | 55 | 0.6007 | - | - | - | - | - |
| 0.9573 | 56 | 1.1547 | - | - | - | - | - |
| 0.9744 | 57 | 0.5713 | - | - | - | - | - |
| 0.9915 | 58 | 0.9089 | 0.3985 | 0.4164 | 0.4264 | 0.3642 | 0.4255 |
| 1.0085 | 59 | 0.594 | - | - | - | - | - |
| 1.0256 | 60 | 0.6554 | - | - | - | - | - |
| 1.0427 | 61 | 0.2794 | - | - | - | - | - |
| 1.0598 | 62 | 0.8654 | - | - | - | - | - |
| 1.0769 | 63 | 0.9698 | - | - | - | - | - |
| 1.0940 | 64 | 1.4827 | - | - | - | - | - |
| 1.1111 | 65 | 0.3159 | - | - | - | - | - |
| 1.1282 | 66 | 0.255 | - | - | - | - | - |
| 1.1453 | 67 | 0.9819 | - | - | - | - | - |
| 1.1624 | 68 | 0.7442 | - | - | - | - | - |
| 1.1795 | 69 | 0.8199 | - | - | - | - | - |
| 1.1966 | 70 | 0.2647 | - | - | - | - | - |
| 1.2137 | 71 | 0.4098 | - | - | - | - | - |
| 1.2308 | 72 | 0.1608 | - | - | - | - | - |
| 1.2479 | 73 | 0.2092 | - | - | - | - | - |
| 1.2650 | 74 | 0.1231 | - | - | - | - | - |
| 1.2821 | 75 | 0.3203 | - | - | - | - | - |
| 1.2991 | 76 | 0.1435 | - | - | - | - | - |
| 1.3162 | 77 | 0.2293 | - | - | - | - | - |
| 1.3333 | 78 | 0.131 | - | - | - | - | - |
| 1.3504 | 79 | 0.1662 | - | - | - | - | - |
| 1.3675 | 80 | 0.094 | - | - | - | - | - |
| 1.3846 | 81 | 0.1454 | - | - | - | - | - |
| 1.4017 | 82 | 0.3096 | - | - | - | - | - |
| 1.4188 | 83 | 0.3188 | - | - | - | - | - |
| 1.4359 | 84 | 0.1156 | - | - | - | - | - |
| 1.4530 | 85 | 0.0581 | - | - | - | - | - |
| 1.4701 | 86 | 0.0543 | - | - | - | - | - |
| 1.4872 | 87 | 0.0427 | - | - | - | - | - |
| 1.5043 | 88 | 0.07 | - | - | - | - | - |
| 1.5214 | 89 | 0.0451 | - | - | - | - | - |
| 1.5385 | 90 | 0.0646 | - | - | - | - | - |
| 1.5556 | 91 | 0.1152 | - | - | - | - | - |
| 1.5726 | 92 | 0.1292 | - | - | - | - | - |
| 1.5897 | 93 | 0.1591 | - | - | - | - | - |
| 1.6068 | 94 | 0.1194 | - | - | - | - | - |
| 1.6239 | 95 | 0.0876 | - | - | - | - | - |
| 1.6410 | 96 | 0.1018 | - | - | - | - | - |
| 1.6581 | 97 | 0.3309 | - | - | - | - | - |
| 1.6752 | 98 | 0.2214 | - | - | - | - | - |
| 1.6923 | 99 | 0.1536 | - | - | - | - | - |
| 1.7094 | 100 | 0.1543 | - | - | - | - | - |
| 1.7265 | 101 | 0.3663 | - | - | - | - | - |
| 1.7436 | 102 | 0.2719 | - | - | - | - | - |
| 1.7607 | 103 | 0.1379 | - | - | - | - | - |
| 1.7778 | 104 | 0.0479 | - | - | - | - | - |
| 1.7949 | 105 | 0.0757 | - | - | - | - | - |
| 1.8120 | 106 | 0.059 | - | - | - | - | - |
| 1.8291 | 107 | 0.119 | - | - | - | - | - |
| 1.8462 | 108 | 0.1295 | - | - | - | - | - |
| 1.8632 | 109 | 0.115 | - | - | - | - | - |
| 1.8803 | 110 | 0.142 | - | - | - | - | - |
| 1.8974 | 111 | 0.1064 | - | - | - | - | - |
| 1.9145 | 112 | 0.0959 | - | - | - | - | - |
| 1.9316 | 113 | 0.0839 | - | - | - | - | - |
| 1.9487 | 114 | 0.1762 | - | - | - | - | - |
| 1.9658 | 115 | 0.1986 | - | - | - | - | - |
| 1.9829 | 116 | 0.0599 | - | - | - | - | - |
| 2.0 | 117 | 0.1145 | 0.3869 | 0.4095 | 0.4135 | 0.3664 | 0.4195 |
| 2.0171 | 118 | 0.0815 | - | - | - | - | - |
| 2.0342 | 119 | 0.1052 | - | - | - | - | - |
| 2.0513 | 120 | 0.1348 | - | - | - | - | - |
| 2.0684 | 121 | 0.255 | - | - | - | - | - |
| 2.0855 | 122 | 0.251 | - | - | - | - | - |
| 2.1026 | 123 | 0.3033 | - | - | - | - | - |
| 2.1197 | 124 | 0.0385 | - | - | - | - | - |
| 2.1368 | 125 | 0.0687 | - | - | - | - | - |
| 2.1538 | 126 | 0.1682 | - | - | - | - | - |
| 2.1709 | 127 | 0.0774 | - | - | - | - | - |
| 2.1880 | 128 | 0.0944 | - | - | - | - | - |
| 2.2051 | 129 | 0.036 | - | - | - | - | - |
| 2.2222 | 130 | 0.0393 | - | - | - | - | - |
| 2.2393 | 131 | 0.0387 | - | - | - | - | - |
| 2.2564 | 132 | 0.0273 | - | - | - | - | - |
| 2.2735 | 133 | 0.056 | - | - | - | - | - |
| 2.2906 | 134 | 0.0279 | - | - | - | - | - |
| 2.3077 | 135 | 0.0557 | - | - | - | - | - |
| 2.3248 | 136 | 0.0197 | - | - | - | - | - |
| 2.3419 | 137 | 0.0216 | - | - | - | - | - |
| 2.3590 | 138 | 0.0212 | - | - | - | - | - |
| 2.3761 | 139 | 0.0239 | - | - | - | - | - |
| 2.3932 | 140 | 0.0526 | - | - | - | - | - |
| 2.4103 | 141 | 0.1072 | - | - | - | - | - |
| 2.4274 | 142 | 0.0347 | - | - | - | - | - |
| 2.4444 | 143 | 0.024 | - | - | - | - | - |
| 2.4615 | 144 | 0.0128 | - | - | - | - | - |
| 2.4786 | 145 | 0.0089 | - | - | - | - | - |
| 2.4957 | 146 | 0.0101 | - | - | - | - | - |
| 2.5128 | 147 | 0.0124 | - | - | - | - | - |
| 2.5299 | 148 | 0.011 | - | - | - | - | - |
| 2.5470 | 149 | 0.0182 | - | - | - | - | - |
| 2.5641 | 150 | 0.0379 | - | - | - | - | - |
| 2.5812 | 151 | 0.0395 | - | - | - | - | - |
| 2.5983 | 152 | 0.0372 | - | - | - | - | - |
| 2.6154 | 153 | 0.031 | - | - | - | - | - |
| 2.6325 | 154 | 0.0136 | - | - | - | - | - |
| 2.6496 | 155 | 0.0355 | - | - | - | - | - |
| 2.6667 | 156 | 0.0296 | - | - | - | - | - |
| 2.6838 | 157 | 0.0473 | - | - | - | - | - |
| 2.7009 | 158 | 0.0295 | - | - | - | - | - |
| 2.7179 | 159 | 0.0576 | - | - | - | - | - |
| 2.7350 | 160 | 0.0592 | - | - | - | - | - |
| 2.7521 | 161 | 0.0571 | - | - | - | - | - |
| 2.7692 | 162 | 0.0221 | - | - | - | - | - |
| 2.7863 | 163 | 0.0179 | - | - | - | - | - |
| 2.8034 | 164 | 0.0195 | - | - | - | - | - |
| 2.8205 | 165 | 0.0291 | - | - | - | - | - |
| 2.8376 | 166 | 0.024 | - | - | - | - | - |
| 2.8547 | 167 | 0.0396 | - | - | - | - | - |
| 2.8718 | 168 | 0.0352 | - | - | - | - | - |
| 2.8889 | 169 | 0.0431 | - | - | - | - | - |
| 2.9060 | 170 | 0.0222 | - | - | - | - | - |
| 2.9231 | 171 | 0.016 | - | - | - | - | - |
| 2.9402 | 172 | 0.0307 | - | - | - | - | - |
| 2.9573 | 173 | 0.0439 | - | - | - | - | - |
| 2.9744 | 174 | 0.0197 | - | - | - | - | - |
| 2.9915 | 175 | 0.0181 | 0.3928 | 0.4120 | 0.4152 | 0.3717 | 0.4180 |
| 3.0085 | 176 | 0.03 | - | - | - | - | - |
| 3.0256 | 177 | 0.0325 | - | - | - | - | - |
| 3.0427 | 178 | 0.0286 | - | - | - | - | - |
| 3.0598 | 179 | 0.0746 | - | - | - | - | - |
| 3.0769 | 180 | 0.0677 | - | - | - | - | - |
| 3.0940 | 181 | 0.0574 | - | - | - | - | - |
| 3.1111 | 182 | 0.0158 | - | - | - | - | - |
| 3.1282 | 183 | 0.0092 | - | - | - | - | - |
| 3.1453 | 184 | 0.0412 | - | - | - | - | - |
| 3.1624 | 185 | 0.0308 | - | - | - | - | - |
| 3.1795 | 186 | 0.022 | - | - | - | - | - |
| 3.1966 | 187 | 0.0157 | - | - | - | - | - |
| 3.2137 | 188 | 0.0109 | - | - | - | - | - |
| 3.2308 | 189 | 0.0059 | - | - | - | - | - |
| 3.2479 | 190 | 0.0206 | - | - | - | - | - |
| 3.2650 | 191 | 0.0135 | - | - | - | - | - |
| 3.2821 | 192 | 0.0199 | - | - | - | - | - |
| 3.2991 | 193 | 0.0124 | - | - | - | - | - |
| 3.3162 | 194 | 0.0081 | - | - | - | - | - |
| 3.3333 | 195 | 0.0052 | - | - | - | - | - |
| 3.3504 | 196 | 0.006 | - | - | - | - | - |
| 3.3675 | 197 | 0.0074 | - | - | - | - | - |
| 3.3846 | 198 | 0.0085 | - | - | - | - | - |
| 3.4017 | 199 | 0.0273 | - | - | - | - | - |
| 3.4188 | 200 | 0.0363 | - | - | - | - | - |
| 3.4359 | 201 | 0.0077 | - | - | - | - | - |
| 3.4530 | 202 | 0.0046 | - | - | - | - | - |
| 3.4701 | 203 | 0.0067 | - | - | - | - | - |
| 3.4872 | 204 | 0.0054 | - | - | - | - | - |
| 3.5043 | 205 | 0.0055 | - | - | - | - | - |
| 3.5214 | 206 | 0.0052 | - | - | - | - | - |
| 3.5385 | 207 | 0.004 | - | - | - | - | - |
| 3.5556 | 208 | 0.0102 | - | - | - | - | - |
| 3.5726 | 209 | 0.0228 | - | - | - | - | - |
| 3.5897 | 210 | 0.0315 | - | - | - | - | - |
| 3.6068 | 211 | 0.0095 | - | - | - | - | - |
| 3.6239 | 212 | 0.0069 | - | - | - | - | - |
| 3.6410 | 213 | 0.0066 | - | - | - | - | - |
| 3.6581 | 214 | 0.0395 | - | - | - | - | - |
| 3.6752 | 215 | 0.0176 | - | - | - | - | - |
| 3.6923 | 216 | 0.0156 | - | - | - | - | - |
| 3.7094 | 217 | 0.0168 | - | - | - | - | - |
| 3.7265 | 218 | 0.0376 | - | - | - | - | - |
| 3.7436 | 219 | 0.0149 | - | - | - | - | - |
| 3.7607 | 220 | 0.0179 | - | - | - | - | - |
| 3.7778 | 221 | 0.0059 | - | - | - | - | - |
| 3.7949 | 222 | 0.013 | - | - | - | - | - |
| 3.8120 | 223 | 0.0081 | - | - | - | - | - |
| 3.8291 | 224 | 0.0136 | - | - | - | - | - |
| 3.8462 | 225 | 0.0129 | - | - | - | - | - |
| 3.8632 | 226 | 0.0132 | - | - | - | - | - |
| 3.8803 | 227 | 0.0228 | - | - | - | - | - |
| 3.8974 | 228 | 0.0091 | - | - | - | - | - |
| 3.9145 | 229 | 0.0112 | - | - | - | - | - |
| 3.9316 | 230 | 0.0124 | - | - | - | - | - |
| 3.9487 | 231 | 0.0224 | - | - | - | - | - |
| 3.9658 | 232 | 0.0191 | - | - | - | - | - |
| 3.9829 | 233 | 0.0078 | - | - | - | - | - |
| **4.0** | **234** | **0.0145** | **0.3959** | **0.411** | **0.4154** | **0.3741** | **0.4179** |
| 4.0171 | 235 | 0.0089 | - | - | - | - | - |
| 4.0342 | 236 | 0.0157 | - | - | - | - | - |
| 4.0513 | 237 | 0.019 | - | - | - | - | - |
| 4.0684 | 238 | 0.0315 | - | - | - | - | - |
| 4.0855 | 239 | 0.0311 | - | - | - | - | - |
| 4.1026 | 240 | 0.0155 | - | - | - | - | - |
| 4.1197 | 241 | 0.0078 | - | - | - | - | - |
| 4.1368 | 242 | 0.0069 | - | - | - | - | - |
| 4.1538 | 243 | 0.0246 | - | - | - | - | - |
| 4.1709 | 244 | 0.011 | - | - | - | - | - |
| 4.1880 | 245 | 0.0169 | - | - | - | - | - |
| 4.2051 | 246 | 0.0065 | - | - | - | - | - |
| 4.2222 | 247 | 0.0093 | - | - | - | - | - |
| 4.2393 | 248 | 0.0059 | - | - | - | - | - |
| 4.2564 | 249 | 0.0072 | - | - | - | - | - |
| 4.2735 | 250 | 0.0114 | - | - | - | - | - |
| 4.2906 | 251 | 0.0048 | - | - | - | - | - |
| 4.3077 | 252 | 0.0099 | - | - | - | - | - |
| 4.3248 | 253 | 0.0061 | - | - | - | - | - |
| 4.3419 | 254 | 0.005 | - | - | - | - | - |
| 4.3590 | 255 | 0.0077 | - | - | - | - | - |
| 4.3761 | 256 | 0.0057 | - | - | - | - | - |
| 4.3932 | 257 | 0.0106 | - | - | - | - | - |
| 4.4103 | 258 | 0.0176 | - | - | - | - | - |
| 4.4274 | 259 | 0.0085 | - | - | - | - | - |
| 4.4444 | 260 | 0.0059 | - | - | - | - | - |
| 4.4615 | 261 | 0.0063 | - | - | - | - | - |
| 4.4786 | 262 | 0.003 | - | - | - | - | - |
| 4.4957 | 263 | 0.0041 | - | - | - | - | - |
| 4.5128 | 264 | 0.0048 | - | - | - | - | - |
| 4.5299 | 265 | 0.0037 | - | - | - | - | - |
| 4.5470 | 266 | 0.0052 | - | - | - | - | - |
| 4.5641 | 267 | 0.0084 | - | - | - | - | - |
| 4.5812 | 268 | 0.0183 | - | - | - | - | - |
| 4.5983 | 269 | 0.0065 | - | - | - | - | - |
| 4.6154 | 270 | 0.0074 | - | - | - | - | - |
| 4.6325 | 271 | 0.0046 | - | - | - | - | - |
| 4.6496 | 272 | 0.009 | - | - | - | - | - |
| 4.6667 | 273 | 0.01 | - | - | - | - | - |
| 4.6838 | 274 | 0.0158 | - | - | - | - | - |
| 4.7009 | 275 | 0.0077 | - | - | - | - | - |
| 4.7179 | 276 | 0.0259 | - | - | - | - | - |
| 4.7350 | 277 | 0.0204 | - | - | - | - | - |
| 4.7521 | 278 | 0.0155 | - | - | - | - | - |
| 4.7692 | 279 | 0.0101 | - | - | - | - | - |
| 4.7863 | 280 | 0.0062 | - | - | - | - | - |
| 4.8034 | 281 | 0.0065 | - | - | - | - | - |
| 4.8205 | 282 | 0.0115 | - | - | - | - | - |
| 4.8376 | 283 | 0.0088 | - | - | - | - | - |
| 4.8547 | 284 | 0.0157 | - | - | - | - | - |
| 4.8718 | 285 | 0.0145 | - | - | - | - | - |
| 4.8889 | 286 | 0.0122 | - | - | - | - | - |
| 4.9060 | 287 | 0.007 | - | - | - | - | - |
| 4.9231 | 288 | 0.0126 | - | - | - | - | - |
| 4.9402 | 289 | 0.0094 | - | - | - | - | - |
| 4.9573 | 290 | 0.016 | 0.3940 | 0.4131 | 0.4148 | 0.3728 | 0.4182 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.27.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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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