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--- |
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base_model: sentence-transformers/paraphrase-MiniLM-L3-v2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:2000 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: However, this Court will determine that there was sufficient evidence |
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to sustain the jury's verdict if the evidence was "of such quality and weight |
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that, having in mind the beyond a reasonable doubt burden of proof standard, reasonable |
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fair-minded men in the exercise of impartial judgment might reach different conclusions |
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on every element of the offense." |
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sentences: |
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- This Court will determine if there was enough evidence to support the jury's verdict |
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by considering whether reasonable people could have reached different conclusions |
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based on the evidence presented. |
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- The VA psychiatrist believed that the Veteran was likely to have PTSD as a direct |
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result of the attack on him during his military service in Korea. |
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- The Veteran started seeing a mental health specialist at the VA on a regular basis. |
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- source_sentence: Under such circumstances, VA is required to prove by clear and |
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unmistakable evidence that a disease or injury manifesting in service both preexisted |
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service and was not aggravated by service. |
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sentences: |
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- The independent mental health expert offered a comprehensive account of the Veteran's |
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mental health issues, service-related impairments, and previous psychiatric and |
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medical treatment experiences. |
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- At the trial, the prosecution failed to provide a search warrant, which was not |
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explained or justified. |
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- In order to establish that a disease or injury did not arise from service, VA |
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must provide clear and convincing evidence that the condition existed prior to |
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military service and was not exacerbated by service. |
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- source_sentence: Evidence of behavior changes following the claimed assault is one |
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type of relevant evidence that may be found in these sources. |
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sentences: |
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- The independent medical clinician comprehensively documented the impact of the |
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Veteran's alleged condition on their functional abilities. |
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- A range of behavioral indicators, including alterations in demeanor, speech patterns, |
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and physical reactions, can serve as valuable evidence in support of allegations |
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of assault. |
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- He claims that his mental health issues, which have been diagnosed as various |
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psychiatric disorders, are a result of the trauma he experienced during his deployment |
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to a combat zone in Vietnam while stationed in Japan in 1974. |
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- source_sentence: The court held Apple had not made the requisite showing of likelihood |
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of success on the merits because it “concluded that there is some doubt as to |
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the copyrightability of the programs described in this litigation.” |
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sentences: |
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- The trial court committed a series of errors in this case, including failing to |
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instruct the jury on an essential element of felonious damage to computers, denying |
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the defendant's motion to dismiss, and entering judgment on a fatally flawed indictment. |
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- The court determined that Apple had not provided sufficient evidence to demonstrate |
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a likelihood of success on the merits, as it had "raised some doubts about the |
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copyrightability of the programs in question." |
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- The Veteran believes that she should be granted service connection for chronic |
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PTSD or other psychiatric disorder because she has been diagnosed with chronic |
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PTSD as a result of several stressful events that occurred during her periods |
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of active duty and active duty for training with the Army National Guard. |
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- source_sentence: In contrast, the scope of punishable conduct under the instant |
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statute is limited by the individual's specified intent to "haras[s]" by communicating |
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a "threat" so as to "engage in a knowing and willful course of conduct" directed |
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at the victim that "alarms, torments, or terrorizes" the victim. |
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sentences: |
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- The scope of punishable conduct under the statute is limited to the individual's |
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intent to harass by communicating a threat so as to engage in a knowing and willful |
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course of conduct directed at the victim that alarms, torments, or terrorizes |
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the victim. |
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- The Veteran has been diagnosed with both major depressive disorder and PTSD. |
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- The trial court's decision on an anti-SLAPP motion is subject to de novo review. |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L3-v2 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli dev |
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type: all-nli-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 1.0 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.0 |
|
name: Dot Accuracy |
|
- type: manhattan_accuracy |
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value: 1.0 |
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name: Manhattan Accuracy |
|
- type: euclidean_accuracy |
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value: 1.0 |
|
name: Euclidean Accuracy |
|
- type: max_accuracy |
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value: 1.0 |
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name: Max Accuracy |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli test |
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type: all-nli-test |
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metrics: |
|
- type: cosine_accuracy |
|
value: 1.0 |
|
name: Cosine Accuracy |
|
- type: dot_accuracy |
|
value: 0.0 |
|
name: Dot Accuracy |
|
- type: manhattan_accuracy |
|
value: 1.0 |
|
name: Manhattan Accuracy |
|
- type: euclidean_accuracy |
|
value: 1.0 |
|
name: Euclidean Accuracy |
|
- type: max_accuracy |
|
value: 1.0 |
|
name: Max Accuracy |
|
- type: cosine_accuracy |
|
value: 1.0 |
|
name: Cosine Accuracy |
|
- type: dot_accuracy |
|
value: 0.0 |
|
name: Dot Accuracy |
|
- type: manhattan_accuracy |
|
value: 1.0 |
|
name: Manhattan Accuracy |
|
- type: euclidean_accuracy |
|
value: 1.0 |
|
name: Euclidean Accuracy |
|
- type: max_accuracy |
|
value: 1.0 |
|
name: Max Accuracy |
|
--- |
|
|
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# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L3-v2 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2). 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. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) <!-- at revision 54825a6a5a83f5d98d318ba2a11bfd31eb906f06 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("justArmenian/legal_paraphrase") |
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# Run inference |
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sentences = [ |
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'In contrast, the scope of punishable conduct under the instant statute is limited by the individual\'s specified intent to "haras[s]" by communicating a "threat" so as to "engage in a knowing and willful course of conduct" directed at the victim that "alarms, torments, or terrorizes" the victim.', |
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"The scope of punishable conduct under the statute is limited to the individual's intent to harass by communicating a threat so as to engage in a knowing and willful course of conduct directed at the victim that alarms, torments, or terrorizes the victim.", |
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'The Veteran has been diagnosed with both major depressive disorder and PTSD.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Triplet |
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* Dataset: `all-nli-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:--------| |
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| cosine_accuracy | 1.0 | |
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| dot_accuracy | 0.0 | |
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| manhattan_accuracy | 1.0 | |
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| euclidean_accuracy | 1.0 | |
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| **max_accuracy** | **1.0** | |
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|
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#### Triplet |
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* Dataset: `all-nli-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
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| Metric | Value | |
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|:-------------------|:--------| |
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| cosine_accuracy | 1.0 | |
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| dot_accuracy | 0.0 | |
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| manhattan_accuracy | 1.0 | |
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| euclidean_accuracy | 1.0 | |
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| **max_accuracy** | **1.0** | |
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|
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#### Triplet |
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* Dataset: `all-nli-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
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| Metric | Value | |
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|:-------------------|:--------| |
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| cosine_accuracy | 1.0 | |
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| dot_accuracy | 0.0 | |
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| manhattan_accuracy | 1.0 | |
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| euclidean_accuracy | 1.0 | |
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| **max_accuracy** | **1.0** | |
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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|
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### Training Dataset |
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#### Unnamed Dataset |
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|
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* Size: 2,000 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 36.01 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 31.41 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 31.39 tokens</li><li>max: 99 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>The weight of the competent and probative medical opinions of record is against finding that the Veteran has a current diagnosis of PTSD or any other chronic acquired psychiatric disorder which is related to her military service.</code> | <code>The weight of the credible and persuasive medical evidence on record suggests that the Veteran does not currently suffer from PTSD or any other chronic psychiatric condition related to her military service.</code> | <code>It is evident that an unauthorized physical intrusion would have been deemed a "search" under the Fourth Amendment when it was originally formulated.</code> | |
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| <code>We have no doubt that such a physical intrusion would have been considered a “search” within the meaning of the Fourth Amendment when it was adopted.</code> | <code>It is evident that an unauthorized physical intrusion would have been deemed a "search" under the Fourth Amendment when it was originally formulated.</code> | <code>In June 1972, the Veteran's condition was assessed by the Army Medical Board, which concluded that the Veteran's back condition made him unfit for active service, leading to his discharge from the military.</code> | |
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| <code>Later in June 1972, the Veteran's condition was evaluated by the Army Medical Board, where it was determined that the Veteran's back condition rendered him physically unfit for active service, and he was subsequently discharged from service.</code> | <code>In June 1972, the Veteran's condition was assessed by the Army Medical Board, which concluded that the Veteran's back condition made him unfit for active service, leading to his discharge from the military.</code> | <code>The court has granted a petition for a writ of certiorari to review a decision made by the Court of Appeal of California, Fourth Appellate District, Division One.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
|
|
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### Evaluation Dataset |
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|
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#### Unnamed Dataset |
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|
|
|
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* Size: 500 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | negative | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 35.69 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 32.11 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 32.12 tokens</li><li>max: 77 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>(Virginia v. Black, supra, 538 U.S. at p. 347.)</code> | <code>The Black Court asserted that the "vagueness doctrine is a safeguard against the arbitrary exercise of power by government officials."</code> | <code>This Court will determine if there was enough evidence to support the jury's verdict by considering whether reasonable people could have reached different conclusions based on the evidence presented.</code> | |
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| <code>However, this Court will determine that there was sufficient evidence to sustain the jury's verdict if the evidence was "of such quality and weight that, having in mind the beyond a reasonable doubt burden of proof standard, reasonable fair-minded men in the exercise of impartial judgment might reach different conclusions on every element of the offense."</code> | <code>This Court will determine if there was enough evidence to support the jury's verdict by considering whether reasonable people could have reached different conclusions based on the evidence presented.</code> | <code>The VA psychiatrist believed that the Veteran was likely to have PTSD as a direct result of the attack on him during his military service in Korea.</code> | |
|
| <code>This VA psychiatrist opined that the Veteran had PTSD more likely than not to be the direct result of the attack on him during service in Korea.</code> | <code>The VA psychiatrist believed that the Veteran was likely to have PTSD as a direct result of the attack on him during his military service in Korea.</code> | <code>She noted that the Veteran's greatest source of stress was caring for their adult child without any assistance.</code> | |
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
|
- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
|
- `tf32`: None |
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- `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 |
|
- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |
|
|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:| |
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| 0 | 0 | - | - | 1.0 | - | |
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| 0.08 | 10 | 0.1402 | 0.0759 | 1.0 | - | |
|
| 0.16 | 20 | 0.0873 | 0.0726 | 1.0 | - | |
|
| 0.24 | 30 | 0.0992 | 0.0677 | 1.0 | - | |
|
| 0.32 | 40 | 0.0759 | 0.0651 | 1.0 | - | |
|
| 0.4 | 50 | 0.0355 | 0.0652 | 1.0 | - | |
|
| 0.48 | 60 | 0.0814 | 0.0666 | 1.0 | - | |
|
| 0.56 | 70 | 0.0353 | 0.0677 | 1.0 | - | |
|
| 0.64 | 80 | 0.1404 | 0.0677 | 1.0 | - | |
|
| 0.72 | 90 | 0.0336 | 0.0664 | 1.0 | - | |
|
| 0.8 | 100 | 0.0559 | 0.0661 | 1.0 | - | |
|
| 0.88 | 110 | 0.0484 | 0.0654 | 1.0 | - | |
|
| 0.96 | 120 | 0.0522 | 0.0650 | 1.0 | - | |
|
| 1.0 | 125 | - | - | - | 1.0 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
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### 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}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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