sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:16465 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
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
- generated_from_trainer
- dataset_size:16465
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: '**1. Key Legal Issues and Holdings:**


    * **Postal Ballot and Election Rules:** The case revolves around the interpretation
    of the Conduct of Elections Rules, 1961, specifically Rule 27, which deals with
    the receipt of postal ballots.

    * **Section 59 of the Representation Act:** The main legal issue is the application
    of Section 59 of the Representation of the People Act, 1951, which deals with
    the manner of voting in elections.

    * **Proper Counting of Votes:** The court considered the issue of proper counting
    of votes, including the placement of counting agents and the presence of police
    officials in the counting hall.


    **2. Significant Facts of the Case:**


    * The election was held on 20-5-1991, and the date of counting was initially set
    for 26-5-1991, but was later postponed to 16-6-1991 due to the assassination of
    Shri Rajiv Gandhi.

    * The election petitioner, Shri Ajit Singh, challenged the election on grounds
    of irregularities in the counting of votes, including the improper acceptance
    of postal ballots.

    * The Returning Officer had surrounded the counting hall with high fences and
    placed benches in rows for the election agents to sit, which limited their access
    to the counting tables.

    * The police was present inside the counting hall, and an official video photography
    of the counting process was taken.

    * The complainant, Narinder Singh, made a complaint about irregularities in the
    counting of votes, and six blank ballot papers and three ballots polled in favor
    of the petitioner were found to be wrongly counted.

    * The Chief Counting Agent, Shri N.S. Jadav, made a complaint about the irregularities,
    and the Returning Officer took corrective action.


    **3. Court''s Ruling:**


    * The Supreme Court upheld the decision of the High Court and dismissed the appeal.

    * The court held that the Returning Officer had justification to place police
    officials in the counting hall to prevent disturbances.

    * The court also held that the placement of benches in rows for the election agents
    was necessary to prevent untoward situations developing at the time of counting.

    * The court rejected the contentions of the election petitioner regarding the
    improper acceptance of postal ballots and the lack of access to the counting tables.

    * The court ruled that the postal ballots received after 26-5-1991, but before
    the counting of votes fixed by the Election Commission, could not have been rejected.


    **4. Citations:**


    * **Jitendra Bahadur Singh v. Kirshna Behari**, (1969) 2 SCC 433 : (1970) 1 SCR
    852

    * **Halsbury''s Laws of England**, 4th Edn., Vol. 15, paras 612 and 616, referred
    to'
  sentences:
  - Can a tenant claim automatic purchase rights if they have not complied with specific
    procedural requirements?
  - What are the limitations regarding the locus standi of government officials in
    challenging compensation awards in land acquisition cases?
  - How should irregularities in the counting process, such as the miscounting of
    blank or improperly filled ballots, be addressed by the Returning Officer?
- source_sentence: '**1. Key Legal Issues and Holdings:**


    * **Capital Gains and Exemption:** The case revolves around the exemption of capital
    gains under Section 45 of the Income Tax Act, 1961.

    * **Definition of Capital Asset:** The main legal issue is the interpretation
    of the term "capital asset" under the Income Tax Act, 1961.

    * **Agricultural Land and Capital Assets:** The court considered whether the land
    in question was an agricultural land and, therefore, exempt from capital gains.


    **2. Significant Facts of the Case:**


    * The assessee purchased an extent of 4 acres of land with a hotel building in
    1950 for a consideration of Rs 5.53 lakhs.

    * The land was registered as urban land in the municipal records and urban land
    tax was levied thereon.

    * The assessee constructed two large buildings on the land, which were used for
    non-residential purposes.

    * The land was sold in 1966-67 at the rate of about Rs 260 per sq. yard.

    * The assessee was raising bananas and vegetables on the land until the year of
    sale.

    * The land was situated on Mount Road, Madras, which is the main artery of the
    city and its business centre.


    **3. Court''s Ruling:**


    * The Supreme Court allowed the Revenue''s appeal and set aside the judgment of
    the High Court.

    * The court held that the land in question was not an agricultural land and, therefore,
    not exempt from capital gains.

    * The court considered a totality of the relevant facts and circumstances, including
    the location, physical characteristics, and use of the land.

    * The court held that the mere fact that vegetables were being raised on the land
    was a stop-gap activity and did not change the nature and character of the land.


    **4. Citations:**


    * **Sarifabibi Mohmed Ibrahim v. CIT**, (1993) 204 ITR 631

    * **CIT v. V.A. Trivedi**, (1988) 172 ITR 95

    * **Gordhanbhai Kahandas Dalwadi v. CIT**, (1981) 127 ITR 664 (Guj)

    * **Motibhai D. Patel (Dr) v. CIT**, (1981) 127 ITR 671 (Guj)'
  sentences:
  - What are the legal implications of terminating an employee on probation, and when
    is such a termination considered punitive rather than administrative?
  - What factors do courts consider when determining whether land qualifies as agricultural
    for the purpose of capital gains exemption?
  - In what circumstances can a summary dismissal of an appeal by the High Court affect
    the right of an accused to show cause against their conviction?
- source_sentence: '**1. Key Legal Issues and Holdings:**


    * **Interpretation of "Income"**: The court considers the meaning of "income"
    under the Income Tax Act, 1961, and its implications for taxing income from house
    property.

    * **Constitutionality of Taxation**: The court examines the constitutionality
    of taxing income from house property, particularly under Entry 82 of List I of
    the Seventh Schedule to the Constitution.

    * **Legislative Power**: The court reviews the legislative power of Parliament
    to levy taxes on income, including income from house property.


    **2. Significant Facts of the Case:**


    * The petitioner, Bhagwan Dass Jain, challenged the constitutionality of taxing
    income from house property under Section 23(2) of the Act.

    * The petitioner argued that there is no income in the true sense of the term
    when the property is used for the assessee''s own residence.

    * The respondent, the Union of India, argued that the tax is levied on the presumed
    income from the property, rather than the actual income.

    * The court considered the contemporaneous law relating to tax on incomes in force
    at the time of the Constitution''s enactment.


    **3. Court''s Ruling:**


    * The court held that the word "income" in Entry 82 of List I of the Seventh Schedule
    to the Constitution should be given a wider meaning, encompassing not only monetary
    benefits but also presumed income.

    * The court ruled that the tax under Section 23(2) of the Act is constitutional
    and justified under Entry 82 of List I of the Seventh Schedule to the Constitution.

    * The court rejected the petitioner''s contention that taxing income from house
    property is unconstitutional.


    **4. Citations:**


    * **Navinchandra Mafatlal v. CIT**, (1955) 1 SCR 829: 26 ITR 758: AIR 1955 SC
    58

    * **Resch v. Federal Commissioner of Taxation**, 66 CLR 198-224

    * **Governors of the Rotunda Hospital, Dublin v. Coman**, 7 TC 517, 586-587

    * **D.M. Vakil v. CIT**, (1946) 14 ITR 298: AIR 1946 Bom 350

    * **Sakarlal Balabhai v. ITO**, (1975) 100 ITR 97 (Guj)

    * **Yogi Berra v. Secretary of War**, 251 US 253 (1920)

    * **United States v. Doremus**, 249 US 86 (1919)'
  sentences:
  - What are the implications of exclusion provisions in the Customs Tariff for products
    that may contain impurities?
  - What are the legal implications of a voidable contract in property transactions,
    and how might this affect the enforcement of an agreement to sell?
  - What constitutional challenges can arise regarding the taxation of income from
    house property, and how did the court address these issues in this case?
- source_sentence: '**1. Key Legal Issues and Holdings:**


    * **Dues of Government Company:** The main legal issue is whether the dues of
    a government company are government dues under Section 537(2) of the Companies
    Act, 1956.

    * **Attachment of Property:** The court considered whether an attachment of property
    by a Revenue Recovery Court creates a charge in the property under Section 125
    of the Companies Act, 1956.

    * **Applicability of Special Statutes:** The court held that special statutes,
    such as the Kerala Revenue Recovery Act, 1968, shall prevail over the Companies
    Act, 1956, but only to the extent that they are applicable.

    * **Conflict between Statutes:** The court considered the conflict between the
    Companies Act, 1956, and the Kerala Revenue Recovery Act, 1968, in relation to
    the attachment of property and the creation of a charge.


    **2. Significant Facts of the Case:**


    * The appellant, a government company, provided a loan to M/s Concert Capital
    Limited and its sister concern, M/s Concert Securities Limited.

    * The defaulting companies failed to repay the loan, and a recovery proceeding
    was initiated against them under the Kerala Revenue Recovery Act, 1968.

    * The properties of the defaulting companies were attached, and the appellant
    sought leave to proceed with the sale of the properties.

    * The High Court rejected the appellant''s application, and the Division Bench
    confirmed the decision.


    **3. Court''s Ruling:**


    * The Supreme Court held that an attachment of property by a Revenue Recovery
    Court does not create a charge in the property under Section 125 of the Companies
    Act, 1956.

    * The court also held that the provisions of the Companies Act, 1956, shall apply
    to the recovery proceeding, but only to the extent that they are not inconsistent
    with the special statutes, such as the Kerala Revenue Recovery Act, 1968.

    * The court dismissed the appeal and upheld the decision of the High Court.


    **4. Citations:**


    * **International Coach Builders Ltd. v. Karnataka State Financial Corpn.**, (2003)
    10 SCC 482

    * **Rajasthan State Financial Corpn. v. Official Liquidator**, (2005) 8 SCC 190

    * **ICICI Bank Ltd. v. SIDCO Leathers Ltd.**, (2006) 10 SCC 452

    * **Sardar Govindrao Mahadik v. Devi Sahai**, (1982) 1 SCC 237

    * **Ovation International (India) (P) Ltd., Re**, (1969) 39 Comp Cas 595 (Bom)'
  sentences:
  - What are the implications of Hindu Law on joint family property and the rights
    of family members in cases of property sale?
  - What legal implications arise from the attachment of properties by a Revenue Recovery
    Court concerning the creation of charges under the Companies Act?
  - What are the requirements for a valid gift under Indian law, particularly in relation
    to acceptance and possession?
- source_sentence: '**1. Key Legal Issues and Holdings:**


    * **Construction of a Will:** The main legal issue is the interpretation of the
    will left by Kothandarama Ayyar, a Hindu inhabitant of the district of Tanjore,
    to determine the disposition of his properties.

    * **Adoption and Inheritance:** The case revolves around the application of the
    will''s provisions regarding adoption and inheritance, particularly with regards
    to the properties in dispute.

    * **Construction of Specific Provisions:** The court considered the construction
    of specific provisions in the will, including Paras 5, 13, and other relevant
    paragraphs.


    **2. Significant Facts of the Case:**


    * The testator, Kothandarama Ayyar, died on 25-4-1905, leaving behind his widow,
    Parbati, and two daughters, Nagammal and Gnanambal.

    * The testator executed his last will on 13-3-1905, giving his widow authority
    to adopt a son of Gnanambal or a nephew''s son of the testator.

    * The will provides for the distribution of the testator''s properties among his
    family members and charities.

    * The dispute revolves around the properties in Kothangudi and Injigudi, which
    are mentioned in Paras 5 and 13 of the will.


    **3. Court''s Ruling:**


    * The Supreme Court upheld the construction of the will by the High Court, which
    held that Para 5 of the will was not operative in the present case.

    * The court rejected the argument that Para 5 was meant to be operative only if
    Gnanambal''s son was adopted by the widow.

    * The court held that the testator''s main desire was that his widow should adopt
    the son of his daughter Gnanambal, and that the provisions made for the two daughters,
    the widow, and the adoptive mother were meant to be applicable under all three
    contingencies referred to in the will.

    * The court allowed the appeal, setting aside the judgment and decree of the High
    Court, and restored the judgment and decree of the Subordinate Judge.


    **4. Citations:**


    * **Venkata Narasimha Appa Row v. Parthasarathy Appa Row**, Privy Council

    * **Edwards, In re, Jones v. Jones**, Romer, L.J.

    * **Venkata Narasimha Appa Row v. Parthasarathy Appa Row**, (1913-14) 41 IA 51

    * **Jones v. Jones**, (1906) 1 Ch 570 (CA)'
  sentences:
  - What legal standards govern the determination of seniority between direct recruits
    and promotees in law enforcement agencies in India?
  - How does the U.P. Urban Buildings (Regulation of Letting, Rent & Eviction) Act,
    1972 determine the applicability of rent control laws to newly constructed buildings?
  - In cases involving wills, how do courts balance the testator's intentions with
    the rights of surviving family members?
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.01730103806228374
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5271049596309112
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5547866205305652
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.734717416378316
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.01730103806228374
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1757016532103037
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11095732410611302
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0734717416378316
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.01730103806228374
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5271049596309112
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5547866205305652
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.734717416378316
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.352689074380117
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23119313084711088
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.239821435624779
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.01384083044982699
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5224913494809689
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5501730103806228
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7277970011534025
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.01384083044982699
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17416378316032297
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11003460207612456
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07277970011534025
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.01384083044982699
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5224913494809689
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5501730103806228
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7277970011534025
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3494776306062529
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2289238571245499
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2378257173312991
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **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': True}) with Transformer model: BertModel 
  (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})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Tejasw1/bge-base-case-law-v1")
# Run inference
sentences = [
    "**1. Key Legal Issues and Holdings:**\n\n* **Construction of a Will:** The main legal issue is the interpretation of the will left by Kothandarama Ayyar, a Hindu inhabitant of the district of Tanjore, to determine the disposition of his properties.\n* **Adoption and Inheritance:** The case revolves around the application of the will's provisions regarding adoption and inheritance, particularly with regards to the properties in dispute.\n* **Construction of Specific Provisions:** The court considered the construction of specific provisions in the will, including Paras 5, 13, and other relevant paragraphs.\n\n**2. Significant Facts of the Case:**\n\n* The testator, Kothandarama Ayyar, died on 25-4-1905, leaving behind his widow, Parbati, and two daughters, Nagammal and Gnanambal.\n* The testator executed his last will on 13-3-1905, giving his widow authority to adopt a son of Gnanambal or a nephew's son of the testator.\n* The will provides for the distribution of the testator's properties among his family members and charities.\n* The dispute revolves around the properties in Kothangudi and Injigudi, which are mentioned in Paras 5 and 13 of the will.\n\n**3. Court's Ruling:**\n\n* The Supreme Court upheld the construction of the will by the High Court, which held that Para 5 of the will was not operative in the present case.\n* The court rejected the argument that Para 5 was meant to be operative only if Gnanambal's son was adopted by the widow.\n* The court held that the testator's main desire was that his widow should adopt the son of his daughter Gnanambal, and that the provisions made for the two daughters, the widow, and the adoptive mother were meant to be applicable under all three contingencies referred to in the will.\n* The court allowed the appeal, setting aside the judgment and decree of the High Court, and restored the judgment and decree of the Subordinate Judge.\n\n**4. Citations:**\n\n* **Venkata Narasimha Appa Row v. Parthasarathy Appa Row**, Privy Council\n* **Edwards, In re, Jones v. Jones**, Romer, L.J.\n* **Venkata Narasimha Appa Row v. Parthasarathy Appa Row**, (1913-14) 41 IA 51\n* **Jones v. Jones**, (1906) 1 Ch 570 (CA)",
    "In cases involving wills, how do courts balance the testator's intentions with the rights of surviving family members?",
    'How does the U.P. Urban Buildings (Regulation of Letting, Rent & Eviction) Act, 1972 determine the applicability of rent control laws to newly constructed buildings?',
]
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   | 0.0173     |
| cosine_accuracy@3   | 0.5271     |
| cosine_accuracy@5   | 0.5548     |
| cosine_accuracy@10  | 0.7347     |
| cosine_precision@1  | 0.0173     |
| cosine_precision@3  | 0.1757     |
| cosine_precision@5  | 0.111      |
| cosine_precision@10 | 0.0735     |
| cosine_recall@1     | 0.0173     |
| cosine_recall@3     | 0.5271     |
| cosine_recall@5     | 0.5548     |
| cosine_recall@10    | 0.7347     |
| cosine_ndcg@10      | 0.3527     |
| cosine_mrr@10       | 0.2312     |
| **cosine_map@100**  | **0.2398** |

#### 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   | 0.0138     |
| cosine_accuracy@3   | 0.5225     |
| cosine_accuracy@5   | 0.5502     |
| cosine_accuracy@10  | 0.7278     |
| cosine_precision@1  | 0.0138     |
| cosine_precision@3  | 0.1742     |
| cosine_precision@5  | 0.11       |
| cosine_precision@10 | 0.0728     |
| cosine_recall@1     | 0.0138     |
| cosine_recall@3     | 0.5225     |
| cosine_recall@5     | 0.5502     |
| cosine_recall@10    | 0.7278     |
| cosine_ndcg@10      | 0.3495     |
| cosine_mrr@10       | 0.2289     |
| **cosine_map@100**  | **0.2378** |

<!--
## 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

#### json

* Dataset: json
* Size: 16,465 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                              |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                                |
  | details | <ul><li>min: 2 tokens</li><li>mean: 26.38 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 333 tokens</li><li>mean: 490.59 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What factors do courts consider when evaluating the reliability of eyewitness testimonies in murder trials?</code>                                | <code>**1. Key Legal Issues and Holdings:**<br><br>* **Culpable Homicide:** The court considered the application of Section 302 of the Indian Penal Code, 1860, which deals with punishment for culpable homicide not amounting to murder.<br>* **Section 302 IPC:** The court upheld the conviction of the accused under Section 302 IPC for the death of Ishwardeen.<br>* **Section 34 IPC:** The court also upheld the conviction of the accused under Section 34 IPC for the death of Ishwardeen, considering the common intention of the accused.<br><br>**2. Significant Facts of the Case:**<br><br>* The deceased, Ishwardeen, was killed in a alleged incident involving the accused, Bhagwan Das, Sheo Mohan, and Shanker @ Sheo Shanker.<br>* The incident occurred on August 18, 1983, at the house of Bhagwan Das, where Ishwardeen and his family were residing.<br>* The accused had allegedly demanded rent from Ishwardeen and had threatened to evict him from the house.<br>* Ishwardeen was killed by the accused, who allegedly gave him 8-9 knife blows.<br>* The case was registered under Section 307 IPC, but it was later converted to Section 302 IPC after Ishwardeen's death.<br><br>**3. Court's Ruling:**<br><br>* The High Court upheld the conviction of Bhagwan Das, Sheo Mohan, and Shanker @ Sheo Shanker under Section 302 IPC and Section 34 IPC.<br>* The court rejected the defense argument that the testimony of the eyewitnesses, Kamla Devi and Subhash, could not be relied upon.<br>* The court relied on the testimony of the eyewitnesses, which was corroborated by the medical evidence.<br>* The court also relied on the post-mortem report, which showed that the antemortem injuries were possible to be caused by a knife at around 9:00 p.m. on August 18, 1983.<br>* The court held that the accused had a common intention to commit the murder of Ishwardeen, and therefore, the conviction under Section 302 IPC with the aid of Section 34 IPC was upheld.<br>* The court also rejected the defense argument that the accused had no motive to commit the murder, and held that the presence of semi-digested food in the stomach of the deceased did not contradict the prosecution's case.<br><br>**4. Citations:**<br><br>* **Dalip Singh v. State of Punjab**<br>* **Section 302 IPC**<br>* **Section 34 IPC**<br>* **Thaman Kumar v. State of Union Territory of Chandigarh**<br>* **State of H.P. v. Jeet Singh**<br>* **Appa Bhat v. State of Gujarat**<br>* **Krishna Mochi v. State of Bihar**<br>* **Israr v. State of U.P.**<br>* **Gali Venkataiah v. State of A.P.**<br>* **Masalti v. State of U.P.**<br>* **Vadivelu Thevar v. State of Madras**<br>* **Galivenkataiah v. State of A.P.**</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
  | <code>What principles guide the court's decisions on wage fixation in cases involving government undertakings?</code>                                   | <code>**1. Key Legal Issues and Holdings:**<br><br>* **Wage Structure:** The main legal issue is whether the wage structure of a government undertaking in the public sector should be different from that of an undertaking in the private sector.<br>* **Section 10(1)(d) of the Industrial Disputes Act, 1947:** The court considered the applicability of this provision in the context of wage fixation.<br>* **Article 39 and 43 of the Directive Principles of State Policy:** The court examined the constitutional implications of making a distinction between laborers in the public and private sectors.<br>* **Region-cum-Industry Principle:** The court upheld the principle of region-cum-industry in wage fixation.<br>* **Gratuity Scheme:** The court considered the validity of the gratuity scheme introduced by the Tribunal, which provided for payment of gratuity to workmen upon retirement or termination of service.<br>* **Fair Wages:** The court examined the issue of fair wages and whether the Tribunal had violated the accepted norms in fixing the wage structure and dearness allowance.<br>* **Retirement Age:** The court held that the retirement age of the employees should be raised to 60 years, and the discretion to continue employees beyond 58 years should be removed.<br><br>**2. Significant Facts of the Case:**<br><br>* The case involves a government undertaking, Hindustan Antibiotics Limited, and its workmen.<br>* The company is a limited one, but it is financed entirely by the Central Government.<br>* The workmen presented a charter of fifteen demands to the company, including demands related to wage fixation.<br>* The Industrial Tribunal, Bombay, made an award dated October 8, 1963, which was challenged by the company and the workmen.<br>* The company argued that the wage structure in the public sector should be different from that in the private sector.<br>* The workmen argued that the character of the employer is irrelevant in wage fixation.<br><br>**3. Court's Ruling:**<br><br>* The court upheld the principle of region-cum-industry in wage fixation.<br>* The court rejected the argument that the character of the employer is relevant in determining wage structures.<br>* The court held that the distinction between public and private sectors is not justified in wage fixation.<br>* The court also noted that the constitutional directive principles of equal pay for equal work and a living wage for all workers should be followed.<br>* The court validated the gratuity scheme introduced by the Tribunal.<br>* The court rejected the contention that the Tribunal had violated the accepted norms in fixing the wage structure and dearness allowance.<br>* The court raised the retirement age of employees to 60 years and removed the discretion to continue employees beyond 58 years.<br>* The court dismissed the appeals by the company and the workmen, except for modifying the award regarding the age of retirement.<br><br>**4. Citations:**<br><br>* **Crown Aluminium Works v. Workmen**<br>* **Express Newspapers (Private) Ltd. v. Union of India**<br>* **French Motor Car Co. Ltd. v. Workmen**<br>* **Hindustan Times Ltd., New Delhi v. Workmen**<br>* Other relevant cases and reports cited in the judgment.</code> |
  | <code>- What role does the recommendation of a State Government play in the land exemption process under the Punjab Towns Improvement Act, 1922?</code> | <code>**1. Key Legal Issues and Holdings:**<br><br>* **Acquisition of Land:** The main legal issue is the acquisition of land by the Improvement Trust under the Punjab Towns Improvement Act, 1922.<br>* **Exemption of Orchards:** The court considered whether the Improvement Trust was bound to exempt orchards from acquisition under Section 56 of the Act.<br>* **Article 14 of the Constitution:** The court held that the Improvement Trust did not violate Article 14 of the Constitution by exempting some orchards while acquiring others.<br>* **Quasi-Judicial Capacity:** The court held that the Improvement Trust acts in a quasi-judicial capacity when determining claims under Section 56 of the Act, but is not bound to give reasons for its decisions.<br>* **Locus Standi:** The court observed that the appellants had no locus standi to invoke Section 56 of the Act, as the acquisition of their land had not been discovered to be unnecessary for the execution of the scheme.<br>* **Power to Exempt Lands:** The court held that the Improvement Trust did not possess the power to exempt lands from the scheme under Section 56 of the Act.<br><br>**2. Significant Facts of the Case:**<br><br>* The Improvement Trust framed a development scheme in 1961 under Section 24 read with Section 28(2) of the Act.<br>* The scheme covered an area of approximately 128 acres, and the Trust acquired the land, including the appellants' land, in 1964.<br>* The appellants applied to the State Government for exempting their land from acquisition on the ground that it contained a fully developed orchard.<br>* The State Government recommended their case to the Chairman of the Improvement Trust, but the Trust refused to exempt their land.<br>* The appellants claimed that the Trust had exempted similar orchards of other persons, and that this was a violation of Article 14 of the Constitution.<br><br>**3. Court's Ruling:**<br><br>* The High Court initially allowed the appellants' writ petition, directing the Trust to allow them a full opportunity of hearing regarding their case for exemption.<br>* The Trust refused to exempt their land, and the appellants appealed to the High Court.<br>* The High Court dismissed the appeal, holding that the Trust had given reasons for its decision and that the appellants had not shown how their land was unnecessary for the execution of the scheme.<br>* The Supreme Court dismissed the appeal, holding that the Improvement Trust did not violate Article 14 of the Constitution by exempting some orchards while acquiring others.<br><br>**4. Citations:**<br><br>* **Punjab Towns Improvement Act, 1922**<br>* **Article 14 of the Constitution of India**<br>* **Section 56 of the Punjab Towns Improvement Act, 1922**<br>* **Section 24 read with Section 28(2) of the Punjab Towns Improvement Act, 1922**<br>* **Section 43 of the Punjab Towns Improvement Act, 1922**</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
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `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`: 16
- `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
- `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`: 4
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch      | Step    | Training Loss | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|
| 0.0777     | 10      | 1.58          | -                      | -                      |
| 0.1553     | 20      | 1.0799        | -                      | -                      |
| 0.2330     | 30      | 0.6653        | -                      | -                      |
| 0.3107     | 40      | 0.4524        | -                      | -                      |
| 0.3883     | 50      | 0.3962        | -                      | -                      |
| 0.4660     | 60      | 0.3472        | -                      | -                      |
| 0.5437     | 70      | 0.3481        | -                      | -                      |
| 0.6214     | 80      | 0.3034        | -                      | -                      |
| 0.6990     | 90      | 0.3612        | -                      | -                      |
| 0.7767     | 100     | 0.2497        | -                      | -                      |
| 0.8544     | 110     | 0.2424        | -                      | -                      |
| 0.9320     | 120     | 0.3037        | -                      | -                      |
| **0.9942** | **128** | **-**         | **0.2359**             | **0.2435**             |
| 1.0097     | 130     | 0.2795        | -                      | -                      |
| 1.0874     | 140     | 0.2519        | -                      | -                      |
| 1.1650     | 150     | 0.2414        | -                      | -                      |
| 1.2427     | 160     | 0.1837        | -                      | -                      |
| 1.3204     | 170     | 0.1734        | -                      | -                      |
| 1.3981     | 180     | 0.1462        | -                      | -                      |
| 1.4757     | 190     | 0.1593        | -                      | -                      |
| 1.5534     | 200     | 0.1648        | -                      | -                      |
| 1.6311     | 210     | 0.1593        | -                      | -                      |
| 1.7087     | 220     | 0.1737        | -                      | -                      |
| 1.7864     | 230     | 0.1237        | -                      | -                      |
| 1.8641     | 240     | 0.1205        | -                      | -                      |
| 1.9417     | 250     | 0.1611        | -                      | -                      |
| 1.9961     | 257     | -             | 0.2376                 | 0.2424                 |
| 2.0194     | 260     | 0.1674        | -                      | -                      |
| 2.0971     | 270     | 0.135         | -                      | -                      |
| 2.1748     | 280     | 0.1464        | -                      | -                      |
| 2.2524     | 290     | 0.1119        | -                      | -                      |
| 2.3301     | 300     | 0.089         | -                      | -                      |
| 2.4078     | 310     | 0.0774        | -                      | -                      |
| 2.4854     | 320     | 0.1039        | -                      | -                      |
| 2.5631     | 330     | 0.1218        | -                      | -                      |
| 2.6408     | 340     | 0.1001        | -                      | -                      |
| 2.7184     | 350     | 0.1072        | -                      | -                      |
| 2.7961     | 360     | 0.0774        | -                      | -                      |
| 2.8738     | 370     | 0.0855        | -                      | -                      |
| 2.9515     | 380     | 0.1096        | -                      | -                      |
| 2.9981     | 386     | -             | 0.2402                 | 0.2381                 |
| 3.0291     | 390     | 0.1076        | -                      | -                      |
| 3.1068     | 400     | 0.1019        | -                      | -                      |
| 3.1845     | 410     | 0.1139        | -                      | -                      |
| 3.2621     | 420     | 0.0732        | -                      | -                      |
| 3.3398     | 430     | 0.0831        | -                      | -                      |
| 3.4175     | 440     | 0.0613        | -                      | -                      |
| 3.4951     | 450     | 0.092         | -                      | -                      |
| 3.5728     | 460     | 0.0891        | -                      | -                      |
| 3.6505     | 470     | 0.0896        | -                      | -                      |
| 3.7282     | 480     | 0.0861        | -                      | -                      |
| 3.8058     | 490     | 0.0743        | -                      | -                      |
| 3.8835     | 500     | 0.077         | -                      | -                      |
| 3.9612     | 510     | 0.1056        | -                      | -                      |
| 3.9767     | 512     | -             | 0.2393                 | 0.2393                 |
| 0.0777     | 10      | 0.3691        | -                      | -                      |
| 0.1553     | 20      | 0.3126        | -                      | -                      |
| 0.2330     | 30      | 0.279         | -                      | -                      |
| 0.3107     | 40      | 0.2477        | -                      | -                      |
| 0.3883     | 50      | 0.2436        | -                      | -                      |
| 0.4660     | 60      | 0.2307        | -                      | -                      |
| 0.5437     | 70      | 0.2487        | -                      | -                      |
| 0.6214     | 80      | 0.2463        | -                      | -                      |
| 0.6990     | 90      | 0.2965        | -                      | -                      |
| 0.7767     | 100     | 0.2101        | -                      | -                      |
| 0.8544     | 110     | 0.1999        | -                      | -                      |
| 0.9320     | 120     | 0.2561        | -                      | -                      |
| **0.9942** | **128** | **-**         | **0.2399**             | **0.242**              |
| 1.0097     | 130     | 0.2504        | -                      | -                      |
| 1.0874     | 140     | 0.246         | -                      | -                      |
| 1.1650     | 150     | 0.2043        | -                      | -                      |
| 1.2427     | 160     | 0.171         | -                      | -                      |
| 1.3204     | 170     | 0.1499        | -                      | -                      |
| 1.3981     | 180     | 0.1402        | -                      | -                      |
| 1.4757     | 190     | 0.1379        | -                      | -                      |
| 1.5534     | 200     | 0.156         | -                      | -                      |
| 1.6311     | 210     | 0.1669        | -                      | -                      |
| 1.7087     | 220     | 0.1578        | -                      | -                      |
| 1.7864     | 230     | 0.1157        | -                      | -                      |
| 1.8641     | 240     | 0.1279        | -                      | -                      |
| 1.9417     | 250     | 0.1766        | -                      | -                      |
| 1.9961     | 257     | -             | 0.2386                 | 0.2410                 |
| 2.0194     | 260     | 0.1693        | -                      | -                      |
| 2.0971     | 270     | 0.1424        | -                      | -                      |
| 2.1748     | 280     | 0.1517        | -                      | -                      |
| 2.2524     | 290     | 0.1151        | -                      | -                      |
| 2.3301     | 300     | 0.0974        | -                      | -                      |
| 2.4078     | 310     | 0.083         | -                      | -                      |
| 2.4854     | 320     | 0.1021        | -                      | -                      |
| 2.5631     | 330     | 0.1305        | -                      | -                      |
| 2.6408     | 340     | 0.1102        | -                      | -                      |
| 2.7184     | 350     | 0.1118        | -                      | -                      |
| 2.7961     | 360     | 0.089         | -                      | -                      |
| 2.8738     | 370     | 0.1111        | -                      | -                      |
| 2.9515     | 380     | 0.145         | -                      | -                      |
| 2.9981     | 386     | -             | 0.2372                 | 0.2400                 |
| 3.0291     | 390     | 0.1115        | -                      | -                      |
| 3.1068     | 400     | 0.1036        | -                      | -                      |
| 3.1845     | 410     | 0.1164        | -                      | -                      |
| 3.2621     | 420     | 0.0728        | -                      | -                      |
| 3.3398     | 430     | 0.0879        | -                      | -                      |
| 3.4175     | 440     | 0.0657        | -                      | -                      |
| 3.4951     | 450     | 0.0825        | -                      | -                      |
| 3.5728     | 460     | 0.0986        | -                      | -                      |
| 3.6505     | 470     | 0.1074        | -                      | -                      |
| 3.7282     | 480     | 0.0923        | -                      | -                      |
| 3.8058     | 490     | 0.078         | -                      | -                      |
| 3.8835     | 500     | 0.0962        | -                      | -                      |
| 3.9612     | 510     | 0.1078        | -                      | -                      |
| 3.9767     | 512     | -             | 0.2378                 | 0.2398                 |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.20.1

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->