
# similarity-sentences-spanish (SSS)

### Dataset Summary

This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.

The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.

The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts.

| **Source** | **Share (rows)** | **Count (rows)** | **Score (avg)** |
|-----------|-----------------|------------------|----------------|
| GPT       | 22.71 %         | 3982             | 0.50           |
| STBS      | 49.21 %         | 8628             | 0.53           |
| STS       | 17.69 %         | 3102             | 0.42           |
| SICK      | 10.38 %         | 1820             | 0.51           |
| **Total** | 100%            | 17532            | 0.49           |

### Objective

The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes.

### Languages

Spanish

## Dataset Structure

### Data Fields

- Sentence 1: The first sentence to be compared.

- Sentence 2: The second sentence to be compared.

- Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.

- Source: The source of the information, represented by its abbreviation.

## Dataset Biases

This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3.

### Source Data

The dataset was created using the following sources:

1. Already existing datasets:
   - STS-ES ([STSB](https://huggingface.co/datasets/stsb_multi_mt))
   - STSB-Multi-MT ([STS](https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es))

2. Newly generated data:
   - Chat GPT-3: The sentences were generated using Chat GPT-3 for various general topics.

The dataset includes sentences from various themes, such as:
- Alimentación y Cocina (Food and Cooking)
- Arte y Cultura (Art and Culture)
- Ciencia y Tecnología (Science and Technology)
- Cine y Televisión (Film and Television)
- Deportes (Sports)
- Economía (Economy)
- Educación (Education)
- Estadística (Statistics)
- Filosofía (Philosophy)
- Finanzas (Finance)
- Historia (History)
- Literatura (Literature)
- Medicina (Medicine)
- Medio Ambiente y Sostenibilidad (Environment and Sustainability)
- Moda y Estilo (Fashion and Style)
- Música (Music)
- Organizacional (Organizational)
- Política y Gobierno (Politics and Government)
- Psicología (Psychology)
- Religión y Espiritualidad (Religion and Spirituality)
- Salud y Bienestar (Health and Wellness)

Please note that these themes are not exhaustive.

The prompts for each label (score) are as follows:

```python
descripciones_similaridad = {
    "0.0": "Rewrite the following sentence in a new sentence about a completely different topic, without any apparent connection to the original sentence. The two sentences must be completely distinct and should not share any thematic similarity.",
    "0.1": "Rewrite the following sentence in a new sentence about a topic completely different from the original sentence. Make sure the two sentences are entirely different and do not share any thematic similarity. At least 90% of the information level should change.",
    "0.2": "Rewrite the following sentence in a new sentence about the same topic as the original sentence, but not an exact copy. You can express different ideas, but the general theme should be similar. Ensure at least 80% of the information level is different.",
    "0.3": "Rewrite the following sentence in a new sentence about a topic related to the original sentence, though not equivalent. Both sentences must share a common theme or general idea, but they can express different viewpoints. At least 70% of the information level should change.",
    "0.4": "Rewrite the following sentence in a new sentence that is not equivalent to the original, but has some similar details or elements. Ensure at least 60% of the information level is different.",
    "0.5": "Rewrite the following sentence in a new sentence that is not equivalent to the original, but is related to some extent. Both sentences should have some details in common and be thematically related at least 50% of the information level.",
    "0.6": "Rewrite the following sentence in a new sentence that is approximately equivalent to the original, but may differ in important information or have certain missing elements. The changes should slightly affect the meaning, and at least 60% of the information level should be preserved.",
    "0.7": "Rewrite the following sentence in a new sentence that is approximately equivalent to the original, but may differ in important information or have some missing elements. Ensure at least 70% of the information level remains the same.",
    "0.8": "Rewrite the following sentence in a new sentence that is mostly equivalent to the original, but may differ in some unimportant details. The changes should affect a maximum of 20% of the information level.",
    "0.9": "Rewrite the following sentence in a new sentence that is nearly equivalent to the original, but may have some differences in minor details that do not significantly impact its meaning. The changes should affect a maximum of 10% of the information level.",
    "1.0": "Rewrite the following sentence in a new sentence that is completely equivalent to the original, as they express exactly the same idea or meaning. The two sentences must share 100% of the information level.",
}
```

- SICK ([SICK Dataset](https://huggingface.co/datasets/sick))


The dataset also includes translated and sampled sentences from the SICK dataset  using Helsinki ([helsinki - EN -ES](https://huggingface.co/datasets/sick))  as the translation tool to achieve an average score close to 0.5 with the entire dataset.

To maintain a balanced representation and avoid excessive prominence of translated data that was not originally written in Spanish and has not been reviewed in Spanish, the intention is to have scores generally centered around 0.5.
task_categories:sentence-similarity size_categories:10K<n<100K language:es license:mit region:us
{"language": ["es"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["sentence-similarity"], "pretty_name": "SimilaritySpanishDataset"}
TAGS
#task_categories-sentence-similarity #size_categories-10K<n<100K #language-Spanish #license-mit #region-us 

similarity-sentences-spanish (SSS)
==================================


### Dataset Summary


This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.


The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.


The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts.



### Objective


The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes.


### Languages


Spanish


Dataset Structure
-----------------


### Data Fields


* Sentence 1: The first sentence to be compared.
* Sentence 2: The second sentence to be compared.
* Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
* Source: The source of the information, represented by its abbreviation.


Dataset Biases
--------------


This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3.


### Source Data


The dataset was created using the following sources:


1. Already existing datasets:


	* STS-ES (STSB)
	* STSB-Multi-MT (STS)
2. Newly generated data:


	* Chat GPT-3: The sentences were generated using Chat GPT-3 for various general topics.


The dataset includes sentences from various themes, such as:


* Alimentación y Cocina (Food and Cooking)
* Arte y Cultura (Art and Culture)
* Ciencia y Tecnología (Science and Technology)
* Cine y Televisión (Film and Television)
* Deportes (Sports)
* Economía (Economy)
* Educación (Education)
* Estadística (Statistics)
* Filosofía (Philosophy)
* Finanzas (Finance)
* Historia (History)
* Literatura (Literature)
* Medicina (Medicine)
* Medio Ambiente y Sostenibilidad (Environment and Sustainability)
* Moda y Estilo (Fashion and Style)
* Música (Music)
* Organizacional (Organizational)
* Política y Gobierno (Politics and Government)
* Psicología (Psychology)
* Religión y Espiritualidad (Religion and Spirituality)
* Salud y Bienestar (Health and Wellness)


Please note that these themes are not exhaustive.


The prompts for each label (score) are as follows:


* SICK (SICK Dataset)


The dataset also includes translated and sampled sentences from the SICK dataset using Helsinki (helsinki - EN -ES) as the translation tool to achieve an average score close to 0.5 with the entire dataset.


To maintain a balanced representation and avoid excessive prominence of translated data that was not originally written in Spanish and has not been reviewed in Spanish, the intention is to have scores generally centered around 0.5.



### Dataset Summary


This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.


The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.


The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts. ### Objective


The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes. ### Languages


Spanish


Dataset Structure
----------------- ### Data Fields


* Sentence 1: The first sentence to be compared.
* Sentence 2: The second sentence to be compared.
* Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
* Source: The source of the information, represented by its abbreviation.


Dataset Biases
--------------


This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3. ### Source Data


The dataset was created using the following sources:


1. Already existing datasets:


	* STS-ES (STSB)
	* STSB-Multi-MT (STS)
2. Newly generated data:


	* Chat GPT-3: The sentences were generated using Chat GPT-3 for various general topics.


The dataset includes sentences from various themes, such as:


* Alimentación y Cocina (Food and Cooking)
* Arte y Cultura (Art and Culture)
* Ciencia y Tecnología (Science and Technology)
* Cine y Televisión (Film and Television)
* Deportes (Sports)
* Economía (Economy)
* Educación (Education)
* Estadística (Statistics)
* Filosofía (Philosophy)
* Finanzas (Finance)
* Historia (History)
* Literatura (Literature)
* Medicina (Medicine)
* Medio Ambiente y Sostenibilidad (Environment and Sustainability)
* Moda y Estilo (Fashion and Style)
* Música (Music)
* Organizacional (Organizational)
* Política y Gobierno (Politics and Government)
* Psicología (Psychology)
* Religión y Espiritualidad (Religion and Spirituality)
* Salud y Bienestar (Health and Wellness)


Please note that these themes are not exhaustive.


The prompts for each label (score) are as follows:


* SICK (SICK Dataset)


The dataset also includes translated and sampled sentences from the SICK dataset using Helsinki (helsinki - EN -ES) as the translation tool to achieve an average score close to 0.5 with the entire dataset.


To maintain a balanced representation and avoid excessive prominence of translated data that was not originally written in Spanish and has not been reviewed in Spanish, the intention is to have scores generally centered around 0.5.
TAGS
#task_categories-sentence-similarity #size_categories-10K<n<100K #language-Spanish #license-mit #region-us 
 ### Dataset Summary


This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.


The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.


The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts. ### Objective


The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes. ### Languages


Spanish


Dataset Structure
----------------- ### Data Fields


* Sentence 1: The first sentence to be compared.
* Sentence 2: The second sentence to be compared.
* Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
* Source: The source of the information, represented by its abbreviation.


Dataset Biases
--------------


This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3. ### Source Data


The dataset was created using the following sources:


1. Already existing datasets:


	* STS-ES (STSB)
	* STSB-Multi-MT (STS)
2. Newly generated data:


	* Chat GPT-3: The sentences were generated using Chat GPT-3 for various general topics.


The dataset includes sentences from various themes, such as:


* Alimentación y Cocina (Food and Cooking)
* Arte y Cultura (Art and Culture)
* Ciencia y Tecnología (Science and Technology)
* Cine y Televisión (Film and Television)
* Deportes (Sports)
* Economía (Economy)
* Educación (Education)
* Estadística (Statistics)
* Filosofía (Philosophy)
* Finanzas (Finance)
* Historia (History)
* Literatura (Literature)
* Medicina (Medicine)
* Medio Ambiente y Sostenibilidad (Environment and Sustainability)
* Moda y Estilo (Fashion and Style)
* Música (Music)
* Organizacional (Organizational)
* Política y Gobierno (Politics and Government)
* Psicología (Psychology)
* Religión y Espiritualidad (Religion and Spirituality)
* Salud y Bienestar (Health and Wellness)


Please note that these themes are not exhaustive.


The prompts for each label (score) are as follows:


* SICK (SICK Dataset)


The dataset also includes translated and sampled sentences from the SICK dataset using Helsinki (helsinki - EN -ES) as the translation tool to achieve an average score close to 0.5 with the entire dataset.


To maintain a balanced representation and avoid excessive prominence of translated data that was not originally written in Spanish and has not been reviewed in Spanish, the intention is to have scores generally centered around 0.5.
passage: TAGS
#task_categories-sentence-similarity #size_categories-10K<n<100K #language-Spanish #license-mit #region-us 
### Dataset Summary


This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.


The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.


The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts.### Objective


The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes.### Languages


Spanish


Dataset Structure
-----------------### Data Fields


* Sentence 1: The first sentence to be compared.
* Sentence 2: The second sentence to be compared.
* Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
* Source: The source of the information, represented by its abbreviation.


Dataset Biases
--------------


This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3.