Datasets:
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Update README.md
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README.md
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Dataset structure features.
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An example from the dataset:
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The average word token count are provided below:
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Train: 92,431,194.
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Test: 4,876,739 (in another file).
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The data have several fields:
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TOKENS: number of tokens (excluding punctuation marks) of SENTENCE.
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TYPE: linguistic register of the text.
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The mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:
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# 6. Dataset Creation
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The purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.
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**6.2.1 Initial Data Collection and Normalization**
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The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.
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**6.3.1 Annotation process**
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Not applicable.
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The data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.
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# 7. Considerations for Using the Data
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The purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.
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Most of the content comes from political, economical and sociological opinion articles. Social biases may be present.
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(If applicable, description of the other limitations in the data.)
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# 8. Additional Information
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The data was originally collected by Lino Urdaneta and Miguel Riveros from Mammut.io.
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Not applicable.
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Not applicable.
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Not applicable.
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Dataset structure features.
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## 5.1 Data Instances
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An example from the dataset:
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The average word token count are provided below:
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## 5.2 Total of tokens (no spelling marks)
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Train: 92,431,194.
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Test: 4,876,739 (in another file).
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## 5.3 Data Fields
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The data have several fields:
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TOKENS: number of tokens (excluding punctuation marks) of SENTENCE.
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TYPE: linguistic register of the text.
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## 5.4 Data Splits
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The mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:
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# 6. Dataset Creation
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## 6.1 Curation Rationale
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The purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.
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## 6.2 Source Data
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**6.2.1 Initial Data Collection and Normalization**
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The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.
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# 6.3 Annotations
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**6.3.1 Annotation process**
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Not applicable.
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## 6.4 Personal and Sensitive Information
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The data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.
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# 7. Considerations for Using the Data
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## 7.1 Social Impact of Dataset
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The purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.
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## 7.2 Discussion of Biases
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Most of the content comes from political, economical and sociological opinion articles. Social biases may be present.
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## 7.3 Other Known Limitations
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(If applicable, description of the other limitations in the data.)
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# 8. Additional Information
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## 8.1 Dataset Curators
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The data was originally collected by Lino Urdaneta and Miguel Riveros from Mammut.io.
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## 8.2 Licensing Information
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Not applicable.
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## 8.3 Citation Information
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Not applicable.
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## 8.4 Contributions
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Not applicable.
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