Datasets:
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
Catalan
Size:
10K - 100K
actualizacion
Browse files- README.md +10 -7
- test_300_manually_reviewed.jsonl +0 -0
README.md
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@@ -43,6 +43,8 @@ This is a synthetic dataset that contains examples, each of them, with the follo
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- Context like "Day: dissabte | Location: Mont-real | mati: el cel estarà molt ennuvolat | tarda: plourà escadusserament | nit: el cel tendirà a estar cobert de núvols | temp: Lleugera pujada de les temperatures"
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- Response like "A la nit el cel estarà ennuvolat"
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### Supported Tasks and Leaderboards
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This dataset is mainly intended to train models for text-generation and named-entity-recognition.
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The dataset consists of examples in a jsonl format with 3 fields each: instruction, context and response.
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### Data Instances
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{
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"instruction": "Quin temps farà a la nit a Camarasa dijous?",
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"context": "Day: dijous | Location: Camarasa | mati: el cel anirà encapotant-se cada cop més | tarda: el sol anirà guanyant terreny als núvols | nit: cel clar | temp: Temperatures sense canvis",
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"response": "A la nit, cel ben clar"
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}
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### Data Fields
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- instruction: Weather-related question.
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- context: Information in the format "Day: [DAY] | Location: [LOCATION] | mati: [WEATHER FORECAST] | tarda: [WEATHER FORECAST] | nit: [WEATHER FORECAST]".
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- response: Whether forecast answering the question.
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### Data Splits
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* dev.json:
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* test.json:
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* train.json:
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## Additional Information
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@@ -90,4 +93,4 @@ This work was funded by the [Departament de la Vicepresidència i de Polítiques
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### Contributions
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[N/A]
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- Context like "Day: dissabte | Location: Mont-real | mati: el cel estarà molt ennuvolat | tarda: plourà escadusserament | nit: el cel tendirà a estar cobert de núvols | temp: Lleugera pujada de les temperatures"
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- Response like "A la nit el cel estarà ennuvolat"
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Added instructions for answering "yes" or "no" questions.
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### Supported Tasks and Leaderboards
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This dataset is mainly intended to train models for text-generation and named-entity-recognition.
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The dataset consists of examples in a jsonl format with 3 fields each: instruction, context and response.
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### Data Instances
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Changed origina context for a more linguistically natural one: "tarda del divendres a Montesquiu al mati s'esperen més nuvolades, a la tarda guspirejarà amb insistència, a la nit podria guspirejar, i Temperatures sense canvis"
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{
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"instruction": "Quin temps farà a la nit a Camarasa dijous?",
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xxx "context": "Day: dijous | Location: Camarasa | mati: el cel anirà encapotant-se cada cop més | tarda: el sol anirà guanyant terreny als núvols | nit: cel clar | temp: Temperatures sense canvis",
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"response": "A la nit, cel ben clar"
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}
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### Data Fields
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- instruction: Weather-related question.
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xxx - context: Information in the format "Day: [DAY] | Location: [LOCATION] | mati: [WEATHER FORECAST] | tarda: [WEATHER FORECAST] | nit: [WEATHER FORECAST]".
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- response: Whether forecast answering the question.
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### Data Splits
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* dev.json: 6873 examples
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* test.json: 1279 examples
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* train.json: 61776 examples
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## Additional Information
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### Contributions
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[N/A]
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test_300_manually_reviewed.jsonl
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