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actualizacion

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  1. README.md +10 -7
  2. test_300_manually_reviewed.jsonl +0 -0
README.md CHANGED
@@ -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.
@@ -56,23 +58,24 @@ The dataset is in Catalan (`ca-CA`).
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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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  {
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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: 5563 examples
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- * test.json: 5688 examples
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- * train.json: 45606 examples
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  ## Additional Information
@@ -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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+
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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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+
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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]
test_300_manually_reviewed.jsonl ADDED
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