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  1. README.md +9 -4
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@@ -33,18 +33,23 @@ Although the SICK corpus inspires the main design of the dataset, it differs in
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  The entailment relation between two sentences is labeled with *entailment*, *contradiction*, or *neutral*. The task is to predict if the premise entails the hypothesis (entailment), negates the hypothesis (contradiction), or is unrelated (neutral).
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  **Input**: ('sentence_A', 'sentence_B'): sentence pair
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  **Output** ('entailment_judgment' column): one of the possible entailment relations (*entailment*, *contradiction*, *neutral*)
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  **Domain:** image captions
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- *Example:*
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- - b **entails** a (a **wynika z** b) – if a situation or an event described by sentence b occurs, it is recognized that a situation or an event described by a occurs as well, i.e., a and b refer to the same event or the same situation;
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- Żaden mężczyzna nie stoi na przystanku autobusowym. (Eng. No man standing at the bus stop.) vs. Mężczyzna z żółtą i białą reklamówką w ręce stoi na przystanku obok autobusu. (Eng. A man with a yellow and white commercial in his hand stands at a bus stop next to a bus.) → **entailment**
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- **Measurements**: Accuracy
 
 
 
 
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  ## Data splits
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  The entailment relation between two sentences is labeled with *entailment*, *contradiction*, or *neutral*. The task is to predict if the premise entails the hypothesis (entailment), negates the hypothesis (contradiction), or is unrelated (neutral).
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+ b **entails** a (a **wynika z** b) – if a situation or an event described by sentence b occurs, it is recognized that a situation or an event described by a occurs as well, i.e., a and b refer to the same event or the same situation;
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  **Input**: ('sentence_A', 'sentence_B'): sentence pair
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  **Output** ('entailment_judgment' column): one of the possible entailment relations (*entailment*, *contradiction*, *neutral*)
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  **Domain:** image captions
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+ **Measurements**: Accuracy
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+ **Example:**
 
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+ Input: `Żaden mężczyzna nie stoi na przystanku autobusowym.` ; `Mężczyzna z żółtą i białą reklamówką w ręce stoi na przystanku obok autobusu.`
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+ Input (translated by DeepL): `No man standing at the bus stop.` ; `A man with a yellow and white bag in his hand stands at a bus stop next to a bus.`
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+ Output: `entailment`
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  ## Data splits
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