पाठ वर्गीकरण प्रश्नोत्तरी

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

1. How does a text classifier structure the output for an input document?
   • By necessarily translating it into another language
   • By assigning one or more predefined category labels
   • By generating only a new unrelated paragraph
   • By changing its compressed file format
2. कौन सा परिदृश्य मल्टी-लेबल वर्गीकरण का उदाहरण है?
   • किसी ईमेल को स्पैम या स्पैम नहीं के रूप में चिह्नित करना
   • एक टिकट को बिल्कुल एक विभाग में रूट करना
   • सिंगल स्टार रेटिंग चुनना
   • किसी लेख को एक साथ 'राजनीति' और 'अर्थव्यवस्था' दोनों के साथ टैग करना
3. On a text dataset dominated by one class, what can a high overall accuracy conceal?
   • An inability to calculate any metric
   • Proof that recall equals one for every category
   • Failure to identify cases of a rare class
   • A guarantee that every minority example is correct
4. वर्गीकरण कार्य में रिकॉल क्या मापता है?
   • सिस्टम ने कितने वास्तविक मामलों को सफलतापूर्वक पकड़ा
   • कितने चिह्नित आइटम वास्तव में सही थे
   • मॉडल कितनी तेजी से चलता है
   • मॉडल कितना बड़ा है
5. In task-specific zero-shot text classification, what is absent for the new classification task?
   • All model pretraining of any kind
   • Every description of the candidate categories
   • The ability to use any learned representation
   • Labeled training examples for that task
6. How can learned text embeddings contribute to a classification pipeline?
   • They guarantee perfect predictions on every new domain
   • They provide numerical features used to compute label scores
   • They delete every grammatical distinction by definition
   • They replace all label definitions with filenames
7. A stated text-classification evaluation has six true positives and two false positives. What is precision for the designated positive class?
   • 75 percent
   • 25 percent
   • 100 प्रतिशत
   • Six percent
8. Which output-score design permits several text labels to score highly at once rather than requiring scores to sum to one?
   • A single softmax over mutually exclusive classes
   • A rule requiring exactly one positive label
   • A separate sigmoid output for each label
   • A filename-only representation without label scores