TopicClassification
API reference for TopicClassification
TopicClassification
TopicClassification
Evaluator to classify text topics using Fiddler’s zero-shot topic classification model.
The TopicClassification evaluator uses Fiddler’s implementation of the mortizlaurer/roberta-base-zeroshot-v2-0-c model to classify text content into predefined topic categories. This evaluator helps identify the main subject matter or theme of text content, providing both topic labels and confidence scores for topic classification.
Key Features:
Topic Classification: Classifies text into predefined topic categories
Dual Score Output: Returns both topic label and probability confidence
Zero-Shot Model: Uses mortizlaurer/roberta-base-zeroshot-v2-0-c for flexible topic classification
Multi-Score Output: Returns both topic name and probability scores
Topic Categories Evaluated:
top_topic: The predicted topic name from the provided topics list
top_topic_prob: Probability score (0.0-1.0) for the predicted topic
Use Cases:
Content Categorization: Automatically organizing content by topic
Document Classification: Sorting documents by subject matter
News Analysis: Categorizing news articles by topic
Customer Support: Routing tickets by topic or issue type
Content Moderation: Identifying content themes for policy enforcement
Scoring Logic: : The topic classification provides two complementary scores:
top_topic: The predicted topic name from the provided topics list
Selected from the topics provided during initialization
Represents the most relevant topic for the input text
top_topic_prob: Confidence score (0.0-1.0) for the prediction : - 0.0-0.3: Low confidence in topic prediction
0.3-0.7: Medium confidence in topic prediction
0.7-1.0: High confidence in topic prediction
Parameters
topics
list[str]
✗
None
List of topic categories to classify text into.
Returns
A list of Score objects containing: : - top_topic: Score object with predicted topic name
top_topic_prob: Score object with probability score (0.0-1.0) Return type: list[Score]
Raises
ValueError – If the text is empty or None, or if no scores are returned from the API.
Example
TopicClassification
TopicClassification
Evaluator to classify text topics using Fiddler’s zero-shot topic classification model.
The TopicClassification evaluator uses Fiddler’s implementation of the mortizlaurer/roberta-base-zeroshot-v2-0-c model to classify text content into predefined topic categories. This evaluator helps identify the main subject matter or theme of text content, providing both topic labels and confidence scores for topic classification.
Key Features:
Topic Classification: Classifies text into predefined topic categories
Dual Score Output: Returns both topic label and probability confidence
Zero-Shot Model: Uses mortizlaurer/roberta-base-zeroshot-v2-0-c for flexible topic classification
Multi-Score Output: Returns both topic name and probability scores
Topic Categories Evaluated:
top_topic: The predicted topic name from the provided topics list
top_topic_prob: Probability score (0.0-1.0) for the predicted topic
Use Cases:
Content Categorization: Automatically organizing content by topic
Document Classification: Sorting documents by subject matter
News Analysis: Categorizing news articles by topic
Customer Support: Routing tickets by topic or issue type
Content Moderation: Identifying content themes for policy enforcement
Scoring Logic: : The topic classification provides two complementary scores:
top_topic: The predicted topic name from the provided topics list
Selected from the topics provided during initialization
Represents the most relevant topic for the input text
top_topic_prob: Confidence score (0.0-1.0) for the prediction : - 0.0-0.3: Low confidence in topic prediction
0.3-0.7: Medium confidence in topic prediction
0.7-1.0: High confidence in topic prediction
Parameters
topics
list[str]
✗
None
List of topic categories to classify text into.
Returns
A list of Score objects containing: : - top_topic: Score object with predicted topic name
top_topic_prob: Score object with probability score (0.0-1.0) Return type: list[Score]
Raises
ValueError – If the text is empty or None, or if no scores are returned from the API.
Example
>>> from fiddler_evals.evaluators import TopicClassification
>>> evaluator = TopicClassification(topics=["technology", "sports", "politics", "entertainment"])
# Technology topic
scores = evaluator.score(“The new AI model shows promising results in natural language processing.”)
print(f”Topic: {scores[0].label}”)
print(f”Confidence: {scores[1].value}”)
# Topic: technology
# Confidence: 0.92
# Sports topic
sports_scores = evaluator.score(“The team won the championship with an amazing performance!”)
print(f”Topic: {sports_scores[0].label}”)
print(f”Confidence: {sports_scores[1].value}”)
# Topic: sports
# Confidence: 0.88
# Politics topic
politics_scores = evaluator.score(“The new policy will affect millions of citizens.”)
print(f”Topic: {politics_scores[0].label}”)
print(f”Confidence: {politics_scores[1].value}”)
# Topic: politics
# Confidence: 0.85
# Filter based on topic and confidence
if scores[0].label == “technology” and scores[1].value > 0.8:
> print(“High confidence technology topic detected”)
{% hint style="info" %}
This evaluator uses zero-shot classification, meaning it can classify text into
any set of topics provided during initialization without requiring training data
for those specific topics. The mortizlaurer/roberta-base-zeroshot-v2-0-c model
is particularly effective for general-purpose topic classification across
diverse domains. The dual-score output provides both categorical classification
and confidence assessment for robust topic analysis workflows.
{% endhint %}
#### name *= 'topic_classification'*
Initialize the TopicClassification evaluator.
#### Parameters
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `topics` | `list[str]` | ✗ | `None` | List of topic categories to classify text into. |
#### Raises
**ValueError** – If the topics are empty or None.
#### score()
Score the topic classification of text content.
#### Parameters
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `text` | `str` | ✗ | `None` | The text content to evaluate for topic classification. |
#### Returns
A list of Score objects for topic name and probability.
**Return type:** list[Score]
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