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import pandas as pd
from transformers import pipeline
from transformers.pipelines.base import Pipeline
from scipy.sparse import csr_matrix
from typing import Mapping, List, Tuple, Any
from bertopic.representation._base import BaseRepresentation
class ZeroShotClassification(BaseRepresentation):
""" Zero-shot Classification on topic keywords with candidate labels
Arguments:
candidate_topics: A list of labels to assign to the topics if they
exceed `min_prob`
model: A transformers pipeline that should be initialized as
"zero-shot-classification". For example,
`pipeline("zero-shot-classification", model="facebook/bart-large-mnli")`
pipeline_kwargs: Kwargs that you can pass to the transformers.pipeline
when it is called. NOTE: Use `{"multi_label": True}`
to extract multiple labels for each topic.
min_prob: The minimum probability to assign a candidate label to a topic
Usage:
```python
from bertopic.representation import ZeroShotClassification
from bertopic import BERTopic
# Create your representation model
candidate_topics = ["space and nasa", "bicycles", "sports"]
representation_model = ZeroShotClassification(candidate_topics, model="facebook/bart-large-mnli")
# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)
```
"""
def __init__(self,
candidate_topics: List[str],
model: str = "facebook/bart-large-mnli",
pipeline_kwargs: Mapping[str, Any] = {},
min_prob: float = 0.8
):
self.candidate_topics = candidate_topics
if isinstance(model, str):
self.model = pipeline("zero-shot-classification", model=model)
elif isinstance(model, Pipeline):
self.model = model
else:
raise ValueError("Make sure that the HF model that you"
"pass is either a string referring to a"
"HF model or a `transformers.pipeline` object.")
self.pipeline_kwargs = pipeline_kwargs
self.min_prob = min_prob
def extract_topics(self,
topic_model,
documents: pd.DataFrame,
c_tf_idf: csr_matrix,
topics: Mapping[str, List[Tuple[str, float]]]
) -> Mapping[str, List[Tuple[str, float]]]:
""" Extract topics
Arguments:
topic_model: Not used
documents: Not used
c_tf_idf: Not used
topics: The candidate topics as calculated with c-TF-IDF
Returns:
updated_topics: Updated topic representations
"""
# Classify topics
topic_descriptions = [" ".join(list(zip(*topics[topic]))[0]) for topic in topics.keys()]
classifications = self.model(topic_descriptions, self.candidate_topics, **self.pipeline_kwargs)
# Extract labels
updated_topics = {}
for topic, classification in zip(topics.keys(), classifications):
topic_description = topics[topic]
# Multi-label assignment
if self.pipeline_kwargs.get("multi_label"):
topic_description = []
for label, score in zip(classification["labels"], classification["scores"]):
if score > self.min_prob:
topic_description.append((label, score))
# Single label assignment
elif classification["scores"][0] > self.min_prob:
topic_description = [(classification["labels"][0], classification["scores"][0])]
# Make sure that 10 items are returned
if len(topic_description) == 0:
topic_description = topics[topic]
elif len(topic_description) < 10:
topic_description += [("", 0) for _ in range(10-len(topic_description))]
updated_topics[topic] = topic_description
return updated_topics
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