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import logging
from typing import Any, Dict, List, Optional
from pydantic import Field, PrivateAttr
from transformers import Pipeline, pipeline
from obsei.analyzer.base_analyzer import (
BaseAnalyzer,
BaseAnalyzerConfig,
MAX_LENGTH,
)
from obsei.payload import TextPayload
from obsei.postprocessor.inference_aggregator import InferenceAggregatorConfig
from obsei.postprocessor.inference_aggregator_function import ClassificationAverageScore
logger = logging.getLogger(__name__)
class ClassificationAnalyzerConfig(BaseAnalyzerConfig):
TYPE: str = "Classification"
labels: Optional[List[str]] = None
label_map: Optional[Dict[str, str]] = None
multi_class_classification: bool = True
add_positive_negative_labels: bool = True
aggregator_config: InferenceAggregatorConfig = Field(
InferenceAggregatorConfig(aggregate_function=ClassificationAverageScore())
)
def __init__(self, **data: Any):
super().__init__(**data)
if self.labels is None:
self.multi_class_classification = False
self.add_positive_negative_labels = False
class TextClassificationAnalyzer(BaseAnalyzer):
TYPE: str = "Classification"
pipeline_name: str = "text-classification"
_pipeline: Pipeline = PrivateAttr()
_max_length: int = PrivateAttr()
model_name_or_path: str
def __init__(self, **data: Any):
super().__init__(**data)
self._pipeline = pipeline(
self.pipeline_name,
model=self.model_name_or_path,
device=self._device_id,
)
if hasattr(self._pipeline.model.config, "max_position_embeddings"):
self._max_length = self._pipeline.model.config.max_position_embeddings
else:
self._max_length = MAX_LENGTH
def prediction_from_model(
self,
texts: List[str],
analyzer_config: Optional[ClassificationAnalyzerConfig] = None,
) -> List[Dict[str, Any]]:
prediction = self._pipeline(texts)
predictions = prediction if isinstance(prediction, list) else [prediction]
label_map = analyzer_config.label_map if analyzer_config is not None else {}
label_map = label_map or {}
return [
{
label_map.get(prediction["label"], prediction["label"]): prediction["score"]
} for prediction in predictions
]
def analyze_input( # type: ignore[override]
self,
source_response_list: List[TextPayload],
analyzer_config: Optional[ClassificationAnalyzerConfig] = None,
**kwargs: Any,
) -> List[TextPayload]:
analyzer_output: List[TextPayload] = []
if (
analyzer_config is not None
and analyzer_config.use_splitter_and_aggregator
and analyzer_config.splitter_config
):
source_response_list = self.splitter.preprocess_input(
source_response_list,
config=analyzer_config.splitter_config,
)
for batch_responses in self.batchify(source_response_list, self.batch_size):
texts = [
source_response.processed_text[: self._max_length]
for source_response in batch_responses
]
batch_predictions = self.prediction_from_model(texts=texts, analyzer_config=analyzer_config)
for score_dict, source_response in zip(batch_predictions, batch_responses):
segmented_data = {
"classifier_data": score_dict
}
if source_response.segmented_data:
segmented_data = {
**segmented_data,
**source_response.segmented_data,
}
analyzer_output.append(
TextPayload(
processed_text=source_response.processed_text,
meta=source_response.meta,
segmented_data=segmented_data,
source_name=source_response.source_name,
)
)
if (
analyzer_config is not None
and analyzer_config.use_splitter_and_aggregator
and analyzer_config.aggregator_config
):
analyzer_output = self.aggregator.postprocess_input(
input_list=analyzer_output,
config=analyzer_config.aggregator_config,
)
return analyzer_output
class ZeroShotClassificationAnalyzer(TextClassificationAnalyzer):
pipeline_name: str = "zero-shot-classification"
def prediction_from_model(
self,
texts: List[str],
analyzer_config: Optional[ClassificationAnalyzerConfig] = None,
) -> List[Dict[str, Any]]:
if analyzer_config is None:
raise ValueError("analyzer_config can't be None")
labels = analyzer_config.labels or []
if analyzer_config.add_positive_negative_labels:
if "positive" not in labels:
labels.append("positive")
if "negative" not in labels:
labels.append("negative")
if len(labels) == 0:
raise ValueError("`labels` can't be empty or `add_positive_negative_labels` should be False")
prediction = self._pipeline(
texts, candidate_labels=labels, multi_label=analyzer_config.multi_class_classification
)
predictions = prediction if isinstance(prediction, list) else [prediction]
return [dict(zip(prediction["labels"], prediction["scores"])) for prediction in predictions]
def analyze_input( # type: ignore[override]
self,
source_response_list: List[TextPayload],
analyzer_config: Optional[ClassificationAnalyzerConfig] = None,
**kwargs: Any,
) -> List[TextPayload]:
if analyzer_config is None:
raise ValueError("analyzer_config can't be None")
return super().analyze_input(
source_response_list=source_response_list,
analyzer_config=analyzer_config,
**kwargs
)
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