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"""Implementation of the zero-shot classifier."""
import json
import logging
import os
from typing import List, Optional, Union
import torch
from transformers import pipeline
from ....frameworks.torch import device_claim
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class ZeroShotClassifier:
"""
Zero-shot classifier based on the HuggingFace pipeline leveraging MLNI.
"""
[docs] def __init__(
self,
resources_path: str,
model_name: str,
device: Optional[Union[torch.device, str]] = None,
):
"""Initialize ZeroShotClassifier.
Args:
resources_path: path where to load hypothesis, candidate labels and, optionally, the model.
model_name: name of the model to load from the cache or download from HuggingFace.
device: device where the inference
is running either as a dedicated class or a string. If not provided is inferred.
"""
device = device_claim(device)
self.device = -1 if device.type == "cpu" else int(device.type.split(":")[1])
self.resources_path = resources_path
self.model_name = model_name
self.load_pipeline()
[docs] def load_pipeline(self) -> None:
"""Load zero shot classification MLNI pipeline."""
metadata_filepath = os.path.join(self.resources_path, "metadata.json")
if os.path.exists(metadata_filepath):
with open(metadata_filepath) as fp:
metadata = json.load(fp)
self.labels = metadata["labels"]
self.hypothesis_template = metadata["hypothesis_template"]
self.model_name_or_path = os.path.join(self.resources_path, self.model_name)
if not os.path.exists(self.model_name_or_path):
logger.info(
f"no model named {self.model_name_or_path} in cache, using HuggingFace"
)
self.model_name_or_path = self.model_name
else:
message = f"could not retrieve the MLNI pipeline from the cache: {metadata_filepath} does not exists!"
logger.error(message)
raise ValueError(message)
self.model = pipeline(
"zero-shot-classification",
model=self.model_name_or_path,
device=self.device,
)
[docs] def predict(self, text: str) -> List[str]:
"""Get sorted classification labels.
Args:
text: text to classify.
Returns:
labels sorted by score from highest to lowest.
"""
return self.model(
text,
candidate_labels=self.labels,
hypothesis_template=self.hypothesis_template,
)["labels"]