flow like the river

BACK_BACK/OBSEI/analyzer.py

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+from obsei.analyzer.classification_analyzer import ClassificationAnalyzerConfig, ZeroShotClassificationAnalyzer
+
+# initialize classification analyzer config
+# It can also detect sentiments if "positive" and "negative" labels are added.
+analyzer_config=ClassificationAnalyzerConfig(
+   labels=["service", "delay", "performance"],
+)
+
+# initialize classification analyzer
+# For supported models refer https://huggingface.co/models?filter=zero-shot-classification
+analyzer = ZeroShotClassificationAnalyzer(
+   model_name_or_path="typeform/mobilebert-uncased-mnli",
+   device="auto"
+)

BACK_BACK/OBSEI/analyzer/classification_analyzer.py

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+from obsei.analyzer.classification_analyzer import ClassificationAnalyzerConfig, ZeroShotClassificationAnalyzer
+
+# initialize classification analyzer config
+# It can also detect sentiments if "positive" and "negative" labels are added.
+analyzer_config=ClassificationAnalyzerConfig(
+   labels=["service", "delay", "performance"],
+)
+
+# initialize classification analyzer
+# For supported models refer https://huggingface.co/models?filter=zero-shot-classification
+text_analyzer = ZeroShotClassificationAnalyzer(
+   model_name_or_path="typeform/mobilebert-uncased-mnli",
+   device="auto"
+)

BACK_BACK/OBSEI/analyzer/dummy_analyzer.py

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+from obsei.analyzer.dummy_analyzer import DummyAnalyzer, DummyAnalyzerConfig
+
+# initialize dummy analyzer's configuration settings
+analyzer_config = DummyAnalyzerConfig()
+
+# initialize dummy analyzer
+analyzer = DummyAnalyzer()

BACK_BACK/OBSEI/analyzer/ner_analyzer.py

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+from obsei.analyzer.ner_analyzer import NERAnalyzer
+
+# NER analyzer does not need configuration settings
+analyzer_config=None
+
+# initialize ner analyzer
+# For supported models refer https://huggingface.co/models?filter=token-classification
+text_analyzer = NERAnalyzer(
+   model_name_or_path="elastic/distilbert-base-cased-finetuned-conll03-english",
+   device = "auto"
+)

BACK_BACK/OBSEI/analyzer/pii_analyzer.py

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+from obsei.analyzer.pii_analyzer import PresidioEngineConfig, PresidioModelConfig, \
+   PresidioPIIAnalyzer, PresidioPIIAnalyzerConfig
+
+# initialize pii analyzer's config
+analyzer_config = PresidioPIIAnalyzerConfig(
+   # Whether to return only pii analysis or anonymize text
+   analyze_only=False,
+   # Whether to return detail information about anonymization decision
+   return_decision_process=True
+)
+
+# initialize pii analyzer
+analyzer = PresidioPIIAnalyzer(
+   engine_config=PresidioEngineConfig(
+       # spacy and stanza nlp engines are supported
+       # For more info refer
+       # https://microsoft.github.io/presidio/analyzer/developing_recognizers/#utilize-spacy-or-stanza
+       nlp_engine_name="spacy",
+       # Update desired spacy model and language
+       models=[PresidioModelConfig(model_name="en_core_web_lg", lang_code="en")]
+   )
+)

BACK_BACK/OBSEI/analyzer/sentiment_analyzer.py

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+from obsei.analyzer.sentiment_analyzer import VaderSentimentAnalyzer
+
+# Vader does not need any configuration settings
+analyzer_config=None
+
+# initialize vader sentiment analyzer
+text_analyzer = VaderSentimentAnalyzer()

BACK_BACK/OBSEI/analyzer/translation_analyzer.py

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+from obsei.analyzer.translation_analyzer import TranslationAnalyzer
+
+# Translator does not need analyzer config
+analyzer_config = None
+
+# initialize translator
+# For supported models refer https://huggingface.co/models?pipeline_tag=translation
+analyzer = TranslationAnalyzer(
+   model_name_or_path="Helsinki-NLP/opus-mt-hi-en",
+   device = "auto"
+)

BACK_BACK/OBSEI/informer.py

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+from pandas import DataFrame
+from obsei.sink.pandas_sink import PandasSink, PandasSinkConfig
+
+# initialize pandas sink config
+sink_config = PandasSinkConfig(
+   dataframe=DataFrame()
+)
+
+# initialize pandas sink
+sink = PandasSink()

BACK_BACK/OBSEI/observer.py

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+import pandas as pd
+from obsei.source.pandas_source import PandasSource, PandasSourceConfig
+
+# Initialize your Pandas DataFrame from your sources like csv, excel, sql etc
+# In following example we are reading csv which have two columns title and text
+csv_file = "https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv"
+dataframe = pd.read_csv(csv_file)
+
+# initialize pandas sink config
+sink_config = PandasSourceConfig(
+   dataframe=dataframe,
+   include_columns=["score"],
+   text_columns=["name", "degree"],
+)
+
+# initialize pandas sink
+sink = PandasSource()

BACK_BACK/OBSEI/sink/panadas_sink.py

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+from pandas import DataFrame
+from obsei.sink.pandas_sink import PandasSink, PandasSinkConfig
+
+# initialize pandas sink config
+sink_config = PandasSinkConfig(
+   dataframe=DataFrame()
+)
+
+# initialize pandas sink
+sink = PandasSink()