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inovex/
multi2convai-quality-it-logreg-ft

Name: multi2convai-quality-it-logreg-ft
Availability: InStock

text-classification

mit

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Multi2ConvAI-Quality: Italian logistic regression model using fasttext embeddings

This model was developed in the Multi2ConvAI project:

domain: Quality (more details about our use cases: (en, de))
language: Italian (ml)
model type: logistic regression
embeddings: fastText embeddings

How to run

Requires:

multi2convai
serialized fastText embeddings (see last section of this readme or these instructions)

Run with one line of code

After installing multi2convai and locally available fastText embeddings you can run:

bash

# assumes working dir is the root of the cloned multi2convai repo

python scripts/run_inference.py -m multi2convai-quality-it-logreg-ft

>>> Create pipeline for config: multi2convai-quality-it-logreg-ft.
>>> Created a LogisticRegressionFasttextPipeline for domain: 'quality' and language 'it'.
>>> 
>>> Enter your text (type 'stop' to end execution): Avviare il programma
>>> 'Avviare il programma' was classified as 'neo.start' (confidence: 0.8943)

How to run model using multi2convai

After installing multi2convai and locally available fastText embeddings you can run:

python

# assumes working dir is the root of the cloned multi2convai repo

from pathlib import Path

from multi2convai.pipelines.inference.base import ClassificationConfig
from multi2convai.pipelines.inference.logistic_regression_fasttext import (
    LogisticRegressionFasttextConfig,
    LogisticRegressionFasttextPipeline,
)

language = "it"
domain = "quality"

# 1. Define paths of model, label dict and embeddings
model_file = "model.pth"
label_dict_file = "label_dict.json"

embedding_path = Path(
    f"../models/embeddings/fasttext/it/wiki.200k.it.embed"
)
vocabulary_path = Path(
    f"../models/embeddings/fasttext/it/wiki.200k.it.vocab"
)

# 2. Create and setup pipeline
model_config = LogisticRegressionFasttextConfig(
    model_file, embedding_path, vocabulary_path
)
config = ClassificationConfig(language, domain, label_dict_file, model_config)

pipeline = LogisticRegressionFasttextPipeline(config)
pipeline.setup()

# 3. Run intent classification on a text of your choice
label = pipeline.run("Avviare il programma")
label
>>> Label(string='neo.start', ratio='0.8943')

Download and serialize fastText