multi2convai-logistics-de-logreg-ft

Multi2ConvAI-Logistics: German logistic regression model using fasttext embeddings

This model was developed in the Multi2ConvAI project:

• domain: Logistics (more details about our use cases: (en, de))
• language: German (de)
• 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:

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

python scripts/run_inference.py -m multi2convai-logistics-de-logreg-ft

>>> Create pipeline for config: multi2convai-logistics-de-logreg-ft.
>>> Created a LogisticRegressionFasttextPipeline for domain: 'logistics' and language 'de'.
>>> 
>>> Enter your text (type 'stop' to end execution): Muss ich eine Maske tragen?
>>> 'Wo kann ich das Paket ablegen?' was classified as 'details.safeplace' (confidence: 0.8943)

How to run model using multi2convai

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

# 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 = "de"
domain = "logistics"

# 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/de/wiki.200k.de.embed"
)
vocabulary_path = Path(
    f"../models/embeddings/fasttext/de/wiki.200k.de.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("Wo kann ich das Paket ablegen?")
label
>>> Label(string='details.safeplace', ratio='0.8943')

Download and serialize fastText

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

mkdir models/fasttext/de
curl https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.de.vec --output models/fasttext/de/wiki.de.vec

python scripts/serialize_fasttext.py -r fasttext/wiki.de.vec -v fasttext/de/wiki.200k.de.vocab -e fasttext/de/wiki.200k.de.embed -n 200000

Further information on Multi2ConvAI:

• https://multi2conv.ai
• https://github.com/inovex/multi2convai
• mailto: [email protected]