Update readme

README.md

@@ -1,3 +1,99 @@
 ---
+tags:
+- text-classification
+widget:
+- text: "Hosted inference API not supported"
 license: mit
+language: en
 ---
+
+# Multi2ConvAI-Logistics: English logistic regression model using fasttext embeddings
+
+This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
+- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
+- language: English (en)
+- model type: logistic regression
+- embeddings: fastText embeddings
+
+## How to run
+
+Requires: 
+- [multi2convai](https://github.com/inovex/multi2convai)
+- serialized fastText embeddings (see last section of this readme or [these instructions](https://github.com/inovex/multi2convai/models/embeddings.README.md))
+
+### 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-logistics-en-logreg-ft
+
+>>> Create pipeline for config: multi2convai-logistics-en-logreg-ft.
+>>> Created a LogisticRegressionFasttextPipeline for domain: 'logistics' and language 'en'.
+>>> 
+>>> Enter your text (type 'stop' to end execution): Muss ich eine Maske tragen?
+>>> 'Where can I put the parcel?' 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:
+
+````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 = "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/en/wiki.200k.en.embed"
+)
+vocabulary_path = Path(
+    f"../models/embeddings/fasttext/en/wiki.200k.en.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("Where can I put the parcel?")
+label
+>>> Label(string='details.safeplace', ratio='0.8943')
+````
+
+### Download and serialize fastText
+````bash
+# assumes working dir is the root of the cloned multi2convai repo
+
+mkdir models/fasttext/en
+curl https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.en.vec --output models/fasttext/en/wiki.en.vec
+
+python scripts/serialize_fasttext.py -r fasttext/wiki.en.vec -v fasttext/en/wiki.200k.en.vocab -e fasttext/en/wiki.200k.en.embed -n 200000
+
+
+````
+
+## Further information on Multi2ConvAI:
+- https://multi2conv.ai
+- https://github.com/inovex/multi2convai
+- mailto: info@multi2conv.ai