ner_fashion_brands

🪐 spaCy Project: Detecting fashion brands in online comments (Named Entity Recognition)

This project uses sense2vec and Prodigy to bootstrap an NER model to detect fashion brands in Reddit comments. For more details, see our blog post.

📋 project.yml

The project.yml defines the data assets required by the project, as well as the available commands and workflows. For details, see the spaCy projects documentation.

⏯ Commands

The following commands are defined by the project. They can be executed using spacy project run [name]. Commands are only re-run if their inputs have changed.

Command	Description
preprocess	Convert the data to spaCy's binary format
train	Train a named entity recognition model
evaluate	Evaluate the model and export metrics
package	Package the trained model so it can be installed
visualize-model	Visualize the model's output interactively using Streamlit
visualize-data	Explore the annotated data in an interactive Streamlit app

⏭ Workflows

The following workflows are defined by the project. They can be executed using spacy project run [name] and will run the specified commands in order. Commands are only re-run if their inputs have changed.

Workflow	Steps
all	preprocess → train → evaluate

🗂 Assets

The following assets are defined by the project. They can be fetched by running spacy project assets in the project directory.

File	Source	Description
assets/fashion_brands_training.jsonl	Local	JSONL-formatted training data exported from Prodigy, annotated with FASHION_BRAND entities (1235 examples)
assets/fashion_brands_eval.jsonl	Local	JSONL-formatted development data exported from Prodigy, annotated with FASHION_BRAND entities (500 examples)
assets/fashion_brands_patterns.jsonl	Local	Patterns file generated with sense2vec.teach and used to pre-highlight during annotation (100 patterns)

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📚 Data

Labelling the data took about 2 hours and was done manually using the patterns to pre-highlight suggestions. The raw text was sourced from the r/MaleFashionAdvice and r/FemaleFashionAdvice subreddits.

File	Count	Description
fashion_brands_patterns.jsonl	100	Match patterns created with sense2vec.teach and sense2vec.to-patterns. Can be used with spaCy's EntityRuler for a rule-based baseline and faster NER annotation.
fashion_brands_training.jsonl	1235	Training data annotated with FASHION_BRAND entities.
fashion_brands_eval.jsonl	500	Evaluation data annotated with FASHION_BRAND entities.

Visualize the data and model

The visualize_data.py script lets you visualize the training and evaluation datasets with displaCy.

python -m spacy project run visualize-data

The visualize_model.py script is powered by spacy-streamlit and lets you explore the trained model interactively.

python -m spacy project run visualize-model

Training and evaluation data format

The training and evaluation datasets are distributed in Prodigy's simple JSONL (newline-delimited JSON) format. Each entry contains a "text" and a list of "spans" with the "start" and "end" character offsets and the "label" of the annotated entities. The data also includes the tokenization. Here's a simplified example entry:

{
  "text": "Bonobos has some long sizes.",
  "tokens": [
    { "text": "Bonobos", "start": 0, "end": 7, "id": 0 },
    { "text": "has", "start": 8, "end": 11, "id": 1 },
    { "text": "some", "start": 12, "end": 16, "id": 2 },
    { "text": "long", "start": 17, "end": 21, "id": 3 },
    { "text": "sizes", "start": 22, "end": 27, "id": 4 },
    { "text": ".", "start": 27, "end": 28, "id": 5 }
  ],
  "spans": [
    {
      "start": 0,
      "end": 7,
      "token_start": 0,
      "token_end": 0,
      "label": "FASHION_BRAND"
    }
  ],
  "_input_hash": -874614165,
  "_task_hash": 2136869442,
  "answer": "accept"
}