Productionizing a CRF model, Recipe Ingredients Tagger in Action.

image of Color coded entities

A popular way to productionize a statistical model would be to expose them as a REST API, so that they can be scaled horizontally and is cost effective. In this post I’ll discuss the steps involved without implementation details.

In my previous post I’ve discussed how to build a simple tagger using CRFSuite. The goal of the tagger is to convert unstructured data to structured one by tagging entities. I took ‘Food and Recipes’ as my domain and have identified 4 important entities which are required to describe a recipe.

  • QTY – Quantity, number of units required. Usually numbers.
  • UNIT – Such as teaspoon, pinch, bottles, cups etc.
  • NAME – Name of the ingredient, example: sugar, almond, chicken, milk etc.
  • COM – Comment about the ingredients. example: crushed, finely chopped, powdered etc.
  • OTHERS – Random text that can be ignored.

I’ve used Flask framework for microservices and GUnicorn for production deployment.

The input/output contract is simple, Given a list of ingredients, The API should identify entities and tag them.

Consider the following homemade mac and cheese recipe from as an example.

image of a recipe
homemade mac and cheese ingredients

Our goal is to identify entities present in the text highlighted in yellow (i.e. list of ingredients).

The API accepts input in the following format.

And generates output as shown below, Tokens and their respective tagged labels.

A simple visualization to understand the output better.

image of Color coded entities
Color coded ingredient entities

CRFSuite is written in C++, We can leverage the CRFSuite’s C++ API by using SWIG wrapper for Python.

The following snippet explains the various steps involved in transforming the incoming data to model understandable features and how the output is interpreted in the end.

Once the flask app is ready, Deploying with GUnicorn is simple.

Since CRF is a statistical model, It requires the modeler to understand the relation between variables and hence spends 90% of the time preparing data for training and testing. In other words, its time consuming. These models can be used as a stepping stone towards building unsupervised learning algorithms, search relevance, recommendation, shopping cart and buy button use cases etc.

You can try the API with different inputs at

(registration required)


Structuring text – Sequence tagging using Conditional Random Field (CRF). Tagging recipe ingredient phrases.

Building a food graph is an interesting problem.
Such graphs can be used to mine similar recipes, analyse relationship between cuisines and food cultures etc.

This blog post from NYTimes about “Extracting Structured Data From Recipes Using Conditional Random Fields” could be an initial step towards building such graphs.

In an attempt to implement the idea shared in the blog post mentioned above, I’ve used CRFSuite to build a model that tags entities in ingredients list.
CRFSuite installation instruction here.

Note: For the impatient, Please checkout the TL;DR section at the end of the post.

3 steps to reach the goal.

  1. Understanding data.
  2. Preparing data.
  3. Building model.

Step 1: Understanding data.

The basic assumption is to use the following 5 entities to tag ingredients of a recipe.

  1. Quantity (QTY)
  2. Unit (UNIT)
  3. Comment (COM)
  4. Name (NAME)
  5. Others (OTHERS)

For example,

Ingredient Quantity Unit Comment Name Others
2 tablespoons of soya sauce 2 tablespoons NA soya, sauce of
Onions sliced and fried brown 3 medium 3 NA sliced, brown, fried onions and
3 Finely chopped Green Chillies 3 NA finely, chopped, green chillies NA

Similarly most of the ingredients shared in recipes can be tagged with these 5 labels.

Step 2: Preparing data.

Preparing data involves the following steps

  1. Collecting data
  2. POS tagging
  3. Labeling tokens
  4. Chunking

A simple script to politely scrape data from any recipe site will do the job. Checkout Scrapy.

I’ve collected data in the following format.

The actual input file is a JSON Lines file.

A three column tab separated file is required for chunking.

  • Column 1 – Token
  • Column 2 – POS tag
  • Column 3 – Label (done manually)

Each token in a ingredient list gets a line in the TSV file and a new line is left to separate ingredients.
The following script generates data in required format taking the JSON lines file mentioned above as input.

$ cat recipes.jl | python > token_pos.tsv

Note that XXX is just a place holder, which will be replaced by the actual label (i.e. one of QTY, UNIT, COM, NAME, OTHERS).
I’ve manually labeled each token with the help of OpenRefine, Skip this step if you are tagging using a model that is already available.
In the end the file should look similar to table shown below.

Next task is chunking and it is explained well here.
The same POS and token position features discussed in the tutorial are used as features in this experiment as well,So using the util script provided in the CRFSuite repository we can generate chunks.

$ cat token_pos_tagged.tsv | python ~/workspace/crfsuite/example/ -s $'\t' > chunk.txt 

After chunking the final output file should look similar to this.

Step 3: Building model

To train

$ crfsuite learn -m <model_name> <chunk_file>

To test

$ crfsuite tag -qt -m <model_name> <chunk_file>

To tag

$ crfsuite tag -m <model_name> <chunk_file>


I’ve collected 2000 recipes out of which 60% is used for training and 40% is used for testing.

Each ingredient is tokenized, POS tagged and manually labeled (hardest part).
Following are the input, intermediate and output files.

  • recipes.jl – a JSON lines file containing 2000 recipes. Input file
  • token_pos.tsv – Intermediate TSV file with token and its POS. (column with XXX is a place holder for next step)
  • token_pos_tagged.tsv – TSV file with token, pos and label columns, after tagging 3rd column manually.
  • train.txt – 60% of input, chunked, for training
  • test.txt – 40% of input, chunked, for testing
  • recipe.model – model output
$ cat recipes.jl | python > token_pos.tsv

Intermediate step: Manually label tokens and generate token_pos_tagged.tsv

$ cat token_pos_tagged.tsv | python ~/workspace/crfsuite/example/ > chunk.txt

Intermediate step: split chunk.txt in 60/40 ratio to get train.txt and test.txt respectively


$ crfsuite learn -m recipes.model train.txt


$ crfsuite tag -qt -m recipes.model test.txt

Performance by label (#match, #model, #ref) (precision, recall, F1):
    QTY: (7307, 7334, 7338) (0.9963, 0.9958, 0.9960)
    UNIT: (3944, 4169, 4091) (0.9460, 0.9641, 0.9550)
    COM: (5014, 5281, 5505) (0.9494, 0.9108, 0.9297)
    NAME: (11943, 12760, 12221) (0.9360, 0.9773, 0.9562)
    OTHER: (6984, 7094, 7483) (0.9845, 0.9333, 0.9582)
Macro-average precision, recall, F1: (0.962451, 0.956244, 0.959025)
Item accuracy: 35192 / 36638 (0.9605)
Instance accuracy: 6740 / 7854 (0.8582)
Elapsed time: 0.328684 [sec] (23895.3 [instance/sec])

Note: -qt option will work only with labeled data.

Precision 96%
Recall 95%
F1 Measure 95%

Read more about precision, recall and F1 measure here

To tag ingredients that the model has never seen before, follow Step 2 and run the following command


$ crfsuite tag -m recipes.model test.txt

code and data here