Join

 

📊 Dataset Metrics

This guide gives you a brief introduction to Argilla Metrics. Argilla Metrics enable you to perform fine-grained analyses of your models and training datasets. Argilla Metrics are inspired by a a number of seminal works such as Explainaboard.

The main goal is to make it easier to build more robust models and training data, going beyond single-number metrics (e.g., F1).

This guide gives a brief overview of currently supported metrics. For the full API documentation see the Python API reference.

All Python metrics are covered in:

from argilla import metrics

This feature is experimental, you can expect some changes in the Python API. Please report on Github any issue you encounter.

Install dependencies

Verify you have already installed Jupyter Widgets in order to properly visualize the plots. See https://ipywidgets.readthedocs.io/en/latest/user_install.html

For running this guide you need to install the following dependencies:

%pip install datasets spacy plotly -qqq

Note: you may need to restart the kernel to use updated packages.

and the spacy model:

!python -m spacy download en_core_web_sm -qqq

1. NER prediction metrics

Load dataset and model

We’ll be using spaCy for this guide, but all the metrics we’ll see are computed for any other framework (Flair, Stanza, Hugging Face, etc.). As an example will use the WNUT17 NER dataset.

import argilla as rg
import spacy
from datasets import load_dataset

nlp = spacy.load("en_core_web_sm")
dataset = load_dataset("wnut_17", split="train")

Log records in dataset

Let’s log spaCy predictions using the built-in rg.monitor method:

nlp = rg.monitor(nlp, dataset="spacy_sm_wnut17")

def predict(records):
    for _ in nlp.pipe([
        " ".join(record_tokens)
        for record_tokens in records["tokens"]
    ]):
        pass
    return {"predicted": [True]*len(records["tokens"])}

dataset.map(predict, batched=True, batch_size=512)

Explore pipeline metrics

from argilla.metrics.token_classification import token_length

token_length(name="spacy_sm_wnut17").visualize()