Python on Christian Hotz-Behofsits

Running a Zero-Shot Classifier on Your Own GPU

Tue, 28 Jul 2026 00:00:00 +0000

In a recent project I had 1,500 tweets and a codebook with 16 categories. There were three ways to get labels: code them by hand, send them to a commercial API, or run an open-weight model on our own GPU. I went with the third one, and this post is what I wish someone had told me before I started.

The reasons for staying local are not ideological. First, the data never leaves the machine. Second, I have already the hardware and there is no per-token cost, so you can iterate on the prompt fifty times instead of five, and prompt iteration is where the accuracy comes from. Third, a pinned model version at temperature=0 is reproducible, which turns the classification step into something you can ship in a replication package rather than a black box you apologize for in a footnote.

Applying Cosine Similarity on a Large Scale

Fri, 30 Sep 2022 00:00:00 +0000

I wrote my first blog post about cosine similarity back in 2019 when the pandemic was out of sight, and most marketing people were unaware of “representational learning.” However, times have changed, and nowadays, many quantitative marketing papers apply word2vec, prod2vec, or similar approaches.

Thanks to gensim, training such a model is straightforward. For example, one can learn the representations of tracks within a few lines of python code:

from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence

model = Word2Vec(
 sentences=LineSentence('./data/tmp_isrc_training.txt'), 
 vector_size=50, window=99, epochs=5, min_count=1, workers=8,
 sg=1, negative=10
)

Multi-hot encoding in JAX

Fri, 19 Aug 2022 00:00:00 +0000

JAX offers a range of practical functions, also for data preparation. One of these is jax.nn.one_hot, which performs a classic one-hot encoding. Unfortunately, I was not able to find a suitable multi-hot equivalent for multi-label applications. However, it is also quite easy to implement the functionality directly in JAX:

import jax
import jax.numpy as jnp
from functools import partial

@partial(jax.jit, static_argnames=("num_classes"))
def multi_hot(labels, num_classes: int):
 return jnp.take(jnp.eye(num_classes), jnp.array(labels), axis=0).sum(axis=0)

Multinomial Logistic Regression in JAX

Fri, 29 Jul 2022 00:00:00 +0000

Classifications are a classic machine learning problem we can tackle using logistic regression. If we distinguish between more than two classes, we call it a multinomial logistic regression. In this post, I will show how this can be done using JAX based on the well-known Fisher’s Iris dataset (every R user should be familiar with this one).

First, we have to load the required libraries and load the data. Since this is a classification, we have a set of predictors (aka. features) and a label for each sample.