Classify Text in 10 Lines with a Hugging Face Transformers Pipeline

A tutorial by Mariana Souza demonstrates how to classify text sentiment in 10 lines of Python using a Hugging Face Transformers pipeline with a pretrained DistilBERT model, running entirely on CPU with no GPU or training required. The script, built with transformers 5.14.1 and PyTorch 2.13.0, labels text as POSITIVE or NEGATIVE using the 67M-parameter distilbert-base-uncased-finetuned-sst-2-english model, achieving scores above 0.99 on example reviews.

Classify Text in 10 Lines with a Hugging Face Transformers Pipeline Install Transformers, load a pretrained DistilBERT model, and classify sentiment locally with no GPU or training. Mariana Souza https://sourcefeed.dev/u/mariana souza What you'll build A Python script that labels any text as POSITIVE or NEGATIVE sentiment using a pretrained DistilBERT model from the Hugging Face Hub — running entirely on your CPU. The core logic is 10 lines. No training, no GPU, no API key. Prerequisites Python 3.10+ — Transformers https://huggingface.co/docs/transformers v5 requires it. Verified here with Python 3.13.5. Verified library versions: transformers 5.14.1, PyTorch https://pytorch.org 2.13.0 CPU build . ~1.5 GB free disk — the CPU PyTorch wheel plus a ~270 MB model download. Internet access on first run — the model downloads once, then loads from the local cache at ~/.cache/huggingface/hub .- No Hugging Face account or token needed; the model is public. Commands below are for macOS/Linux. On Windows, activate the virtual environment with .venv\Scripts\activate instead. 1. Create a project and virtual environment Keep this isolated from your system Python — Transformers pulls in a dozen dependencies you don't want globally. mkdir first-pipeline && cd first-pipeline python3 -m venv .venv source .venv/bin/activate Your prompt should now show .venv . 2. Install PyTorch and Transformers Transformers v5 is PyTorch-only TensorFlow and Flax backends were dropped in 5.0 . Install the CPU build of torch explicitly — on Linux this skips several gigabytes of CUDA libraries you don't need for this tutorial: pip install torch --index-url https://download.pytorch.org/whl/cpu pip install transformers Confirm both landed: python python -c "import torch, transformers; print torch. version , transformers. version " Expected: 2.13.0+cpu 5.14.1 plain 2.13.0 on macOS or newer. 3. Write the classifier Create classify.py : python from transformers import pipeline classifier = pipeline "text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", reviews = "The battery life on this laptop is incredible.", "Shipping took three weeks and the box arrived crushed.", for review, result in zip reviews, classifier reviews : print f"{result 'label' :<8} {result 'score' :.4f} {review}" Two things worth knowing. pipeline bundles the tokenizer, model, and post-processing into one callable — you pass raw strings, it returns dicts with a label and a score the model's probability for that label . And the model is pinned explicitly: pipeline "text-classification" alone works, but it logs a "No model was supplied" warning and leaves you at the mercy of whatever default Hugging Face picks later. The pinned checkpoint, distilbert-base-uncased-finetuned-sst-2-english https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english , is a 67M-parameter DistilBERT fine-tuned on Stanford Sentiment Treebank movie reviews — small enough to run comfortably on any laptop CPU. 4. Run it python classify.py The first run downloads the model ~268 MB and tokenizer files, then caches them. Subsequent runs load from disk and classify both sentences in well under a second. Verify it works You should see exactly this scores may differ in the last decimal places across platforms : POSITIVE 0.9998 The battery life on this laptop is incredible. NEGATIVE 0.9997 Shipping took three weeks and the box arrived crushed. If both labels match with scores above 0.99, everything works. Swap in your own sentences and rerun — no re-download happens. Troubleshooting ModuleNotFoundError: No module named 'transformers' — you're running a Python outside the virtual environment. Check that your prompt shows .venv , re-run source .venv/bin/activate , and confirm with which python that it points into .venv . OSError: We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files. — the Hub is unreachable on first download. Check your connection and proxy settings, and make sure HF HUB OFFLINE isn't set in your shell echo $HF HUB OFFLINE should print nothing . Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF TOKEN to enable higher rate limits and faster downloads. — informational, not an error; the download still works. To silence it, create a free token at huggingface.co/settings/tokens and export it as HF TOKEN . ERROR: Could not find a version that satisfies the requirement transformers — pip found no compatible wheel, which almost always means Python older than 3.10. Run python3 --version and install a current Python before recreating the venv. Next steps - Pass top k=None to pipeline to get scores for every label instead of just the winner — useful for confidence thresholds. - Swap the model string for cardiffnlp/twitter-roberta-base-sentiment-latest https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest adds a neutral class, tuned for social media — nothing else in the code changes. That interchangeability is the pipeline API's whole point. - Try pipeline "zero-shot-classification" to classify against your own arbitrary labels with no fine-tuning. - Got an NVIDIA GPU? Install the CUDA build of torch and pass device="cuda" to pipeline . - The Pipeline API reference https://huggingface.co/docs/transformers/main classes/pipelines lists every supported task, and the free Hugging Face LLM Course https://huggingface.co/learn/llm-course goes from here to fine-tuning your own models. Sources & further reading - Transformers Installation https://huggingface.co/docs/transformers/en/installation — huggingface.co - Pipelines API Reference https://huggingface.co/docs/transformers/en/main classes/pipelines — huggingface.co - Transformers Quickstart https://huggingface.co/docs/transformers/en/quicktour — huggingface.co - DistilBERT base uncased finetuned SST-2 model card https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english — huggingface.co - PyTorch Get Started Locally https://pytorch.org/get-started/locally/ — pytorch.org Mariana Souza https://sourcefeed.dev/u/mariana souza · Senior Editor Mariana covers the fast-moving world of machine learning and generative AI, with a particular focus on how these technologies are reshaping development workflows. When she isn't stress-testing the latest foundation models, she's usually at a local hackathon. Discussion 0 No comments yet Be the first to weigh in.