# Classify Text in 10 Lines with a Hugging Face Transformers Pipeline > Source: > Published: 2026-07-24 11:40:52+00:00 # 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.