# Pipeline tutorial, summarization doesn't work **URL:** \ **Category:** 🤗Transformers\ **Created:** [March 31, 2026, 9:35am UTC](https://discuss.huggingface.co/t/pipeline-tutorial-summarization-doesnt-work/174833 "2026-03-31T09:35:36Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![bacca400](https://avatars.discourse-cdn.com/v4/letter/b/ee59a6/32.png) [@bacca400](https://discuss.huggingface.co/u/bacca400)\ **Post date:** [March 31, 2026, 9:35am UTC](https://discuss.huggingface.co/t/pipeline-tutorial-summarization-doesnt-work/174833/1 "2026-03-31T09:35:36Z")
I’m doing a HF tutorial on transformers at [Transformers, what can they do? · Hugging Face](https://huggingface.co/learn/llm-course/chapter1/3). It uses a Colab notebook at [Google Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter1/section3.ipynb) . When I try to run the code for a summarizer, which begins with this: ```auto from transformers import pipeline summarizer = pipeline("summarization") ``` I get an error ```auto --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /tmp/ipykernel_13970/3730791013.py in () 1 from transformers import pipeline 2 ----> 3 summarizer = pipeline("summarization") 4 summarizer( 5 """ 2 frames/usr/local/lib/python3.12/dist-packages/transformers/pipelines/ __init__.py in pipeline(task, model, config, tokenizer, feature_extractor, image_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs) 775 ) 776 else: --> 777 normalized_task, targeted_task, task_options = check_task(task) 778 if pipeline_class is None: 779 pipeline_class = targeted_task["impl"] /usr/local/lib/python3.12/dist-packages/transformers/pipelines/ __init__.py in check_task(task) 379 380 """ --> 381 return PIPELINE_REGISTRY.check_task(task) 382 383 /usr/local/lib/python3.12/dist-packages/transformers/pipelines/base.py in check_task(self, task) 1354 raise KeyError(f"Invalid translation task {task}, use 'translation_XX_to_YY' format") 1355 -> 1356 raise KeyError( 1357 f"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}" 1358 ) KeyError: "Unknown task summarization, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']" ``` The pipeline no longer supports “summarization”. Is there a way to get this to work. I’m mainly curious. It appears the pipeline module may have been updated but the Colab notebook was not. Thanks! ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [March 31, 2026, 9:47am UTC](https://discuss.huggingface.co/t/pipeline-tutorial-summarization-doesnt-work/174833/2 "2026-03-31T09:47:42Z")
Yeah. It’s real [version incompatibility between Transformers v4 and v5](https://discuss.huggingface.co/t/issue-with-summarization-and-translation-pipeline/173919). If go with v5, try without pipeline: ```py from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_id = "google-t5/t5-small" # or your finetuned summarization checkpoint tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSeq2SeqLM.from_pretrained(model_id) inputs = tokenizer( "summarize: " + text, return_tensors="pt", truncation=True ).input_ids outputs = model.generate(inputs, max_new_tokens=100, do_sample=False) summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print(summary) ``` Or try another supported pipeline: ```py from transformers import pipeline summarizer = pipeline("text-generation", model="Qwen/Qwen3-4B-Instruct-2507") messages = [ { "role": "user", "content": "Summarize the following text in 3 bullet points:\n\n" + text } ] out = summarizer(messages, max_new_tokens=200) print(out[0]["generated_text"][-1]["content"]) ``` ---
**Author:** ![bacca400](https://avatars.discourse-cdn.com/v4/letter/b/ee59a6/32.png) [@bacca400](https://discuss.huggingface.co/u/bacca400)\ **Post date:** [March 31, 2026, 9:51am UTC](https://discuss.huggingface.co/t/pipeline-tutorial-summarization-doesnt-work/174833/3 "2026-03-31T09:51:44Z")
Thank you John, you are helpful as always. I think this tutorial is from 2022 so it makes sense something is outdated. I won’t be programming an AI myself but I still need to learn the basics. ---
**Author:** ![bacca400](https://avatars.discourse-cdn.com/v4/letter/b/ee59a6/32.png) [@bacca400](https://discuss.huggingface.co/u/bacca400)\ **Post date:** [March 31, 2026, 9:56am UTC](https://discuss.huggingface.co/t/pipeline-tutorial-summarization-doesnt-work/174833/4 "2026-03-31T09:56:02Z")
This worked in Colab in my own notebook. ```auto # Fixed code from this tutorial: # https://huggingface.co/learn/llm-course/chapter1/3 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_id = "google-t5/t5-small" # or your finetuned summarization checkpoint tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSeq2SeqLM.from_pretrained(model_id) text = """ America has changed dramatically during recent years. Not only has the number of graduates in traditional engineering disciplines such as mechanical, civil, electrical, chemical, and aeronautical engineering declined, but in most of the premier American universities engineering curricula now concentrate on and encourage largely the study of engineering science. As a result, there are declining offerings in engineering subjects dealing with infrastructure, the environment, and related issues, and greater concentration on high technology subjects, largely supporting increasingly complex scientific developments. While the latter is important, it should not be at the expense of more traditional engineering. Rapidly developing economies such as China and India, as well as other industrial countries in Europe and Asia, continue to encourage and advance the teaching of engineering. Both China and India, respectively, graduate six and eight times as many traditional engineers as does the United States. Other industrial countries at minimum maintain their output, while America suffers an increasingly serious decline in the number of engineering graduates and a lack of well-educated engineers. """ inputs = tokenizer( "summarize: " + text, return_tensors="pt", truncation=True ).input_ids outputs = model.generate(inputs, max_new_tokens=100, do_sample=False) summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print(summary) ``` The program did have some warnings and did not wrap the output text “summary” but that’s fine.