# Issue with summarization and translation pipeline
**URL:**
**Author:**  [@yuvrajsharma000](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fyuvrajsharma000)%5C
**Post date:** [March 1, 2026, 11:26am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fissue-with-summarization-and-translation-pipeline%2F173919%2F1 "2026-03-01T11:26:50Z")
1. summarization as a task in pipeline object is not available.
2. Similar issue for translation pipeline
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**Author:**  [@system](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fsystem)%5C
**Post date:** [March 1, 2026, 11:26am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fissue-with-summarization-and-translation-pipeline%2F173919%2F2 "2026-03-01T11:26:51Z")
Unlisted on March 1, 2026, 11:26am UTC
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**Author:**  [@system](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fsystem)%5C
**Post date:** [March 2, 2026, 12:16am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fissue-with-summarization-and-translation-pipeline%2F173919%2F3 "2026-03-02T00:16:05Z")
Listed on March 2, 2026, 12:16am UTC
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**Author:**  [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [March 2, 2026, 4:27am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fissue-with-summarization-and-translation-pipeline%2F173919%2F4 "2026-03-02T04:27:51Z")
This is because [several pipelines were removed in the upgrade from Transformers v4 to v5](https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md).
* * *
## Why `pipeline("summarization")` and translation pipelines fail
### 1) In Transformers **v5.x** , those pipeline tasks were **removed**
Transformers **v5** removed the legacy text-to-text pipelines:
- `Text2TextGenerationPipeline`
- and the related `SummarizationPipeline` / `TranslationPipeline`
The migration guide states these were deprecated and are now removed, recommending modern chat models via `TextGenerationPipeline` instead. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/main/MIGRATION_GUIDE_V5.md "raw.githubusercontent.com"))
### 2) `pipeline()` only accepts tasks that are **registered**
`pipeline("…")` doesn’t “infer” what you mean; it checks a registry (`SUPPORTED_TASKS`). In **v5.2.0** , the list of accepted tasks includes things like `text-generation`, `question-answering`, etc., but **does not include** `summarization` or `translation`. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/v5.2.0/src/transformers/pipelines/ __init__.py "raw.githubusercontent.com"))
So:
- `pipeline("summarization")` → **Unknown task / task not available**
- `pipeline("translation…")` → similarly fails (because the pipeline task is not registered)
### 3) It’s confusing because older docs/tutorials still show the old pattern
There’s an open issue noting that the summarization/translation docs still referenced `pipeline()` examples, which misleads users on v5. ([GitHub](https://github.com/huggingface/transformers/issues/43827 "Summarization/Translation docs still reference pipeline() after v5 pipeline removals · Issue #43827 · huggingface/transformers · GitHub"))
### 4) Translation errors can be misleading
There’s also a reported bug where the error message suggests `translation_XX_to_YY` tasks are available, but they still fail because `"translation"` isn’t actually supported in v5’s pipeline registry. ([GitHub](https://github.com/huggingface/transformers/issues/43825 "pipeline() error message incorrectly suggests translation tasks are supported in v5 · Issue #43825 · huggingface/transformers · GitHub"))
### 5) This is not just you—downstream libraries broke too
For example, MLflow users reported breakage because `TranslationPipeline` / `SummarizationPipeline` classes no longer exist in v5. ([GitHub](https://github.com/mlflow/mlflow/issues/20604 "[FR] Add support for transformers 5.x (TranslationPipeline, SummarizationPipeline removed) · Issue #20604 · mlflow/mlflow · GitHub"))
* * *
## Quick confirmation (recommended first)
Run this to confirm you’re on v5 and see what pipeline tasks exist:
```python
import transformers
from transformers.pipelines import get_supported_tasks
print(transformers. __version__ )
print(get_supported_tasks())
```
If the printed task list doesn’t include `summarization` / `translation`, you’re in the v5 behavior described above. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/v5.2.0/src/transformers/pipelines/ __init__.py "raw.githubusercontent.com"))
* * *
## Solutions (choose one)
## Option A — Keep the old pipeline API (pin to v4)
If you want `pipeline("summarization")` and `pipeline("translation_xx_to_yy")` to work as in older tutorials, install Transformers v4:
```bash
pip install -U "transformers<5"
```
Why this works: v4 pipeline docs explicitly describe summarization and `translation_xx_to_yy` pipeline tasks as supported. ([Hugging Face](https://huggingface.co/docs/transformers/v4.57.1/zh/main_classes/pipelines "Pipelines"))
Use this option if:
- you’re following older notebooks/courses
- you want the classic pipeline outputs (`{"summary_text": ...}`, `{"translation_text": ...}`)
* * *
## Option B — Stay on v5 and use **seq2seq models via `generate()`**
Even though the convenience pipelines are gone, **seq2seq models still work** (e.g., BART/T5/Marian/NLLB). You just call `model.generate()` yourself.
The official generation docs recommend `max_new_tokens` to control output length. ([Hugging Face](https://huggingface.co/docs/transformers/en/main_classes/text_generation "Generation"))
### Summarization example (BART)
```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "facebook/bart-large-cnn"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device).eval()
text = "YOUR ARTICLE HERE"
inputs = tok(text, return_tensors="pt", truncation=True).to(device)
with torch.inference_mode():
out = model.generate(
**inputs,
max_new_tokens=150, # recommended way to control output length
num_beams=4,
no_repeat_ngram_size=3,
)
summary = tok.decode(out[0], skip_special_tokens=True)
print(summary)
```
### Translation example (Marian)
```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "Helsinki-NLP/opus-mt-en-ja"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device).eval()
text = "Hello, how are you?"
inputs = tok(text, return_tensors="pt", truncation=True).to(device)
with torch.inference_mode():
out = model.generate(**inputs, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))
```
### Important translation pitfall (multilingual models)
If you use models like **NLLB** or **M2M100** , you typically must force the target language token using `forced_bos_token_id`. ([Hugging Face](https://huggingface.co/docs/transformers/v4.41.1/model_doc/nllb "NLLB - Transformers"))
* * *
## Option C — Follow the v5 recommendation: use `pipeline("text-generation")` with a chat model
Transformers v5’s migration guide explicitly recommends using `TextGenerationPipeline` with a modern chat model for summarization/translation via prompting. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/main/MIGRATION_GUIDE_V5.md "raw.githubusercontent.com"))
### Summarization via chat model
```python
from transformers import pipeline
summarizer = pipeline("text-generation", model="Qwen/Qwen3-4B-Instruct-2507")
messages = [{"role": "user", "content": "Summarize the following text:\n\n[TEXT]"}]
out = summarizer(messages)
print(out[0]["generated_text"][-1]["content"])
```
### Translation via chat model
Same idea; change the prompt:
```python
from transformers import pipeline
translator = pipeline("text-generation", model="Qwen/Qwen3-4B-Instruct-2507")
messages = [{"role": "user", "content": "Translate this into Japanese:\n\nHello, how are you?"}]
out = translator(messages)
print(out[0]["generated_text"][-1]["content"])
```
Use this option if:
- you want better “instruction-following” behavior without model-family-specific translation settings
- you prefer staying inside the pipeline API (but using `text-generation`)
* * *
## What I would pick (rule of thumb)
- Want **old tutorials to work unchanged** → **Option A** (`transformers<5`)
- Want **deterministic seq2seq translation/summarization** with classic models → **Option B** (`generate()`)
- Want **highest convenience** and often better quality via instruction prompts → **Option C** (`pipeline("text-generation")`)