transformers/docs/source/en/model_doc/eurobert.md

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*This model was published in HF papers on 2025-03-07 and contributed to Hugging Face Transformers on 2026-03-04.*

# EuroBERT

<div style="float: right;">
  <div class="flex flex-wrap space-x-1">
    <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
    <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
  </div>
</div>

## Overview

[EuroBERT](https://huggingface.co/papers/2503.05500) is a multilingual encoder model based on a refreshed transformer architecture, akin to Llama but with bidirectional attention. It supports a mixture of European and widely spoken languages, with sequences of up to 8192 tokens.

You can find all the original EuroBERT checkpoints under the [EuroBERT](https://huggingface.co/collections/EuroBERT/eurobert) collection, or read more about the release in the [EuroBERT blogpost](https://huggingface.co/blog/EuroBERT/release).

The example below demonstrates how to predict the `<|mask|>` token with [`Pipeline`], [`AutoModel`], and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="fill-mask",
    model="EuroBERT/EuroBERT-210m",
    device=0
)
pipeline("Plants create <|mask|> through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch

from transformers import AutoModelForMaskedLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "EuroBERT/EuroBERT-210m",
)
model = AutoModelForMaskedLM.from_pretrained(
    "EuroBERT/EuroBERT-210m",
    device_map="auto",
    attn_implementation="sdpa"
)
inputs = tokenizer("Plants create <|mask|> through a process known as photosynthesis.", return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits

masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)

print(f"The predicted token is: {predicted_token}")
```

</hfoption>
</hfoptions>

## EuroBertConfig

[[autodoc]] EuroBertConfig

## EuroBertModel

[[autodoc]] EuroBertModel
    - forward

## EuroBertForMaskedLM

[[autodoc]] EuroBertForMaskedLM
    - forward

## EuroBertForSequenceClassification

[[autodoc]] EuroBertForSequenceClassification
    - forward

## EuroBertForTokenClassification

[[autodoc]] EuroBertForTokenClassification
    - forward