EuroBERT
/

EuroBERT-2.1B

@@ -1,199 +1,119 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+- fr
+- de
+- es
+- zh
+- it
+- ru
+- pl
+- pt
+- ja
+- vi
+- nl
+- ar
+- tr
+- hi
+pipeline_tag: fill-mask
+tags:
+- code
 ---
+# EuroBERT-210m
+<div>
+  <img src="img/banner.png" width="100%"  alt="EuroBERT" />
+</div>
+## Table of Contents
+1. [Overview](#overview)
+2. [Usage](#Usage)
+3. [Evaluation](#Evaluation)
+4. [License](#license)
+5. [Citation](#citation)
+## Overview
+EuroBERT is a family of multilingual encoder models designed for a variety of tasks—such as classification, retrieval, or evaluation metrics— supporting 15 languages, mathematics and code, and sequences of up to 8,192 tokens.
+EuroBERT models exhibit the strongest multilingual performance across [domains and tasks](#Evaluation) compared to similarly sized systems.
+It is available in 3 sizes:
+- [EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) - 210 million parameters
+- [EuroBERT-610m](https://huggingface.co/EuroBERT/EuroBERT-610m) - 610 million parameters
+- [EuroBERT-2.1B](https://huggingface.co/EuroBERT/EuroBERT-2.1B) - 2.1 billion parameters
+For more information about EuroBERT, please check our [blog](***) post and the [arXiv](***) preprint.
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+model_id = "EuroBERT/EuroBERT-2.1B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)
+text = "The capital of France is <|mask|>."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+# To get predictions for the mask:
+masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
+predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+print("Predicted token:", predicted_token)
+# Predicted token:  Paris
+```
+**💻 You can use these models directly with the transformers library starting from v4.48.0:**
+```sh
+pip install -U transformers>=4.48.0
+```
+**🏎️ If your GPU supports it, we recommend using EuroBERT with Flash Attention 2 to achieve the highest efficiency. To do so, install Flash Attention 2 as follows, then use the model as normal:**
+```bash
+pip install flash-attn
+```
 ## Evaluation
+We evaluate EuroBERT on a suite of tasks to cover various real-world use cases for multilingual encoders, including retrieval performance, classification, sequence regression, quality estimation, summary evaluation, code-related tasks, and mathematical tasks.
+**Key highlights:**
+The EuroBERT family exhibits strong multilingual performance across domains and tasks.
+- EuroBERT-2.1B, our largest model, achieves the highest performance among all evaluated systems. It outperforms the largest system, XLM-RoBERTa-XL.
+- EuroBERT-610m is competitive with XLM-RoBERTa-XL, a model 5 times its size, on most multilingual tasks and surpasses it in code and mathematics tasks.
+- The smaller EuroBERT-210m generally outperforms all similarly sized systems.
+<div>
+  <img src="img/multilingual.png" width="100%"  alt="EuroBERT" />
+</div>
+<div>
+  <img src="img/code_math.png" width="100%"  alt="EuroBERT" />
+</div>
+<div>
+  <img src="img/long_context.png" width="100%"  alt="EuroBERT" />
+</div>
+## License
+We release the EuroBERT model architectures, model weights, and training codebase under the Apache 2.0 license.
+## Citation
+If you use EuroBERT in your work, please cite:
+```
+SOON
+```