Instructions to use EuroBERT/EuroBERT-210m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EuroBERT/EuroBERT-210m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EuroBERT/EuroBERT-210m", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -94,15 +94,15 @@ The EuroBERT family exhibits strong multilingual performance across domains and
|
|
| 94 |
- The smaller EuroBERT-210m generally outperforms all similarly sized systems.
|
| 95 |
|
| 96 |
<div>
|
| 97 |
-
<img src="img/multilingual.
|
| 98 |
</div>
|
| 99 |
|
| 100 |
<div>
|
| 101 |
-
<img src="img/code_math.
|
| 102 |
</div>
|
| 103 |
|
| 104 |
<div>
|
| 105 |
-
<img src="img/long_context.
|
| 106 |
</div>
|
| 107 |
|
| 108 |
### Suggested Fine-Tuning Hyperparameters
|
|
|
|
| 94 |
- The smaller EuroBERT-210m generally outperforms all similarly sized systems.
|
| 95 |
|
| 96 |
<div>
|
| 97 |
+
<img src="img/multilingual.png" width="100%" alt="EuroBERT" />
|
| 98 |
</div>
|
| 99 |
|
| 100 |
<div>
|
| 101 |
+
<img src="img/code_math.png" width="100%" alt="EuroBERT" />
|
| 102 |
</div>
|
| 103 |
|
| 104 |
<div>
|
| 105 |
+
<img src="img/long_context.png" width="100%" alt="EuroBERT" />
|
| 106 |
</div>
|
| 107 |
|
| 108 |
### Suggested Fine-Tuning Hyperparameters
|