Instructions to use ClassCat/roberta-small-basque with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClassCat/roberta-small-basque with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ClassCat/roberta-small-basque")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ClassCat/roberta-small-basque") model = AutoModelForMaskedLM.from_pretrained("ClassCat/roberta-small-basque", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language: eu
license: cc-by-sa-4.0
datasets:
- cc100
- oscar
widget:
- text: "Euria egingo <mask> gaur ?"
- text: "<mask> umeari liburua eman dio."
- text: "Zein da zure <mask> ?"
---
## RoBERTa Basque small model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of RoBERTa base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of [CC-100/eu](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data
* Subset of [oscar](https://huggingface.co/datasets/oscar)
### Usage
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='ClassCat/roberta-small-basque')
unmasker("Zein da zure <mask> ?")
``` |