Instructions to use Recognai/distilbert-base-es-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Recognai/distilbert-base-es-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Recognai/distilbert-base-es-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Recognai/distilbert-base-es-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("Recognai/distilbert-base-es-multilingual-cased") - Notebooks
- Google Colab
- Kaggle
DistilBERT base multilingual model Spanish subset (cased)
This model is the Spanish extract of distilbert-base-multilingual-cased (https://huggingface.co/distilbert-base-multilingual-cased), a distilled version of the BERT base multilingual model. This model is cased: it does make a difference between english and English.
It uses the extraction method proposed by Geotrend described in https://github.com/Geotrend-research/smaller-transformers.
The resulting model has the same architecture as DistilmBERT: 6 layers, 768 dimension and 12 heads, with a total of 63M parameters (compared to 134M parameters for DistilmBERT).
The goal of this model is to reduce even further the size of the distilbert-base-multilingual multilingual model by selecting only most frequent tokens for Spanish, reducing the size of the embedding layer. For more details visit the paper from the Geotrend team: Load What You Need: Smaller Versions of Multilingual BERT.
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