# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("MMG/mlm-spanish-roberta-base")
model = AutoModelForMaskedLM.from_pretrained("MMG/mlm-spanish-roberta-base")Quick Links
mlm-spanish-roberta-base
This model has a RoBERTa base architecture and was trained from scratch with 3.6 GB of raw text over 10 epochs. 4 Tesla V-100 GPUs were used for the training.
To test the quality of the resulting model we evaluate it over the GLUES benchmark for Spanish NLU. The results are the following:
| Task | Score (metric) |
|---|---|
| XNLI | 71.99 (accuracy) |
| Paraphrasing | 74.85 (accuracy) |
| NER | 85.34 (F1) |
| POS | 97.49 (accuracy) |
| Dependency Parsing | 85.14/81.08 (UAS/LAS) |
| Document Classification | 93.00 (accuracy) |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MMG/mlm-spanish-roberta-base")