Text Classification
Transformers
Safetensors
Spanish
modernbert
multi-label-classification
politics
spanish
costa-rica
knowledge-distillation
manifesto-project
text-embeddings-inference
Instructions to use PENCR/mrbert-es-ld1-pe2-multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PENCR/mrbert-es-ld1-pe2-multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PENCR/mrbert-es-ld1-pe2-multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PENCR/mrbert-es-ld1-pe2-multilabel") model = AutoModelForSequenceClassification.from_pretrained("PENCR/mrbert-es-ld1-pe2-multilabel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78bd44a8bc1b829e318a88de32da7042b2fdccff30e76bb35e96cea62ce33f6d
- Size of remote file:
- 1.07 MB
- SHA256:
- ed8dc3e139a6f2c6e1781996aabfef34c32241dcff263dbc66cf69b4760aeee9
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