Instructions to use skimai/spanberta-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skimai/spanberta-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skimai/spanberta-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("skimai/spanberta-base-cased") model = AutoModel.from_pretrained("skimai/spanberta-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("skimai/spanberta-base-cased")
model = AutoModel.from_pretrained("skimai/spanberta-base-cased", device_map="auto")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skimai/spanberta-base-cased")