Instructions to use mbruton/spa_mBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_mBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_mBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_mBERT") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_mBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from mbruton/spa_mBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/mbruton/spa_mBERT/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://mbruton/spa_mBERT@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mbruton/spa_mBERT/resolve/refs%2Fpr%2F2/optimizer.pt
1.42 GB
- Xet hash:
- 7f5ddec7576ed6e5417107059f54370aeb4c0172324be7eb0818a6f4c03f0c4d
- Size of remote file:
- 1.42 GB
- SHA256:
- 8d70f5622d7a0415a8ac4e17332756192a062d3c63cea12c6ef97f179d0e22c4
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