Instructions to use emanjavacas/MacBERTh-ing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/MacBERTh-ing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emanjavacas/MacBERTh-ing")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emanjavacas/MacBERTh-ing") model = AutoModelForSequenceClassification.from_pretrained("emanjavacas/MacBERTh-ing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from emanjavacas/MacBERTh-ing: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/emanjavacas/MacBERTh-ing/resolve/main/scheduler.pt
- Command line
-
hf download hf://emanjavacas/MacBERTh-ing/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/emanjavacas/MacBERTh-ing/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- bb19b454cd48a2953a49d9a5e6ee1b0466ebf3a4b4fc5c9f45509633e4444703
- Size of remote file:
- 623 Bytes
- SHA256:
- 51b918da4fc3cbae9fddacc2c9f670190e23bf5af81a10ace564c01ef0a89855
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