Instructions to use BayesTensor/debertas_seeker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BayesTensor/debertas_seeker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BayesTensor/debertas_seeker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BayesTensor/debertas_seeker") model = AutoModelForSequenceClassification.from_pretrained("BayesTensor/debertas_seeker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from BayesTensor/debertas_seeker: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/BayesTensor/debertas_seeker/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://BayesTensor/debertas_seeker/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/BayesTensor/debertas_seeker/resolve/main/last-checkpoint/optimizer.pt
1.48 GB
- Xet hash:
- 61c88ff27540de7f140467ba2616ddf276d247399977d3b1315f2d717791413b
- Size of remote file:
- 1.48 GB
- SHA256:
- bb141463b53f69fc963e09ea0da9ba5fda33860798b350c5ab616411d738cbc5
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