Instructions to use ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1") model = AutoModelForSequenceClassification.from_pretrained("ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1: direct link, hf CLI and curl.
- Browser
- Download file 997 MB
-
https://huggingface.co/ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1/resolve/main/optimizer.pt
- Command line
-
hf download hf://ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model_V1/resolve/main/optimizer.pt
997 MB
- Xet hash:
- bce7bcd9a629d9acce5412ed2e34b997355642688f2f29fa9f00b5f5c3ff66bc
- Size of remote file:
- 997 MB
- SHA256:
- b02207559892ba06b1b123c3034e419c8a158f29b3c18ed5d0a498d9dc994464
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