Instructions to use ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model 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 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")# 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") model = AutoModelForSequenceClassification.from_pretrained("ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ManojAlexender/Research_paper_MLM_all_CGO_Level_2_Final_Model/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 6601ec5e0a4f4bbb262092cca263cc6df3ba5e72629ba96c0097ab2af49c11ff
- Size of remote file:
- 4.66 kB
- SHA256:
- 55cbbc9dc2583f7316483340f7e71af41ca78ac221f5923fdc114bcc6e11c329
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.