Instructions to use abhi1nandy2/EManuals_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhi1nandy2/EManuals_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abhi1nandy2/EManuals_BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abhi1nandy2/EManuals_BERT") model = AutoModelForMaskedLM.from_pretrained("abhi1nandy2/EManuals_BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from abhi1nandy2/EManuals_BERT: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/abhi1nandy2/EManuals_BERT/resolve/main/training_args.bin
- Command line
-
hf download hf://abhi1nandy2/EManuals_BERT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/abhi1nandy2/EManuals_BERT/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 8957d4d681c263ab8bcb70800438bed14646ec92fb24a6d8d74ce2d0fec1cb87
- Size of remote file:
- 2.8 kB
- SHA256:
- c8fc0bc41e3b415ce7e62d0c4ed873addc70c30c7cad392ea871b3999aefaadb
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