Instructions to use AISE-TUDelft/Custom-Activations-BERT-GELU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AISE-TUDelft/Custom-Activations-BERT-GELU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AISE-TUDelft/Custom-Activations-BERT-GELU")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AISE-TUDelft/Custom-Activations-BERT-GELU") model = AutoModelForMaskedLM.from_pretrained("AISE-TUDelft/Custom-Activations-BERT-GELU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AISE-TUDelft/Custom-Activations-BERT-GELU: direct link, hf CLI and curl.
- Browser
- Download file 39.5 MB
-
https://huggingface.co/AISE-TUDelft/Custom-Activations-BERT-GELU/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AISE-TUDelft/Custom-Activations-BERT-GELU/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AISE-TUDelft/Custom-Activations-BERT-GELU/resolve/main/pytorch_model.bin
39.5 MB
- Xet hash:
- 2dce8a74d801a8fce143376a6ac89ab3c67fe85caee32c36413030001b90fdb5
- Size of remote file:
- 39.5 MB
- SHA256:
- 2bd9d471bd049abbdc6ae1d4201a0bf0e92dfd75363602377cfb62ed01647b6c
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