Instructions to use ashraq/bert-random-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashraq/bert-random-weights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ashraq/bert-random-weights")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ashraq/bert-random-weights") model = AutoModelForMaskedLM.from_pretrained("ashraq/bert-random-weights", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ashraq/bert-random-weights: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/ashraq/bert-random-weights/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://ashraq/bert-random-weights@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ashraq/bert-random-weights/resolve/refs%2Fpr%2F1/tokenizer.json
13.6 MB
- Xet hash:
- f7c27219fb0ce445969a1876fcda99323f065c2d8d820594c538c31f55ed2c82
- Size of remote file:
- 13.6 MB
- SHA256:
- 09216b42d2697b7b4a26ac05ff09ba8bf52dc19b896c5ceee8bbff9f39055322
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.