Instructions to use ydshieh/tiny-random-AlbertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-AlbertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ydshieh/tiny-random-AlbertForMaskedLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-AlbertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("ydshieh/tiny-random-AlbertForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ydshieh/tiny-random-AlbertForMaskedLM: direct link, hf CLI and curl.
- Browser
- Download file 1.98 MB
-
https://huggingface.co/ydshieh/tiny-random-AlbertForMaskedLM/resolve/main/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-AlbertForMaskedLM/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-AlbertForMaskedLM/resolve/main/tf_model.h5
1.98 MB
- Xet hash:
- 0daff5edeb456346a5152066a585e7ad964658d6e7ac37ba8fd0136dfb91d378
- Size of remote file:
- 1.98 MB
- SHA256:
- 00c3315d431190a7d5a04a82faa7ea5622fec3dd73d78142732f479cbd0e64de
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