Instructions to use andreasmadsen/efficient_mlm_m0.20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreasmadsen/efficient_mlm_m0.20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="andreasmadsen/efficient_mlm_m0.20")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.20") model = AutoModelForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from andreasmadsen/efficient_mlm_m0.20: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/andreasmadsen/efficient_mlm_m0.20/resolve/main/tf_model.h5
- Command line
-
hf download hf://andreasmadsen/efficient_mlm_m0.20/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/andreasmadsen/efficient_mlm_m0.20/resolve/main/tf_model.h5
1.63 GB
- Xet hash:
- 4d7224023596a31fdd9f9c8bca89ff286a3df360394adad91cd10d6fd21948a1
- Size of remote file:
- 1.63 GB
- SHA256:
- 063ec0909533f64bc50f0cd7a75d9f55f472b2a95a3b987e3a28113f19aa97c2
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