Instructions to use datalama/mmBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datalama/mmBERT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="datalama/mmBERT-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("datalama/mmBERT-small") model = AutoModelForMaskedLM.from_pretrained("datalama/mmBERT-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from datalama/mmBERT-small: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/datalama/mmBERT-small/resolve/main/tokenizer.json
- Command line
-
hf download hf://datalama/mmBERT-small/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/datalama/mmBERT-small/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- f7c9fa051db948708020fea121f6bc818b89cea0bf2ca9e53ea10760d12f2001
- Size of remote file:
- 17.5 MB
- SHA256:
- 197d4cc5406ee12cc50c8b5511f2393cc32d9db321545979ce041c1199178356
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.