Instructions to use l3cube-pune/me-bert-mixed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-bert-mixed-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/me-bert-mixed-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-bert-mixed-v2") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/me-bert-mixed-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/me-bert-mixed-v2: direct link, hf CLI and curl.
- Browser
- Download file 951 MB
-
https://huggingface.co/l3cube-pune/me-bert-mixed-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/me-bert-mixed-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/me-bert-mixed-v2/resolve/main/pytorch_model.bin
951 MB
- Xet hash:
- 4eda918cd5aa97a70161d33c0651aa2647112f6bc1195f0a88e551ddaceab3ea
- Size of remote file:
- 951 MB
- SHA256:
- d66948e6f7993d7abcec2fb7a3ebeee95b7e76ce82c4151f060795340314ae76
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