Instructions to use moussaKam/barthez with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moussaKam/barthez with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="moussaKam/barthez")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("moussaKam/barthez") model = AutoModelForSeq2SeqLM.from_pretrained("moussaKam/barthez", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moussaKam/barthez: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/moussaKam/barthez/resolve/a733cdd537dd4a13f203c6f0889e26e9dfedbb68/pytorch_model.bin
- Command line
-
hf download hf://moussaKam/barthez@a733cdd537dd4a13f203c6f0889e26e9dfedbb68/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moussaKam/barthez/resolve/a733cdd537dd4a13f203c6f0889e26e9dfedbb68/pytorch_model.bin
557 MB
- Xet hash:
- e4ebd57de597e2a9e6bdf549e5e7c37125e947faf9b756dcf07fe02c6782335e
- Size of remote file:
- 557 MB
- SHA256:
- 5b5ce8af0cc38247421778ab5697014376e7a6f22ba5b09ed1ccdd4efc713270
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