Fill-Mask
Transformers
ONNX
Safetensors
Bashkir
bashkir-roberta-preln
bashkir
masked-language-modeling
roberta
sentencepiece
custom-code
onnxruntime
custom_code
Instructions to use failed09/bashkir-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use failed09/bashkir-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="failed09/bashkir-roberta", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("failed09/bashkir-roberta", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm_bashkir_bert_16k.model from failed09/bashkir-roberta: direct link, hf CLI and curl.
- Browser
- Download file 587 kB
-
https://huggingface.co/failed09/bashkir-roberta/resolve/main/spm_bashkir_bert_16k.model
- Command line
-
hf download hf://failed09/bashkir-roberta/spm_bashkir_bert_16k.model
-
curl -L -o spm_bashkir_bert_16k.model https://huggingface.co/failed09/bashkir-roberta/resolve/main/spm_bashkir_bert_16k.model
587 kB
- Xet hash:
- 2a6c078dd07ef9b82895c92c9615d8a4a45dc71cef31704cb78d729afef47a81
- Size of remote file:
- 587 kB
- SHA256:
- 4e77b9cfc9ab8c200406ee46c2d20fc46734da9ddcf01dff0f0e370f6c4ac244
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.