Feature Extraction
Transformers
Safetensors
Fairseq
French
pantagruel_uni
fill-mask
data2vec2
JEPA
text
custom_code
Instructions to use PantagrueLLM/text-base-wiki-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/text-base-wiki-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/text-base-wiki-mlm", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/text-base-wiki-mlm", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/text-base-wiki-mlm with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "PantagrueLLM/text-base-wiki-mlm" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfdda78cf04c7aa68241b63474d9de1fb16e952cc6f9cf05d6899770f4b8ef25
- Size of remote file:
- 654 MB
- SHA256:
- fa851cb374a0713156b2f9b93fd8b5da6d9a6e8622e81fe33e3c2fa5e3fface6
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