Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
text
custom_code
Instructions to use PantagrueLLM/text-base-wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/text-base-wiki with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/text-base-wiki", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/text-base-wiki", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/text-base-wiki 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" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56d41ce13b9a16fc0463fa9a339c58568c61ed00e29752736c47b87dd62d3d2b
- Size of remote file:
- 499 MB
- SHA256:
- 44d17393282f9ea57362190fa40f80e5810b271f95ce9cdbe1d5f771fde526b2
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