Feature Extraction
Transformers
TensorBoard
French
deberta-v2
deberta-v3
debertav2
debertav3
camembert
Instructions to use almanach/camembertv2-base-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembertv2-base-ckpts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="almanach/camembertv2-base-ckpts")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("almanach/camembertv2-base-ckpts", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
Upload checkpoints/iter_ckpt_rank_10/iter_ckpt_rank_10-271500.index with huggingface_hub
9d3ac27 verified - iter_ckpt_rank_00
- iter_ckpt_rank_01
- iter_ckpt_rank_02
- iter_ckpt_rank_03
- iter_ckpt_rank_04
- iter_ckpt_rank_05
- iter_ckpt_rank_06
- iter_ckpt_rank_07
- iter_ckpt_rank_08
- iter_ckpt_rank_09
- iter_ckpt_rank_10
- iter_ckpt_rank_11
- iter_ckpt_rank_12
- iter_ckpt_rank_13
- iter_ckpt_rank_14
- iter_ckpt_rank_15
- summaries
- 46.9 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB
- 1.34 GB xet
- 38.3 kB