Instructions to use almanach/camembertv2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembertv2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="almanach/camembertv2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("almanach/camembertv2-base") model = AutoModelForMaskedLM.from_pretrained("almanach/camembertv2-base") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
#3
by itazap HF Staff - opened
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -51,7 +51,7 @@
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"model_max_length": 1024,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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-
"tokenizer_class": "
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"trim_offsets": true,
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"unk_token": "[UNK]"
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}
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"model_max_length": 1024,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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+
"tokenizer_class": "PreTrainedTokenizerFast",
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"trim_offsets": true,
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"unk_token": "[UNK]"
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}
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