Model overview
CamemBERTav2 is a French language model pretrained on a large corpus of 275B tokens of French text. It is the second version of the CamemBERTa model, which is based on the DebertaV2 architecture. CamemBERTav2 is trained using the Replaced Token Detection (RTD) objective with 20% mask rate on 275B tokens on 32 H100 GPUs. The dataset used for training is a combination of French OSCAR dumps from the CulturaX Project, French scientific documents from HALvest, and the French Wikipedia. The model is a drop-in replacement for the original CamemBERTa model. Note that the new tokenizer is different from the original CamemBERTa tokenizer, so you will need to use Fast Tokenizers to use the model. It will work with `DebertaV2TokenizerFast` from `transformers` library even if the original `DebertaV2TokenizerFast` was sentencepiece-based. The new update includes: - Much larger pretraining dataset: 275B unique tokens (previously ~32B) - A newly built tokenizer based on WordPiece with 32,768 tokens, addition of the newline and tab characters, support emojis, and better handling of numbers (numbers are split into two digits tokens) - Extended context window of 1024 tokens
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。