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camembert-ner

Jean-Baptiste

Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location entity|precision|recall|f1 -|-|-|- PER|0.9372|0.9598|0.9483 ORG|0.8099|0.8265|0.8181 LOC|0.8905|0.9005|0.8955 MISC|0.8175|0.8117|0.8146 For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa

Text analysis
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Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location In order to simplify, the prefix B- or I- from original conll2003 was removed. I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size: Model performances computed on conll2003 validation dataset (computed on the tokens predictions)

Text analysis
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These are quants for the DS4 inference engine, built with an importance matrix (imatrix) and with tensor offsets aligned for efficient mmap on Apple Silicon. They are a drop-in replacement for the files published in antirez/deepseek-v4-gguf, with two changes: The MTP file is reproduced here unchanged so a single repo holds everything `download_model.sh` expects. Use the IQ2XXS quant on 128 GB Mac machines, pair it with the MTP file for optional speculative decoding.

Text generation
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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