The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on github. The base tokenizer and model used for training is *'camembert-base'*. http://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf The usage of this model follows the common transformers patterns. Here is a short example of its usage:
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View details ↗>Chaotic systems are intrinsically sensitive to small errors, challenging efforts to construct predictive data-driven models of real-world dynamical systems such as fluid flows or neuronal activity. Prior efforts comprise either specialized models trained separately on individual time series, or foundation models trained on vast time series databases with little underlying dynamical structure. Motivated by dynamical systems theory, we present Panda, Patched Attention for Nonlinear DynAmics. We train Panda on a novel synthetic, extensible dataset of 2 \times 10^4 chaotic dynamical systems that we discover using an evolutionary algorithm. Trained purely on simulated data, Panda exhibits emergent properties: zero-shot forecasting of unseen real world chaotic systems, and nonlinear resonance patterns in cross-channel attention heads. Despite having been trained only on low-dimensional ordinary differential equations, Panda spontaneously develops the ability to predict partial differential equations without retraining. We demonstrate a neural scaling law for differential equations, underscoring the potential of pretrained models for probing abstract mathematical domains like nonlinear dynamics. This checkpoint was trained for (only) 100k iterations, with per-device batch size 1024, across 4 AMD MI100X GPUs. NOTE: we are currently in the process of scaling up our model and training, so stay tuned! Update: We have released a bigger model: panda-72M
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View details ↗The key difference between Z-Image CyberRealistic v9 and v8 is not merely that it is "another version," but rather a shift in prompting philosophy. V9 moves away from tag stacks toward descriptive language: it better understands natural phrasing about lighting, scene and materials, relies less on booster words like "masterpiece" or "8k", and delivers more natural-looking skin, consistent anatomy and realistic lighting. Below are six prompts that demonstrate these differences in practice. All examples were generated with the same seed and sampler settings on both v8 and v9 for a fair comparison. 💡 Tip: These PNGs have the **ComfyUI workflow and prompt embedded!** Don't just copy the image from the browser preview. Go to the Files and versions tab, open the image there, and click "Download" to get the original file with all metadata intact.
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