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AI 模型 84759
- grc_odycy_joint_sm↗
chcaa
- bert-base-uncased-finetuned-advanced-srl_arg↗
dannashao
This model is a fine-tuned version of bert-base-uncased on the English Universal Propbank dataset for the Semantics Role Labeling (SRL) task. It achieves the following results on the evaluation set: - Loss: 0.0914 - Precision: 0.8664 - Recall: 0.8673 - F1: 0.8669 - Accuracy: 0.9812 This more advanced SRL model uses similar apporach as the Augment method described in NegBERT (Khandelwal, et al. 2020). That is, adding a special token ([V]) immediately before the predicate: > This [V] is a sentence. Note that **the special token and the predicate is considered a whole**. That is, the actual sentence is like > 'This' **'[V] is'** 'a' 'sentence' '.'
- lenovo_qwen2.1↗
Danrisi
> l3n0v0, amateur photo, low-lit, shot on cheap mobile phone, evening, high iso, shadowed foreground, dark. A 20-year-old Eastern European-looking woman casually dressed in an oversized pink T-shirt with text "sample text sample text sample text", outdoors, her gaze directed to the side, serious expression, hold cup of coffee, her blonde hair loosely tied back and she has fringes that frames her face. minimal makeup visible. she is standing on the street near soviet-era storey buildings, trees, parked cars near. Dull, overcast lighting > l3n0v0. George Bush portrait, standing with his gaze directed to the Twin Towers (WTC before 9/11) at dusk, his face is shadowed, dark silhouettes of skyscrappers against deep blue sky, distant yellow lights scattered in background, twilight atmosphere, dark shadowy cityscape, underexposed nighttime scene, minimal ambient lighting, natural evening lighting conditions, dark foreground elements
- Italian_NER_XXL_v2↗
DeepMount00
**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ Welcome to the second generation of our state-of-the-art Named Entity Recognition model for Italian text. Building on the success of our previous version, Italian_NER_XXL_v2 delivers significantly enhanced performance with an **accuracy of 87.5%** and **F1 score of 89.2%** - an improvement of over 8 percentage points from my previous model. Italian_NER_XXL_v2 remains the only model in Italy capable of identifying a comprehensive range of **52** different entity categories, maintaining our unique position in the Italian NLP landscape. This unparalleled breadth of entity recognition makes our model the premier choice for privacy, legal, and financial applications.
- isl-latn-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed455↗
francesca9805
- magi-1.0↗
GEHIRNS
- mochi-1-preview↗
genmo
Mochi 1 preview is an open state-of-the-art video generation model with high-fidelity motion and strong prompt adherence in preliminary evaluation. This model dramatically closes the gap between closed and open video generation systems. We’re releasing the model under a permissive Apache 2.0 license. Try this model for free on our playground. This repository comes with a simple, composable API, so you can programmatically call the model. This API gives the highest quality results. You can find a full example here. But, roughly, it looks like this: The following example will use the `bfloat16` variant of the model and requires 22GB VRAM to run. There is a slight drop in the quality of the generated video as a result.
- bert-base-NER-Russian↗
Gherman
- qwen2.5-omni-7b-mlx-4bit↗
giangndm
- multiberts-seed_1-step_1400k↗
Google