全站搜索
AI 模型 84759
- pix2text-mfr↗
breezedeus
This MFR model utilizes the TrOCR architecture developed by Microsoft, starting with its initial values and retrained using a dataset of mathematical formula images. The resulting MFR model can be used to convert images of mathematical formulas into LaTeX text representation. More detailed can be found: Pix2Text V1.0 New Release: The Best Open-Source Formula Recognition Model | Breezedeus.com. 此 MFR 模型使用了微软的 TrOCR 架构，以其为初始值并利用数学公式图片数据集进行了重新训练。 获得的 MFR 模型可用于把数学公式图片转换为 LaTeX 文本表示。更多细节请见：Pix2Text V1.0 新版发布：最好的开源公式识别模型 | Breezedeus.com。 This method requires the installation of pix2text, utilizing the Mathematical Formula Detection model (MFD) within Pix2Text. It is capable of recognizing not only pure formula images but also mixed images containing text.
- GPT-5-Distill-Qwen3-4B-Instruct-Heretic-GGUF↗
ChiKoi7
- distilcamembert-base↗
cmarkea
We present a distillation version of the well named CamemBERT, a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of concept is shown in the DistilBERT paper and the code used for the training is inspired by the code of DistilBERT. The training for the distilled model (student model) is designed to be the closest as possible to the original model (teacher model). To perform this the loss function is composed of 3 parts: * DistilLoss: a distillation loss which measures the silimarity between the probabilities at the outputs of the student and teacher models with a cross-entropy loss on the MLM task ; * CosineLoss: a cosine embedding loss. This loss function is applied on the last hidden layers of student and teacher models to guarantee a collinearity between them ; * MLMLoss: and finaly a Masked Language Modeling (MLM) task loss to perform the student model with the original task of the teacher model. The final loss function is a combination of these three losses functions. We use the following ponderation:
- rerank-v3.5↗
Cohere
- scribe-fasttext-lid176↗
crash-sv
- AingDiffusion9.2↗
digiplay
- semsimula-ladder-live-ts-d384-l2-none-norc-pm2-pmx16-sr2-fob↗
dimitarpg13
- blt-7b↗
facebook
- convnext-small-224↗
facebook
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
- mms-tts-swh↗
facebook