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Text Classification

Multilingual-MiniLM-L12-H384

MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository. Please note: This checkpoint uses BertModel with…
March 6, 20240Likes0Comments
Depth Estimation

glpn-nyu-finetuned

glpn-nyu-finetuned This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset. It achieves the following results on the evaluation set: Loss: 1.5286 Mae: 3.1196 Rmse: 3.5796 Abs Rel: 5.9353 Log Mae: 0.6899 Log Rmse: 0.8145 Delta1: 0.3012 Delta2: 0.3076 Delta3: 0.3093 Model description More information needed Intended uses & limitations …
March 6, 20240Likes0Comments
Image Classification

davit_tiny.msft_in1k

Edit model card Model card for davit_tiny.msft_in1k Model Details Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison By Top-1 Citation Model card for davit_tiny.msft_in1k A DaViT image classification model. Trained on ImageNet-1k by paper authors. Thanks to Fredo Guan for bringing the classification backbone to timm. Model Details …
March 6, 20240Likes0Comments
Reinforcement Learning

ppo-QbertNoFrameskip-v4

PPO Agent playing QbertNoFrameskip-v4 This is a trained model of a PPO agent playing QbertNoFrameskip-v4 using the stable-baselines3 library. The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-Benchmark--VmlldzoxNjI3NTIy Evaluation Results Mean_reward: 15685.00 +/- 115.217 Usage (with Stable-baselines3) You need to use gym==0.19 since it includes Atari Roms. The Action Space is 6 since we use only possible actions in this game.…
March 6, 20240Likes0Comments