BERT_Review
activebus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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Explore AI models 84838
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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View details ↗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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View details ↗ABot-Recon
acvlab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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View details ↗ABot-World-0-5B-LF
acvlab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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View details ↗adakajuan
adakajuan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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View details ↗distilbert-portuguese-cased
adalbertojunior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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View details ↗Qwen3.8-27B-GGUF
adaliboy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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View details ↗peeLoraV1
Adam3DX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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View details ↗tinybert-emotion-balanced
AdamCoddThis model is a fine-tuned version of bert-tiny on the emotion balanced dataset. It achieves the following results on the evaluation set: - Loss: 0.1809 - Accuracy: 0.9354 TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT). The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split. This model is not as accurate as the distilbert-emotion-balanced one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.
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View details ↗vit-base-nsfw-detector
AdamCoddThis model is a fine-tuned version of vit-base-patch16-384 on around 25_000 images (drawings, photos...). It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9654 ** New [07/30] **: I created a new ViT model specifically to detect NSFW/SFW images for stable diffusion usage (read the disclaimer below for the reason): **AdamCodd/vit-nsfw-stable-diffusion**. **Disclaimer**: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea: - Loss: 0.3682 (↑ 292.95%) - Accuracy: 0.8600 (↓ 10.91%) - F1: 0.8654 - AUC: 0.9376 (↓ 5.75%) - Precision: 0.8350 - Recall: 0.8980
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View details ↗beans-deit-tiny
Adamix3003This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the AI-Lab-Makerere/beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0988 - Accuracy: 0.9699 - F1 Macro: 0.9699 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP
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View details ↗beans-deit-tiny
Adamix303This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the AI-Lab-Makerere/beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0619 - Accuracy: 0.9850 - F1 Macro: 0.9850 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP
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View details ↗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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View details ↗8LayerChessGPT2
adamkarvonen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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View details ↗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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View details ↗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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View details ↗Qwen3-8B-taboo-gold_50_mix
adamkarvonen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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View details ↗Qwen3-8B-taboo-green_50_mix
adamkarvonen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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View details ↗Qwen3-8B-taboo-moon_50_mix
adamkarvonen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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View details ↗Qwen3-8B-taboo-ship_50_mix
adamkarvonen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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View details ↗Qwen3-8B-taboo-smile_50_mix
adamkarvonen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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View details ↗CarbonBeagle-11B-truthy-GGUF
adamksn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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View details ↗Mistral-7B-Sunda-v1.0-GGUF
adamksn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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