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Explore AI models 2428
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 (aka BERT-Tiny) on an SetFit/enron_spam for `Spam Dectection` downstream task. It achieves the following results on the evaluation set: - Loss: 0.0593 - Precision: 0.9851 - Recall: 0.9871 - Accuracy: 0.986 - F1: 0.9861 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4
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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 ↗This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set: In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates.
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View details ↗This model is a fine-tuned version of distilroberta-base on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.1116 - Accuracy: **0.98**23 This model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. This model is case-sensitive: it makes a difference between English and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
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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 ↗mobilebert-finetuned-pos
mrm8488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 ↗asr-question-detection
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View details ↗roberta-spam
mshenoda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 ↗en-base
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View details ↗punctuate-16
mukowaty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 ↗policylm-1.7b
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View details ↗This is seara/rubert-tiny2-russian-sentiment model fine-tuned for __sentiment classification__ of short __Russian__ financial posts from Telegram channels.
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View details ↗toutiao
myml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-Reranker-0.6B-ONNX
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View details ↗Qwen3.5-4B-Hmm
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View details ↗nobody-pii-de
naeyn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 ↗sumi-ja-pii
NagaYu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 ↗emotion-analysis
nahiar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 ↗Ara-Prompt-Guard_V0
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View details ↗ViRanker
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View details ↗counterbench-bert-base-uncased
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View details ↗vega-08b-public-intents
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