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View details ↗This model is a warm-started *BERT2BERT* (mini) model fine-tuned on the *CNN/Dailymail* summarization dataset.
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View details ↗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 ↗bert-tiny-finetuned-squadv2
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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 ↗mobilebert-finetuned-pos
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View details ↗phi-4-14B-grpo-gsm8k-3e-q4
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View details ↗spanish-gpt2
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View details ↗t5-base-finetuned-common_gen
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View details ↗t5-base-finetuned-emotion
mrm8488Google's T5 base fine-tuned on emotion recognition dataset for **Emotion Recognition** downstream task. The **T5** model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract: Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
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