Yangheng
Rank:Average Model Cost: $0.0000
Number of Runs: 20,752
Models by this creator
deberta-v3-base-absa-v1.1
$-/run
20.1K
Huggingface
deberta-v3-large-absa-v1.1
deberta-v3-large-absa-v1.1
Note This model is training with 30k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!) DeBERTa for aspect-based sentiment analysis The deberta-v3-large-absa model for aspect-based sentiment analysis, trained with English datasets from ABSADatasets. Training Model This model is trained based on the FAST-LCF-BERT model with microsoft/deberta-v3-large, which comes from PyABSA. To track state-of-the-art models, please see PyASBA. Usage Example in PyASBA An example for using FAST-LCF-BERT in PyASBA datasets. Datasets This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files: If you use this model in your research, please cite our paper:
$-/run
649
Huggingface
deberta-v3-large-absa
deberta-v3-large-absa
Note This model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!) DeBERTa for aspect-based sentiment analysis The deberta-v3-large-absa model for aspect-based sentiment analysis, trained with English datasets from ABSADatasets. Training Model This model is trained based on the FAST-LSA-T model with microsoft/deberta-v3-large, which comes from PyABSA. To track state-of-the-art models, please see PyASBA. Usage Example in PyASBA An example for using FAST-LSA-T in PyASBA Datasets This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files: If you use this model in your research, please cite our paper:
$-/run
31
Huggingface
deberta-v3-base-absa
deberta-v3-base-absa
Note This model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!) DeBERTa for aspect-based sentiment analysis The deberta-v3-base-absa model for aspect-based sentiment analysis, trained with English datasets from ABSADatasets. Training Model This model is trained based on the FAST-LCF-BERT model with microsoft/deberta-v3-base, which comes from PyABSA. To track state-of-the-art models, please see PyASBA. Usage Example in PyASBA An example for using FAST-LCF-BERT in PyASBA datasets. Datasets This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files: If you use this model in your research, please cite our paper:
$-/run
18
Huggingface
pyabsa_checkpoints
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0
Huggingface