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swtx/Erlangshen-Roberta-110M-Similarity
Erlangshen-Roberta-110M-Similarity is a text classification model from swtx. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
We collect 20 paraphrace datasets in the Chinese domain for finetune, with a total of 2773880 samples. Our model is mainly based on roberta
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From the Hugging Face model README
We collect 20 paraphrace datasets in the Chinese domain for finetune, with a total of 2773880 samples. Our model is mainly based on roberta
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
texta='今天的饭不好吃'
textb='今天心情不好'
output=model(torch.tensor([tokenizer.encode(texta,textb)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))
| Model | BQ | BUSTM | AFQMC |
|---|---|---|---|
| Erlangshen-Roberta-110M-Similarity | 85.41 | 95.18 | 81.72 |
| Erlangshen-Roberta-330M-Similarity | 86.21 | 99.29 | 93.89 |
| Erlangshen-MegatronBert-1.3B-Similarity | 86.31 | - | - |
If you find the resource is useful, please cite the following website in your paper.
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}