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Vision_or_not

Text Classification

Inference Endpoints

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Johnson8187 commited on Dec 18, 2024

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@@ -4,7 +4,7 @@ language:
 - zh
 - en
 base_model:
-- MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
 pipeline_tag: text-classification
 library_name: transformers
 ---
@@ -24,7 +24,7 @@ The model is fine-tuned for sequence classification tasks and provides a straigh
 # Fine-Tuning Information
 This model is fine-tuned based on the mDeBERTa-v3-base-mnli-xn model, which is a multilingual version of DeBERTa (Decoding-enhanced BERT with disentangled attention). The fine-tuning data used is primarily in Traditional Chinese, which makes the model well-suited for processing texts in this language. However, the model has been tested and can also perform well with English inputs.
-Base Model: [mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
 Fine-Tuning Data: Traditional Chinese text data
 # Quick Start

 - zh
 - en
 base_model:
+- joeddav/xlm-roberta-large-xnli
 pipeline_tag: text-classification
 library_name: transformers
 ---
 # Fine-Tuning Information
 This model is fine-tuned based on the mDeBERTa-v3-base-mnli-xn model, which is a multilingual version of DeBERTa (Decoding-enhanced BERT with disentangled attention). The fine-tuning data used is primarily in Traditional Chinese, which makes the model well-suited for processing texts in this language. However, the model has been tested and can also perform well with English inputs.
+Base Model: [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli)
 Fine-Tuning Data: Traditional Chinese text data
 # Quick Start