Transformers
PyTorch
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bridgetower
Inference Endpoints
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@@ -52,7 +52,7 @@ for text in texts:
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  scores[text] = outputs.logits[0,1].item()
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  ```
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- Here is how to use this model to perfom masked language modeling:
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  ```python
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  from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
@@ -104,18 +104,14 @@ The model was pre-trained for 100k steps on 8 NVIDIA A100 GPUs with a batch size
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  The optimizer used was AdamW with a learning rate of 1e-5. No data augmentation was used except for center-crop. The image resolution in pre-training is set to 288 x 288.
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  ## Evaluation results
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- Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other down stream tasks.
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  ### BibTeX entry and citation info
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  ```bibtex
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  @article{xu2022bridge,
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- title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning},
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- author={Xu, Xiao and
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- Wu, Chenfei and
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- Rosenman, Shachar and
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- Lal, Vasudev and
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- Duan, Nan},
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- journal={arXiv preprint arXiv:2206.08657},
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- year={2022}
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  }
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  ```
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  scores[text] = outputs.logits[0,1].item()
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  ```
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+ Here is how to use this model to perform masked language modeling:
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  ```python
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  from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
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  The optimizer used was AdamW with a learning rate of 1e-5. No data augmentation was used except for center-crop. The image resolution in pre-training is set to 288 x 288.
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  ## Evaluation results
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+ Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other downstream tasks.
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  ### BibTeX entry and citation info
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  ```bibtex
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  @article{xu2022bridge,
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+ title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning},
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+ author={Xu, Xiao and Wu, Chenfei and Rosenman, Shachar and Lal, Vasudev and Che, Wanxiang and Duan, Nan},
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+ journal={arXiv preprint arXiv:2206.08657},
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+ year={2022}
 
 
 
 
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  }
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  ```