Comments (1)
j-min
commented on August 22, 2024
Hi, thanks a lot for your interest!
In my experiments, most of the target text was pretty short (< 20 tokens), so I don't have experience using VL-T5 to generate such long text. Theoretically, the model would learn the distribution in the target data, but LM can often degenerate for various reasons (e.g., trained on small data).
For your use case, how about controlling min_length parameters in generate() method?
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Related Issues (20)
- forward function has encounter unexpected "head_mask" HOT 4
- Two gpu run gpa.py , encouner "[E ProcessGroupNCCL. cpp:341] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, "
- Could you please consider adding a license? HOT 1
- WHY scripts GQA task batchsize of VL-T5 and VL-BART is not consistent of the paper? HOT 1
- Another download link? I can't download vcr task "train_boxes36.h5"(22.4G) HOT 1
- Considering adding refcoco inference example.
- a bug of VLT5TokenizerFast HOT 1
- Error when finetuning RefCOCO with BART HOT 1
- Some weights of VLT5VQA were not initialized from the model checkpoint at t5-base and are newly initialized HOT 1
- About the CPU memory leak HOT 7
- [Errno 32] Broken pipe HOT 1
- The model only pre-trained on the VG
- code wrong HOT 4
- Extremely low zero-shot performance (0% acc on both val and test) on RefCOCOg HOT 1
- Can this model support Multilingual? HOT 1
- what's the difference between the 'evaluate' method and 'evaluate_raw' method?
- how to solve this error HOT 6
- transformers version-up
- VL-T5 decoder all layers output how to get them ? size(batch_size, n_layers, sequence_length, hidden_dim)?
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