Comments (4)
Hello,
I don't have experience with them. Can you provide more information:
- What FFCV Field are you using ?
- What is the data type a tokenizer expects as an input ?
- What is the output data type ?
- Do they work by batch or by sample ?
- Are they implemented in python or in lower level language and accessed through
cffi
or python modules ?
from ffcv.
I'm storing the textual metadata in a JSON field. Here is a quick tour of how they work: https://huggingface.co/docs/transformers/preprocessing. They expect strings as input and output dictionaries of int arrays as PyTorch/TensorFlow tensors or a list of int literals. They work on both batched (as a list) or single strings. They come in two varieties: a full python version and a faster version which wraps an underlying Rust implementation. They run on CPU and I estimate that the Python version of the BERT tokenizer processes a sentence about as fast as torchvision takes to process an image with standard ResNet-style augmentations.
from ffcv.
Do you need all the three elements of the dict that the tokenizer returns ?
from ffcv.
Not always but often- the token types ids are more useful for specific NLP tasks. For my use case (replacing this dataset for CLIP training) we need the attention mask in addition to the input ids because not all captions span the full length.
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Related Issues (20)
- Speedup on Image to Image problems? HOT 1
- Indexing HOT 1
- Changing Indices during training leads to much slower training HOT 2
- Memory Leak in Ffcv Loader? HOT 4
- Import error - libopencv_impgproc missing HOT 2
- Installation issues HOT 6
- Small bug (improvement suggestion) in the quickstart doc HOT 1
- stuck in the loader when using only cpu HOT 4
- Grayscale Image Datasets HOT 2
- Top-1 accuracy on ImageNet drops between runs -- only difference is FFCV HOT 2
- Large .beton files slow down or even freeze learning during loading [possible bug] HOT 2
- [General question] FFCV scope
- ModuleNotFoundError: No module named 'ffcv.compiler
- doubt about mutli-gpu train when use imagenet 4 gpus HOT 1
- Default num_workers is incompatible with SLURM
- Installing FFCV on CPU-only node HOT 1
- Exact performance improvement
- Unable to save anything in the Fields HOT 1
- Compression error causes performance drop
- Reuse memory?
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from ffcv.