Comments (1)
Those are just hypothetical examples. There are some dataset that map easily to them, for instance machine translation datasets, image classification ones, omniglot can be used for the siamese examples, several sentiment analysis datasets and so on. A google search will return many more than I can list here. We explicitly tried not to tie to specific datasets, because some of them would require some minimal data monging to be made in a CSV format that is readable by Ludwig, so we tried to avoid that quagmire.
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Related Issues (20)
- Inappropriate saving of the merged fine tuned llama-2 model HOT 4
- Retrain previously fine tuned adapter HOT 4
- Remove default target modules for Gemma once it's updated in PEFT
- Re-Enable AdaptionPrompt when HuggingFace releases the PEFT fix.
- Unable to fine-tune when not using quantization HOT 2
- batch_size > 1 results in NaN loss value HOT 11
- You are calling `save_pretrained` to a 4-bit converted model, but your `bitsandbytes` version doesn't support it. HOT 2
- Temporarily disable expensive text metrics
- Dependency issue HOT 8
- Add Ludwig config json to output directory containing model weights HOT 3
- Improve docker build times for `ludwig-ray` and `ludwig-ray-gpu`
- Ray - protobuf issue HOT 5
- Support for Models stored in GCS bucket HOT 1
- GPU is not available
- Wandb on ludwigai/ludwig-ray-gpu:latest + ray throws AttributeError: module 'pydantic.fields' has no attribute 'ModelField'
- Token-level Probability Always 0.0 When Fine-tuning Llama2-7b Model on Single GPU
- Dependency issue HOT 2
- `RESPONSE` contains lot longer text than is expected based on the `output_features` and `max_sequence_length`.
- PyYAML error while installing with python 3.12
- Ray retraining fails with StopIteration exception when retraining a model with small datasets HOT 1
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