Comments (1)
Figured it out, here's how!
data = {
"inputs": s,
"parameters": {
"candidate_labels": [
"hostess",
"waiter"
],
"multi_label": True
}
}
from notebooks.
Related Issues (20)
- Links to Colab notebooks are broken. HOT 1
- [text_classification.ipynb] Model initialized twice during hyperparameter search
- training, deploying sentence transformer model ( setfit method) in sagemaker pipeline HOT 2
- SAM iou_score is greater than 1?
- Incomplete Output from IDEFICS Inference Code HOT 1
- AttributeError: 'NoneType' object has no attribute 'get' while training HOT 2
- Pandas is required for the TAPAS tokenizer
- Separate model and inference script tar.gz not working.
- instruction finetuning IDEFICS
- Fine_tune_BLIP2_on_an_image_captioning_dataset_PEFT.ipynb loss is all NaN HOT 25
- Training SegFormer model not working (goes through notebook, but model loss becomes nan) on dataset I created (stuck for a week or so) HOT 5
- Informer/Transformer: multivariate forecasting with a panel of time-series
- Broken link in sagemaker notebook HOT 2
- SDXL_Dreambooth_LoRA_advanced_example.ipynb Training Issue
- FSDP training not loading saving the best checkpoint
- PatchTSTMixer fix HOT 4
- (TF)DistilBertForTokenClassification requires the 🤗 Datasets library
- Broken link in Load LoRAs for inference HOT 1
- RuntimeError expected scalar type Long but found Int in audio_classification.ipynb
- Incorrect variable name `tokenized_dataset` in Chapter 7 section 6 of course HOT 1
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from notebooks.