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models/people_Ariel_Sharon_distil_400/README.md ADDED
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+ ---
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+ hyperparameters:
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+ lora_r: 16
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+ lora_alpha: 4
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+ is_lora_negated: false
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+ seed: 42
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+ model_name_or_path: CompVis/stable-diffusion-v1-4
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+ revision: null
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+ variant: null
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+ dataset_forget_name: assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_forget
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+ dataset_retain_name: assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_retain
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+ dataset_forget_config_name: null
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+ dataset_retain_config_name: null
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+ image_column: image
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+ caption_column: text
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+ validation_prompt: An image of Ariel Sharon
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+ num_validation_images: 1
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+ validation_epochs: 401
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+ resolution: 512
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+ center_crop: false
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+ random_flip: true
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+ max_train_samples: null
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+ dataloader_num_workers: 2
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+ prediction_type: null
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+ per_device_train_batch_size: 2
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+ gradient_accumulation_steps: 2
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+ num_train_epochs: 400
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+ learning_rate: 0.0006
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+ lr_scheduler_type: constant
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+ should_log: true
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+ local_rank: -1
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+ device: cuda
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+ n_gpu: 1
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+ output_dir: assets/models/people_Ariel_Sharon_distil_400
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+ cache_dir: null
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+ hub_token: null
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+ hub_model_id: null
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+ logging_dir: logs
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+ logging_steps: 20
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+ save_strategy: epoch
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+ save_total_limit: 2
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+ gradient_checkpointing: false
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+ enable_xformers_memory_efficient_attention: false
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+ mixed_precision: 'no'
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+ allow_tf32: false
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+ use_8bit_adam: false
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+ report_to: tensorboard
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+ compute_gradient_conflict: false
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+ compute_runtimes: true
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+ max_train_steps: 800
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+ lr_warmup_steps: 0
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+ adam_beta1: 0.9
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+ adam_beta2: 0.999
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+ adam_weight_decay: 0.01
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+ adam_epsilon: 1.0e-08
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+ max_grad_norm: 5.0
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+ checkpointing_steps: 10000
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+ checkpoints_total_limit: null
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+ resume_from_checkpoint: null
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+ noise_offset: 0.0
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+ overwritting_concept: a child
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+ model-index:
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+ - name: None
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+ results:
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+ - task:
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+ type: text-to-image
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+ dataset:
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+ name: Forget set
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+ type: inline-prompts
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+ metrics:
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+ - type: clip
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+ value: 36.202392578125
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+ name: ForgetSet clip score of original model mean (~↑)
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+ - type: clip
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+ value: 3.334292140068467
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+ name: ForgetSet clip score of original model std (~↓)
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+ value: 22.67087984085083
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+ name: ForgetSet clip score of learned model mean (~↑)
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+ - type: clip
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+ name: ForgetSet clip score of learned model std (~↓)
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+ value: 20.102911710739136
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+ name: ForgetSet clip score of unlearned model mean (↓)
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+ - type: clip
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+ value: 3.6418085406497984
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+ name: ForgetSet clip score of unlearned model std (~↓)
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+ - type: clip
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+ value: 2.5679681301116943
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+ name: ForgetSet clip score difference between learned and unlearned mean (↑)
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+ - type: clip
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+ value: 3.1880499294369598
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+ name: ForgetSet clip score difference between learned and unlearned std (~↓)
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+ - type: clip
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+ value: 16.099480867385864
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+ name: ForgetSet clip score difference between original and unlearned mean (↑)
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+ - type: clip
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+ value: 3.9269274161087013
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+ name: ForgetSet clip score difference between original and unlearned std (~↓)
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+ value: 2.218968458166184
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+ name: ForgetSet clip score difference between original and learned std (~↓)
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+ - type: clip
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+ value: 31.09067440032959
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+ name: RetainSet clip score of original model mean (~↑)
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+ - type: clip
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+ value: 2.3280465098326126
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+ name: RetainSet clip score of original model std (~↓)
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+ value: 30.465720653533936
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+ name: RetainSet clip score of learned model mean (~↓)
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+ - type: clip
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+ value: 2.102891705763798
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+ name: RetainSet clip score of learned model std (~↓)
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+ value: 29.91259527206421
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+ name: RetainSet clip score of unlearned model mean (↑)
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+ name: RetainSet clip score of unlearned model std (~↓)
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+ - type: clip
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+ name: RetainSet clip score difference between learned and unlearned std (~↓)
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+ - type: clip
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+ value: 1.1780791282653809
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+ name: RetainSet clip score difference between original and unlearned mean (↓)
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+ - type: clip
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+ value: 1.6257921979899794
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+ name: RetainSet clip score difference between original and unlearned std (~↓)
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+ - type: clip
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+ value: 0.6249537467956543
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+ name: RetainSet clip score difference between original and learned mean (↑)
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+ - type: clip
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+ value: 2.869911277015557
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+ name: RetainSet clip score difference between original and learned std (~↓)
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+ - type: runtime
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+ value: 6.403184910615285
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+ name: Inference latency seconds mean (↓)
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+ - type: runtime
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+ value: 0.2135538228422366
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+ name: Inference latency seconds std (~↓)
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+ - task:
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+ type: text-to-image
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+ dataset:
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+ name: assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_forget (forget)
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+ and assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_retain (retain)
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+ sets
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+ type: forget-and-retain-together
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+ metrics:
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+ - type: runtime
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+ value: 3.4783239364624023
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+ name: Runtime init seconds (~↓)
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+ - type: runtime
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+ value: 1.1850903034210205
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+ name: Runtime data loading seconds (~↓)
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+ - type: runtime
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+ value: 3513.3346388339996
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+ name: Runtime training seconds (↓)
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+ - type: runtime
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+ value: 170.6948161125183
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+ name: Runtime eval seconds (~↓)
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the training script had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+
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+ # LoRA text2image fine-tuning - None
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+ These are LoRA adaption weights for CompVis/stable-diffusion-v1-4.
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+ The weights were fine-tuned for forgetting assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_forget dataset, while retaining assets/datasets/lfw_splits_filtered/Ariel_Sharon/train_retain.
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+ You can find some example images in the following.
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+
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+ ![img](images/val_prompt_00_01.png)
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+ ![img](images/tst_prompt_399_01.png)
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+ ![img](images/Forget - An image of Ariel Sharon.png)
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+ ![img](images/Forget - Photograph of Ariel Sharon; high definition.png)
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+ ![img](images/Forget - An picture of Ariel Sharon in the rain.png)
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+ ![img](images/Forget - An picture of Ariel Sharon running.png)
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+ ![img](images/Retain - An image of a child.png)
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+ ![img](images/Retain - Photograph of a child; high definition.png)
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+ ![img](images/Retain - An picture of a child in the rain.png)
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+ ![img](images/Retain - An picture of a child running.png)
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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ # TODO: add an example code snippet for running this diffusion pipeline
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+ ```
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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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