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from dataclasses import dataclass, field |
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from typing import Optional |
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from result_parser import yes_or_no, find_option_number, anomaly_detection, trajectory_prediction, trajectory_classification |
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result_parsers = { |
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"poi_category_recognition": find_option_number, |
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"poi_identification": yes_or_no, |
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"urban_region_function_recognition": find_option_number, |
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"administrative_region_determination": find_option_number, |
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"point_trajectory": find_option_number, |
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"point_region": find_option_number, |
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"trajectory_region": find_option_number, |
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"trajectory_identification": yes_or_no, |
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"trajectory_trajectory": find_option_number, |
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"direction_determination": find_option_number, |
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"trajectory_anomaly_detection": anomaly_detection, |
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"trajectory_classification": trajectory_classification, |
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"trajectory_prediction": trajectory_prediction |
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} |
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max_tokens = { |
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"poi_category_recognition": 15, |
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"poi_identification": 15, |
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"urban_region_function_recognition": 15, |
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"administrative_region_determination": 15, |
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"point_trajectory": 15, |
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"point_region": 15, |
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"trajectory_region": 15, |
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"trajectory_identification": 15, |
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"trajectory_trajectory": 15, |
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"direction_determination": 15, |
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"trajectory_anomaly_detection": 15, |
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"trajectory_classification": 15, |
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"trajectory_prediction": 50 |
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} |
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dataset_files = { |
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"poi_category_recognition": ["../datasets/basic/knowledge_comprehension/poi_category_recognition.jsonl"], |
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"poi_identification": ["../datasets/basic/knowledge_comprehension/poi_identification.jsonl"], |
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"urban_region_function_recognition": ["../datasets/basic/knowledge_comprehension/urban_region_function_recognition.jsonl"], |
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"administrative_region_determination": ["../datasets/basic/knowledge_comprehension/administrative_region_determination.jsonl"], |
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"point_trajectory": ["../datasets/basic/spatiotemporal_reasoning/point_trajectory.jsonl"], |
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"point_region": ["../datasets/basic/spatiotemporal_reasoning/point_region_2regions.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/point_region_3regions.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/point_region_4regions.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/point_region_5regions.jsonl"], |
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"trajectory_region": ["../datasets/basic/spatiotemporal_reasoning/trajectory_region_length2.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_region_length4.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_region_length6.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_region_length8.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_region_length10.jsonl"], |
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"trajectory_identification": ["../datasets/basic/spatiotemporal_reasoning/trajectory_identification_downsampling.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_identification_staggered_sampling.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_identification_spatial_offset.jsonl", |
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"../datasets/basic/spatiotemporal_reasoning/trajectory_identification_temporal_offset.jsonl"], |
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"trajectory_trajectory": ["../datasets/basic/accurate_calculation/trajectory_trajectory.jsonl"], |
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"direction_determination": ["../datasets/basic/accurate_calculation/direction_determination.jsonl"], |
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"trajectory_anomaly_detection": ["../datasets/basic/downstream_applications/trajectory_anomaly_detection_abnormal.jsonl", |
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"../datasets/basic/downstream_applications/trajectory_anomaly_detection_normal.jsonl"], |
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"trajectory_classification": ["../datasets/basic/downstream_applications/trajectory_classification.jsonl"], |
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"trajectory_prediction": ["../datasets/basic/downstream_applications/trajectory_prediction.jsonl"] |
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} |
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icl_files = { |
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"poi_identification": "../datasets/icl/poi_identification.jsonl", |
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"trajectory_region": "../datasets/icl/trajectory_region.jsonl", |
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"trajectory_trajectory": "../datasets/icl/trajectory_trajectory.jsonl", |
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"direction_determination": "../datasets/icl/direction_determination.jsonl", |
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"trajectory_anomaly_detection": "../datasets/icl/trajectory_anomaly_detection.jsonl", |
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"trajectory_prediction": "../datasets/icl/trajectory_prediction.jsonl" |
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} |
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cot_files = { |
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"urban_region_function_recognition": "../datasets/cot/urban_region_function_recognition.jsonl", |
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"trajectory_region": "../datasets/cot/trajectory_region.jsonl", |
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"trajectory_trajectory": "../datasets/cot/trajectory_trajectory.jsonl", |
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"trajectory_classification": "../datasets/cot/trajectory_classification.jsonl" |
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} |
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sft_files = { |
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"administrative_region_determination": { |
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"train": "../datasets/sft/administrative_region_determination_train.jsonl", |
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"valid": "../datasets/sft/administrative_region_determination_valid.jsonl" |
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}, |
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"direction_determination": { |
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"train": "../datasets/sft/direction_determination_train.jsonl", |
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"valid": "../datasets/sft/direction_determination_valid.jsonl" |
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}, |
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"trajectory_anomaly_detection":{ |
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"train": "../datasets/sft/trajectory_anomaly_detection_train.jsonl", |
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"valid": "../datasets/sft/trajectory_anomaly_detection_valid.jsonl" |
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}, |
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"trajectory_prediction": { |
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"train": "../datasets/sft/trajectory_prediction_train.jsonl", |
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"valid": "../datasets/sft/trajectory_prediction_valid.jsonl" |
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}, |
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"trajectory_region": { |
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"train": "../datasets/sft/trajectory_region_train.jsonl", |
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"valid": "../datasets/sft/trajectory_region_valid.jsonl" |
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}, |
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"trajectory_trajectory": { |
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"train": "../datasets/sft/trajectory_trajectory_train.jsonl", |
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"valid": "../datasets/sft/trajectory_trajectory_valid.jsonl" |
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} |
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} |
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@dataclass |
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class ScriptArguments: |
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""" |
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These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train. |
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""" |
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per_device_train_batch_size: Optional[int] = field(default=4) |
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per_device_eval_batch_size: Optional[int] = field(default=1) |
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gradient_accumulation_steps: Optional[int] = field(default=4) |
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learning_rate: Optional[float] = field(default=2e-4) |
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max_grad_norm: Optional[float] = field(default=0.3) |
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weight_decay: Optional[int] = field(default=0.001) |
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lora_alpha: Optional[int] = field(default=16) |
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lora_dropout: Optional[float] = field(default=0.1) |
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lora_r: Optional[int] = field(default=8) |
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max_seq_length: Optional[int] = field(default=2048) |
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model_name: Optional[str] = field( |
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default=None, |
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metadata={ |
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"help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc." |
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} |
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) |
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dataset_name: Optional[str] = field( |
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default="stingning/ultrachat", |
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metadata={"help": "The preference dataset to use."}, |
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) |
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fp16: Optional[bool] = field( |
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default=False, |
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metadata={"help": "Enables fp16 training."}, |
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) |
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bf16: Optional[bool] = field( |
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default=False, |
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metadata={"help": "Enables bf16 training."}, |
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) |
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packing: Optional[bool] = field( |
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default=True, |
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metadata={"help": "Use packing dataset creating."}, |
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) |
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gradient_checkpointing: Optional[bool] = field( |
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default=True, |
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metadata={"help": "Enables gradient checkpointing."}, |
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) |
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use_flash_attention_2: Optional[bool] = field( |
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default=False, |
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metadata={"help": "Enables Flash Attention 2."}, |
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) |
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optim: Optional[str] = field( |
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default="paged_adamw_32bit", |
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metadata={"help": "The optimizer to use."}, |
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) |
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lr_scheduler_type: str = field( |
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default="constant", |
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metadata={"help": "Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis"}, |
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) |
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max_steps: int = field(default=1000, metadata={"help": "How many optimizer update steps to take"}) |
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warmup_ratio: float = field(default=0.03, metadata={"help": "Fraction of steps to do a warmup for"}) |
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save_steps: int = field(default=100, metadata={"help": "Save checkpoint every X updates steps."}) |
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logging_steps: int = field(default=10, metadata={"help": "Log every X updates steps."}) |
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output_dir: str = field( |
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default="./results", |
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metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, |
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) |
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