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CoTaNER-LoRA

Safetensors
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CoTaNER-LoRA
File size: 2,109 Bytes
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{
  "output_dir": "models/gliner2-agri-lora",
  "experiment_name": "gliner2",
  "num_epochs": 5,
  "max_steps": -1,
  "batch_size": 8,
  "eval_batch_size": 8,
  "gradient_accumulation_steps": 4,
  "encoder_lr": 1e-05,
  "task_lr": 0.0005,
  "weight_decay": 0.01,
  "adam_beta1": 0.9,
  "adam_beta2": 0.999,
  "adam_epsilon": 1e-08,
  "max_grad_norm": 1.0,
  "scheduler_type": "linear",
  "warmup_ratio": 0.1,
  "warmup_steps": 0,
  "num_cycles": 0.5,
  "fp16": true,
  "bf16": false,
  "eval_strategy": "epoch",
  "eval_steps": 500,
  "save_total_limit": 3,
  "save_best": true,
  "metric_for_best": "eval_loss",
  "greater_is_better": false,
  "logging_steps": 10,
  "logging_first_step": true,
  "report_to_wandb": false,
  "wandb_project": null,
  "wandb_entity": null,
  "wandb_run_name": null,
  "wandb_tags": [],
  "wandb_notes": null,
  "early_stopping": false,
  "early_stopping_patience": 3,
  "early_stopping_threshold": 0.0,
  "num_workers": 4,
  "pin_memory": true,
  "prefetch_factor": 2,
  "seed": 42,
  "deterministic": false,
  "local_rank": -1,
  "debug": false,
  "max_train_samples": -1,
  "max_eval_samples": -1,
  "validate_data": true,
  "max_len": null,
  "strict_training": true,
  "allow_invalid_samples": false,
  "log_proposal_metrics": true,
  "gold_injection_start": 1.0,
  "gold_injection_end": 0.25,
  "gold_injection_hold_frac": 0.15,
  "diagnostics_every_n_steps": 0,
  "profile_first_n_steps": 0,
  "ddp_consensus_check": true,
  "ddp_find_unused_parameters": false,
  "ddp_static_graph": true,
  "dry_run_recall_steps": 0,
  "gate_recall": 0.97,
  "gate_long_recall": 0.93,
  "on_capacity_exceeded": "raise",
  "group_by_length": true,
  "length_group_window_batches": 50,
  "compile_model": false,
  "gradient_checkpointing": true,
  "fused_optimizer": true,
  "allow_tf32": true,
  "float32_matmul_precision": "high",
  "use_lora": true,
  "lora_r": 16,
  "lora_alpha": 32.0,
  "lora_dropout": 0.05,
  "lora_use_dora": false,
  "lora_target_modules": [
    "encoder",
    "span_rep",
    "classifier",
    "count_embed",
    "count_pred"
  ],
  "save_adapter_only": true
}