Upload 15_125_negs_23's state dict

.gitattributes

@@ -46,3 +46,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 15_150_negs_22/logs/15_150_negs_22_log_plot.jpg filter=lfs diff=lfs merge=lfs -text
 10_lr_attn_mask_pool_instead_cls_11/logs/10_lr_attn_mask_pool_instead_cls_11_log_plot.jpg filter=lfs diff=lfs merge=lfs -text
 15_75_negs_21/logs/15_75_negs_21_log_plot.jpg filter=lfs diff=lfs merge=lfs -text
+15_125_negs_23/logs/15_125_negs_23_log_plot.jpg filter=lfs diff=lfs merge=lfs -text

15_125_negs_23/15_125_negs_23.py

@@ -0,0 +1,1583 @@
+# %% [code]
+get_ipython().system('pip install evaluate seqeval underthesea positional-encodings[pytorch]')
+
+# %% [code]
+import warnings
+warnings.filterwarnings('ignore')
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, TensorDataset, DataLoader
+import torch.nn.functional as F
+import albumentations as albu
+from transformers import AutoTokenizer, AutoModel
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from positional_encodings.torch_encodings import PositionalEncoding1D
+
+from sklearn.metrics import f1_score
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from scipy.spatial.transform import Rotation as R
+from sklearn.model_selection import KFold, StratifiedGroupKFold, GroupKFold, StratifiedKFold
+from sklearn.metrics import precision_recall_fscore_support
+from timm.utils import ModelEmaV3
+import timm
+
+import os
+import gc
+import json
+from pathlib import Path
+import pickle
+from tqdm.auto import tqdm
+import copy
+import numpy as np
+import pandas as pd
+import polars as pl
+from PIL import Image
+import time
+from tqdm import tqdm
+from matplotlib import pyplot as plt
+import seaborn as sns
+from multiprocessing import Manager as MemoryManager
+from functools import lru_cache
+import shutil
+import glob
+import cv2
+import random
+import re
+import joblib
+import math
+from huggingface_hub import HfApi, snapshot_download
+import evaluate
+from underthesea import word_tokenize as vi_tokenize_tool
+import spacy
+en_tokenize_tool = spacy.load("en_core_web_sm")
+from collections import defaultdict, Counter
+
+# %% [code]
+file_path = "/kaggle/input/notebooks/sambui22022517/kltn-lr-bm25/bm25_scores.npy"
+
+bm25_scores = np.load(file_path, allow_pickle=True)
+print(bm25_scores.shape)
+
+# %% [code]
+# Global config
+SEEDS = [26092004]
+topk = 1
+nfolds = 5
+only_fold_idx = None
+test_only = 0
+debug_only = 0
+
+# Config thư mục
+dataset = 'kltn/raw' # vhe, bkee, casie, kltn/only_issues, kltn/only_actions, kltn/raw
+root_dir = f'/kaggle/input/notebooks/sambui22022517/kltn-data/{dataset}' ## Thư mục chứa file train, val, test
+train_dir = f'{root_dir}'
+# val_dir = f'{root_dir}/val'
+test_dir = f'{root_dir}'
+
+# Config checkpoints
+
+# Config training
+epochs = 18 if not debug_only else 2
+batch_size = 32
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# # Thêm biến toàn cục nào đó vào đây
+repo_name = 'SS3M/kltn-lr-experiments'
+state_dict_save_name = "15_125_negs_23"
+checkpoints_dir = state_dict_save_name
+pretrained_dir = "/kaggle/working"
+os.makedirs(f'{checkpoints_dir}', exist_ok=True)
+
+backbone_model_name = "bert-base-uncased" if dataset == "casie" else "vinai/phobert-base"
+word_tokenize = lambda text: [token.text for token in en_tokenize_tool(text)] if dataset == "casie" else vi_tokenize_tool(text)
+max_len_dict = {
+    'kltn/raw': 256, 
+    'kltn/only_issues': 52, 
+    'kltn/only_actions': 69, 
+    'vhe': 51, 
+    'bkee': 62, 
+    'casie': 40, 
+}
+zero_events_rate_dict = {
+    'kltn/raw': 1000, 
+    'kltn/only_issues': 0, 
+    'kltn/only_actions': 0.2, 
+    'vhe': 1000, # mean keep all zero-events samples
+    'bkee': 1000, 
+    'casie': 1000, 
+}
+
+max_len = max_len_dict[dataset]
+max_n_parts = 2
+max_span_len = 14
+n_negs = 5 * 25
+zero_events_rate = zero_events_rate_dict[dataset]
+
+# Trainer
+trainer_params = {
+    "training_time": "00:11:30:00",
+    "eval_mode": "max",
+    "topk": topk,
+    "save_name": state_dict_save_name,
+    "save_best": True,
+    "save_last": True,
+    "device": device, 
+    "logging": True, 
+    "logging_file": True, 
+    "checkpoints_dir": checkpoints_dir, 
+    "early_stopping": 30, 
+    "eval_from_ratio": 0.4, 
+    "eval_every": 1, 
+    "schedule_in_step": False, 
+    "use_ema": True, 
+    "ema_from_ratio": 0.3, 
+    "ema_decay": 0.9995, 
+    "max_grad_norm": 200.0, 
+    "return_best": True, 
+    "return_last": True, 
+}
+
+# Memory
+train_memory_params = {
+    'max_len': max_len, 
+    'max_n_parts': max_n_parts, 
+    'n_negs': n_negs,
+}
+val_memory_params = {
+    'max_len': max_len, 
+    'max_n_parts': max_n_parts, 
+    'n_negs': n_negs,
+}
+corpus_memory_params = {
+    'max_len': max_len, 
+    'max_n_parts': max_n_parts, 
+}
+
+# Data Loader
+def seed_worker(worker_id):
+    worker_seed = torch.initial_seed() % 2**32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+    
+train_loader_params = {
+    'batch_size': batch_size, 
+    'shuffle': True,
+    'pin_memory':True, 
+    'num_workers': 2, 
+    'drop_last': False, 
+    'worker_init_fn': seed_worker, 
+    'persistent_workers': False, 
+}
+val_loader_params = {
+    'batch_size': batch_size, 
+    'shuffle': False,
+    'pin_memory':True, 
+    'num_workers': 1, 
+    'drop_last': False, 
+    'worker_init_fn': seed_worker, 
+    'persistent_workers': False, 
+}
+
+# Model
+model_params = {
+    'backbone_name': backbone_model_name, 
+    'projection_dim': 256, 
+    'normalize': True, 
+}
+
+# Loss Func
+loss_func_params = {
+    'lambda_contrastive': 1.0,
+    'lambda_triplet': 0.5,
+}
+eval_func_params = {}
+
+# Optim
+optim_params = {
+    'name': 'AdamW', 
+    'lr': 1e-4,
+    'weight_decay': 1e-4,
+}
+scheduler_params = {
+    'name': 'CosineAnnealingLR',
+    'T_max': 20,  # Số epoch để hoàn thành một chu kỳ giảm LR
+    'eta_min': 1e-6  # Learning rate nhỏ nhất trong chu kỳ
+}
+
+# %% [code]
+def set_seed(seed=42):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # if using multi-GPU
+    torch.use_deterministic_algorithms(False)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ['PYTHONHASHSEED'] = str(seed)
+
+# %% [code]
+class CustomLoss(nn.Module):
+    def __init__(
+        self,
+        temperature=0.05,
+        margin=0.2,
+        lambda_contrastive=1.0,
+        lambda_triplet=0.5,
+    ):
+        super().__init__()
+
+        self.temperature = temperature
+        self.margin = margin
+
+        self.lambda_contrastive = lambda_contrastive
+        self.lambda_triplet = lambda_triplet
+
+    def forward(
+        self, 
+        encoded_text, 
+        encoded_pos, 
+        encoded_neg, 
+        pos_mask
+    ):
+        loss_contrastive = self.multi_pos_contrastive_loss(encoded_text, encoded_pos, encoded_neg, pos_mask)
+        loss_triplet = self.hardest_triplet_loss(encoded_text, encoded_pos, encoded_neg, pos_mask)
+
+        total_loss = (
+            self.lambda_contrastive * loss_contrastive + 
+            self.lambda_triplet * loss_triplet
+        )
+
+        return {
+            "total": total_loss,
+            "contrastive_loss": loss_contrastive,
+            "triplet_loss": loss_triplet,
+        }
+
+    def multi_pos_contrastive_loss(self, q, pos, neg, pos_mask):
+        B, P, D = pos.shape
+        N = neg.shape[1]
+
+        # ===== concat docs =====
+        docs = torch.cat([pos, neg], dim=1)  # [B, P+N, D]
+
+        # ===== similarity =====
+        logits = torch.matmul(q.unsqueeze(1), docs.transpose(1, 2)).squeeze(1)
+        logits = logits / self.temperature  # [B, P+N]
+
+        # ===== labels =====
+        labels = torch.zeros_like(logits)
+        labels[:, :P] = pos_mask  # chỉ pos hợp lệ
+
+        # ===== log-softmax =====
+        log_prob = logits - torch.logsumexp(logits, dim=1, keepdim=True)
+
+        # ===== normalize theo số pos thật =====
+        pos_count = pos_mask.sum(dim=1).clamp(min=1)
+
+        loss = -(labels * log_prob).sum(dim=1) / pos_count
+
+        return loss.mean()
+
+    def hardest_triplet_loss(self, q, pos, neg, pos_mask):
+        # ===== similarity =====
+        pos_sim = torch.matmul(q.unsqueeze(1), pos.transpose(1, 2)).squeeze(1)  # [B, P]
+        neg_sim = torch.matmul(q.unsqueeze(1), neg.transpose(1, 2)).squeeze(1)  # [B, N]
+
+        # ===== mask pos =====
+        pos_sim_masked = pos_sim.clone()
+        pos_sim_masked[pos_mask == 0] = float('inf')  # loại pad
+
+        # ===== hardest =====
+        hardest_pos = pos_sim_masked.min(dim=1).values
+        hardest_neg = neg_sim.max(dim=1).values
+
+        # ===== loss =====
+        loss = F.relu(self.margin + hardest_neg - hardest_pos)
+
+        return loss.mean()
+
+# %% [code]
+class CustomEvalFn(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, pred_topk, real_topk):
+        """
+        pred_topk: List[List[int]]  shape [B, K]
+        real_topk: List[List[int]]  shape [B, Ki]
+        """
+
+        B = len(pred_topk)
+
+        total_recall = 0.0
+        total_map = 0.0
+        total_mrp = 0.0
+
+        for i in range(B):
+            preds = pred_topk[i]
+            gts = set(real_topk[i])
+
+            # ===== Recall@K =====
+            hit = any(p in gts for p in preds)
+            total_recall += 1.0 if hit else 0.0
+
+            # ===== AP =====
+            num_hits = 0
+            ap = 0.0
+
+            for rank, p in enumerate(preds, start=1):
+                if p in gts:
+                    num_hits += 1
+                    precision_at_rank = num_hits / rank
+                    ap += precision_at_rank
+
+            if len(gts) > 0:
+                ap /= len(gts)
+
+            total_map += ap
+
+            # ===== R-Precision =====
+            r = len(gts)
+
+            if r > 0:
+                top_r = preds[:r]
+
+                tp_r = sum(p in gts for p in top_r)
+
+                rp = tp_r / r
+            else:
+                rp = 0.0
+
+            total_mrp += rp
+
+        recall = total_recall / B
+        mAP = total_map / B
+        mRP = total_mrp / B
+
+        return {
+            "recall": recall,
+            "mAP": mAP,
+            "mRP": mRP,
+        }
+
+# %% [code]
+class EncodeModel(nn.Module):
+    def __init__(
+        self,
+        backbone_name,
+        projection_dim,
+        normalize=True,
+        dropout=0.1
+    ):
+        super().__init__()
+
+        self.encoder = AutoModel.from_pretrained(backbone_name)
+
+        hidden_size = self.encoder.config.hidden_size
+
+        # mạnh hơn single linear
+        self.proj = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_size, projection_dim)
+        )
+
+        # residual projection
+        self.residual_proj = (
+            nn.Identity()
+            if hidden_size == projection_dim
+            else nn.Linear(hidden_size, projection_dim)
+        )
+
+        self.normalize = normalize
+
+    def mean_pooling(self, hidden, attention_mask):
+        mask = attention_mask.unsqueeze(-1).float()  # [N, L, 1]
+        pooled = (hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-6)
+
+        return pooled
+
+    def forward(self, input_ids, attention_mask, is_query=True):
+
+        if is_query:
+            # [B, n_parts, L]
+            B, n_parts, L = input_ids.shape
+
+            input_ids = input_ids.view(-1, L)
+            attention_mask = attention_mask.view(-1, L)
+
+            outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask
+            )
+
+            hidden = outputs.last_hidden_state # [B*n_parts, L, H]
+
+            chunk_repr = self.mean_pooling(hidden, attention_mask) # [B*n_parts, H]
+            chunk_repr = chunk_repr.view(B, n_parts, -1) # [B, n_parts, H]
+            pooled = chunk_repr.mean(dim=1)
+            # [B, H]
+
+        else:
+            # [B, K, n_parts, L]
+            B, K, n_parts, L = input_ids.shape
+
+            input_ids = input_ids.view(-1, L)
+            attention_mask = attention_mask.view(-1, L)
+
+            outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask
+            )
+
+            hidden = outputs.last_hidden_state
+
+            chunk_repr = self.mean_pooling(hidden, attention_mask) # [B*K*n_parts, H]
+            chunk_repr = chunk_repr.view(B, K, n_parts, -1) # [B, K, n_parts, H]
+            pooled = chunk_repr.mean(dim=2) # [B, K, H]
+
+        # residual MLP projection
+        emb = self.proj(pooled) + self.residual_proj(pooled)
+
+        if self.normalize:
+            emb = F.normalize(emb, dim=-1)
+
+        return emb
+
+def test_model():
+    model = nn.DataParallel(EncodeModel('vinai/phobert-base', 256, True)).to(device)
+    model.eval()
+
+    bz = 32
+    vocab_size = 1000
+    qi = torch.randint(0, vocab_size, (bz, 1, 256)).to(device)
+    qa = torch.ones(bz, 1, 256).to(device)
+    di = torch.randint(0, vocab_size, (bz, 5, 2, 256)).to(device)
+    da = torch.ones(bz, 5, 2, 256).to(device)
+
+    st = time.time()
+    with torch.no_grad():
+        encoded_text = model(qi, qa, is_query=True)
+        encoded_pos = model(di, da, is_query=False)
+        encoded_neg = model(di, da, is_query=False)
+        print(encoded_text.shape, encoded_pos.shape, encoded_neg.shape)
+    print(time.time() - st)
+
+    del model, qi, qa, di, da, encoded_text, encoded_pos, encoded_neg
+    torch.cuda.empty_cache()
+    gc.collect()
+test_model()
+
+# %% [code]
+def configure_optimizers(network, optim_params, scheduler_params):
+    try:
+        optim_params = copy.copy(optim_params)
+        scheduler_params = copy.copy(scheduler_params)
+        
+        optim_name = optim_params.pop('name')
+        scheduler_name = scheduler_params.pop('name')
+    
+        optimizer_cls = globals().get(optim_name) or getattr(optim, optim_name, None)
+        scheduler_cls = globals().get(scheduler_name) or getattr(optim.lr_scheduler, scheduler_name, None)
+    
+        if optimizer_cls is None:
+            raise ValueError(f"Optimizer '{optim_name}' is not available!")
+    
+        optimizer = optimizer_cls(network.parameters(), **optim_params)
+        
+        scheduler = None
+        if scheduler_params and scheduler_cls:  # Chỉ tạo scheduler nếu có tham số
+            scheduler = scheduler_cls(optimizer, **scheduler_params)
+    
+        return optimizer, scheduler
+
+    except KeyError as e:
+        raise ValueError(f"Missing {e} in config!!")
+            
+def freeze(self, model):
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad = False
+
+def unfreeze(self, model):
+    model.train()
+    for param in model.parameters():
+        param.requires_grad = True
+
+def reduce_batch_size(loader, ratio=0.5):
+    new_bs = max(1, int(loader.batch_size * ratio))
+
+    shuffle = isinstance(loader.sampler, RandomSampler)
+
+    new_loader = DataLoader(
+        dataset=loader.dataset,
+        batch_size=new_bs,
+        shuffle=shuffle,
+        sampler=None if shuffle else loader.sampler,
+        num_workers=loader.num_workers,
+        collate_fn=loader.collate_fn,
+        pin_memory=loader.pin_memory,
+        drop_last=loader.drop_last,
+        timeout=loader.timeout,
+        worker_init_fn=loader.worker_init_fn,
+        multiprocessing_context=loader.multiprocessing_context,
+        generator=loader.generator,
+        prefetch_factor=loader.prefetch_factor if loader.num_workers > 0 else None,
+        persistent_workers=loader.persistent_workers,
+        pin_memory_device=loader.pin_memory_device
+    )
+
+    return new_loader
+
+def list_to_tuple(x):
+    if isinstance(x, (list, tuple)):
+        return tuple(list_to_tuple(i) for i in x)
+    return x
+
+def fmt(x):
+    if isinstance(x, float):
+        return round(x, 5)
+    if isinstance(x, dict):
+        return {k: fmt(v) for k, v in x.items()}
+    if isinstance(x, list):
+        return [fmt(v) for v in x]
+    return x
+    
+class ModelEmaV3Proxy(ModelEmaV3):
+    def __getattr__(self, name):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.module, name)
+            
+class DataParallelProxy(nn.DataParallel):
+    def __getattr__(self, name):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            attr = getattr(self.module, name)
+
+            if callable(attr):
+                def wrapper(*args, **kwargs):
+                    return self._parallel_apply_method(name, *args, **kwargs)
+                return wrapper
+
+            return attr
+
+    def _parallel_apply_method(self, method_name, *inputs, **kwargs):
+        if not self.device_ids:
+            return getattr(self.module, method_name)(*inputs, **kwargs)
+
+        inputs_scattered, kwargs_scattered = self.scatter(inputs, kwargs, self.device_ids)
+
+        replicas = self.replicate(self.module, self.device_ids)
+
+        outputs = self.parallel_apply(
+            [getattr(replica, method_name) for replica in replicas],
+            inputs_scattered,
+            kwargs_scattered
+        )
+
+        return self.gather(outputs, self.output_device)
+    
+class Trainer:
+    def __init__(
+        self, training_time="00:11:30:00", eval_mode="max", topk=1, save_name="network", save_best=True, save_last=False, max_grad_norm=200.0, 
+        logging=0, logging_file=False, checkpoints_dir="", early_stopping=False, eval_from_ratio=-1, eval_every=1, device='cpu', 
+        schedule_in_step=True, use_ema=True, ema_from_ratio=-1, ema_decay=0.999, return_best=True, return_last=True
+    ):
+        self.ema_net = None
+        
+        self.training_time = self._time_str_to_seconds(training_time)
+        self.mode = eval_mode
+        self.topk = topk
+        self.device = device
+        self.logging = logging if logging < epochs else 1
+        self.logging_file = logging_file
+        self.checkpoints_dir = checkpoints_dir
+        self.early_stopping = early_stopping
+        self.eval_from_ratio = eval_from_ratio
+        self.eval_every = eval_every
+        self.save_name = save_name
+        self.save_best = save_best
+        self.save_last = save_last
+        self.return_best = return_best
+        self.return_last = return_last
+        self.max_grad_norm = max_grad_norm
+        self.schedule_in_step = schedule_in_step
+        self.use_ema = use_ema
+        self.ema_from_ratio = ema_from_ratio
+        self.ema_decay = ema_decay
+        
+        self.best_stage = [[float('-inf') if self.mode == 'max' else float('inf'), None, None]]
+        self.grad_scaler = torch.amp.GradScaler(self.device, init_scale=1024.0)
+
+    def fit(self, network, optimizer, scheduler, loss_fn, epochs, train_loader, val_loader=None, corpus_loader=None, eval_fn=None, start_epoch=1, start_training_time=None, refresh_every=3):
+        if eval_fn is None:
+            if self.mode == "max":
+                eval_fn = lambda *x: -loss_fn(*x)
+            else:
+                eval_fn = lambda *x: loss_fn(*x)
+
+        if torch.cuda.device_count() > 1:
+            network = DataParallelProxy(network)
+        network = network.to(self.device)
+            
+        if not start_training_time:
+            start_training_time = time.time()
+            
+        start_ema = int(epochs * self.ema_from_ratio)
+        start_eval = int(epochs * self.eval_from_ratio)
+        
+        if val_loader is None:
+            print(f'[Trainer CallBack] 📢 Không có Val Set, không thể đánh giá và Early Stopping!')
+        else:
+            model_to_use_str = 'mô hình EMA' if self.use_ema else 'mô hình gốc'
+            start_model_update_str = f'Bắt đầu cập nhật EMA từ epoch {start_epoch + start_ema}!' if self.use_ema else ''
+            print(f'[Trainer CallBack] 📢 Đánh giá bằng {model_to_use_str} từ epoch {start_epoch + start_eval}!', start_model_update_str)
+
+        training_log = {}
+        for epoch in range(start_epoch, epochs+start_epoch):
+            if self.use_ema and self.ema_net is None and epoch - start_epoch >= start_ema:
+                self.ema_net = ModelEmaV3Proxy(network, self.ema_decay, device=self.device)
+                
+            try:
+                eval_net = self.ema_net if (self.use_ema and self.ema_net is not None) else network
+                if (epoch - start_epoch) % refresh_every == 0:
+                    encoded_docs = self._get_encoded_docs(eval_net, corpus_loader)
+                    print(f"[Trainer CallBack] 📢 Epoch {epoch}/{epochs}: Refresh Encoded Doc (refresh_every={refresh_every})!")
+                    
+                train_loss_epoch, train_loss_epoch_dict = self._train_epoch(network, train_loader, optimizer, scheduler, loss_fn, encoded_docs)
+                logging_dict = {'lr': [group['lr'] for group in optimizer.param_groups], 'train_loss': train_loss_epoch}
+                logging_dict.update(train_loss_epoch_dict)
+                    
+                if val_loader is not None and epoch - start_epoch >= start_eval and (epoch - start_epoch - start_eval) % self.eval_every == 0:                    
+                    val_score, val_score_dict, _ = self._eval_epoch(eval_net, val_loader, eval_fn, encoded_docs)
+                    update = self._update_best_network(eval_net, val_score, epoch)
+                    logging_dict.update({'val_score': val_score, 'best_score': self.best_stage[0][0], 'new_best_model': update})
+                    logging_dict.update(val_score_dict)
+                if not self.schedule_in_step and scheduler:
+                    scheduler.step()
+                    
+            except RuntimeError as e:
+                if "out of memory" in str(e).lower():
+                    print(f"[Trainer CallBack] ⚠️ Epoch {epoch}/{epochs}: CUDA Out of Memory! Clearing GPU cache...")
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                    if torch.cuda.is_available():
+                        torch.cuda.synchronize()
+                    print(f"[Trainer CallBack] ✅ Epoch {epoch}/{epochs}: GPU memory cleared")
+                    
+                    train_loader = reduce_batch_size(train_loader, ratio=0.5)
+                    if val_loader is not None:
+                        val_loader = reduce_batch_size(val_loader, ratio=0.5)
+                        
+                    logging_dict = {'lr': [group['lr'] for group in optimizer.param_groups], 'train_loss': float('inf')}
+                else:
+                    raise
+                    
+            training_log[epoch] = logging_dict                    
+            if self.is_early_stopping(epoch):
+                print(f'[Trainer CallBack] 📢 Epoch {epoch}/{epochs}: Detect Overfitting! Breaking Training Process...')
+                break
+            if self.logging:
+                if epoch % self.logging == 0:
+                    print(f'[Trainer CallBack] 📢 Epoch {epoch}/{epochs}:', fmt(logging_dict))
+            else:
+                print(f'{epoch}...', end=' ')
+                    
+            if self._at_time_limit(start_training_time):
+                print(f'[Trainer CallBack] ⚠️ Epoch {epoch}/{epochs}: Thời gian training giới hạn là {self.training_time}, hết giờ tại epoch {epoch}/{epochs}')
+                break
+                
+        if self.logging_file:
+            os.makedirs(f'{self.checkpoints_dir}/logs', exist_ok=True)
+            with open(f"{self.checkpoints_dir}/logs/{self.save_name}_logging.json", "a", encoding="utf-8") as f:
+                f.write(json.dumps(training_log))
+                
+        if self.use_ema and self.ema_net is not None:
+            self._save_state_dict(self.ema_net.module)
+        else:
+            self._save_state_dict(network)
+        print(f'[Trainer CallBack] 📢 Kết thúc training.\n')
+
+        best_model, last_model = None, None
+        eval_net = self.ema_net.module if (self.use_ema and self.ema_net is not None) else network
+        if self.return_best :
+            best_model = self.best_stage[0][2] if self.best_stage[0][2] is not None else eval_net.state_dict()
+            best_model = {k.replace("module.", ""): v.detach().cpu().clone() for k, v in best_model.items()}
+        if self.return_last:
+            last_model = eval_net.state_dict()
+            last_model = {k.replace("module.", ""): v.detach().cpu().clone() for k, v in last_model.items()}
+
+        del network
+        torch.cuda.empty_cache()
+        gc.collect()
+        return training_log, best_model, last_model
+
+    def _time_str_to_seconds(self, time_str):
+        days, hours, minutes, seconds = map(int, time_str.split(":"))
+        return days * 86400 + hours * 3600 + minutes * 60 + seconds
+
+    def _update_best_network(self, network, val_score, epoch):
+        topk = max(1, self.topk)
+        self.best_stage.append([val_score, epoch, {k: v.detach().cpu().clone() for k, v in network.state_dict().items()}])
+        self.best_stage = sorted(self.best_stage, reverse=(self.mode == 'max'), key=lambda x: x[0])[:topk]
+        if val_score in [x[0] for x in self.best_stage]:
+            return True
+        return False
+
+    def is_early_stopping(self, epoch):
+        if self.best_stage[0][1] is None:
+            return False
+        if not self.early_stopping:
+            return False
+        return epoch - self.best_stage[0][1] >= self.early_stopping
+
+    def _at_time_limit(self, start_training_time):
+        return time.time() - start_training_time >= self.training_time
+
+    def _save_state_dict(self, network):
+        if self.topk <= 0:
+            return
+
+        if self.save_best:
+            for r in range(self.topk):
+                os.makedirs(f'{self.checkpoints_dir}/r{r+1}s', exist_ok=True)
+                
+            for rank, (score, epoch, state_dict) in enumerate(self.best_stage):
+                if state_dict is None:
+                    continue
+                state_dict = {k.replace("module.", ""): v.detach().cpu().clone() for k, v in state_dict.items()}
+                torch.save(state_dict, f'{self.checkpoints_dir}/r{rank+1}s/{self.save_name}_r{rank+1}_vs{score:.5f}_{"ema" if self.ema_net is not None else ""}.pth')
+        if self.save_last:
+            os.makedirs(f'{self.checkpoints_dir}/lasts', exist_ok=True)
+            state_dict = {k.replace("module.", ""): v.detach().cpu().clone() for k, v in network.state_dict().items()}
+            torch.save(state_dict, f'{self.checkpoints_dir}/lasts/{self.save_name}_last_{"ema" if self.ema_net is not None else ""}.pth')
+        
+    def _train_epoch(self, network, train_loader, optimizer, scheduler, loss_fn, encoded_docs):
+        network.train()
+        total_loss = 0
+        total_loss_dict = {}
+        for batch_idx, batch in enumerate(train_loader):
+            optimizer.zero_grad()
+            with torch.autocast(device_type=self.device, dtype=torch.float16):
+                loss, loss_dict = self._cal_loss(network, batch, batch_idx, loss_fn, encoded_docs)
+
+                for k, v in loss_dict.items():
+                    t = total_loss_dict.get(k, 0)
+                    total_loss_dict[k] = t + v
+            self.grad_scaler.scale(loss).backward()
+            self.grad_scaler.unscale_(optimizer)
+            grad_norm = nn.utils.clip_grad_norm_(network.parameters(), self.max_grad_norm)
+            # print(grad_norm) # Bỏ cmt dòng này để biết nên chọn max_grad_norm bằng bao nhiêu...
+            self.grad_scaler.step(optimizer)
+            self.grad_scaler.update()
+            if self.schedule_in_step and scheduler:
+                scheduler.step()
+            if self.use_ema and self.ema_net is not None:
+                self.ema_net.update(network)
+            total_loss += loss
+        return (total_loss / len(train_loader)).item(), {k: v.item() / len(train_loader) for k, v in total_loss_dict.items()}
+
+    def _eval_epoch(self, network, val_loader, eval_fn, encoded_docs):
+        network.eval()
+        total_score = 0.0
+        total_score_dict = {}
+        object_lists = None  # sẽ init sau
+    
+        with torch.no_grad():
+            for batch_idx, batch in enumerate(val_loader):
+                score, score_dict, objects = self._cal_val_score(network, batch, batch_idx, eval_fn, encoded_docs)
+                total_score += score
+
+                for k, v in score_dict.items():
+                    t = total_score_dict.get(k, 0)
+                    total_score_dict[k] = t + v
+    
+                if objects:
+                    if object_lists is None:
+                        object_lists = [[] for _ in range(len(objects))]
+    
+                    for i, obj in enumerate(objects):
+                        object_lists[i].append(obj.detach())
+    
+        if object_lists is not None:
+            object_arrays = [
+                torch.concat(obj_list, dim=0).cpu().numpy()
+                for obj_list in object_lists
+            ]
+        else:
+            object_arrays = []
+    
+        return total_score / len(val_loader), {k: v / len(val_loader) for k, v in total_score_dict.items()}, object_arrays
+
+    def _get_encoded_docs(self, network, corpus_loader):
+        network.eval()
+        with torch.no_grad():
+            encoded_docs = []
+            for batch_idx, batch in enumerate(corpus_loader):
+                input_ids = batch['input_ids'].to(self.device)
+                attn_mask = batch['attn_mask'].to(self.device)
+                encoded_doc = network(input_ids, attn_mask, is_query=False)
+                encoded_docs.append(encoded_doc)
+            encoded_docs = torch.concat(encoded_docs, dim=0).squeeze(1)
+        return encoded_docs
+
+    def _cal_loss(self, network, batch, batch_idx, loss_fn, encoded_docs):
+        # Bạn cần override _cal_loss để tính loss
+        text_input_ids = batch['text_input_ids'].to(self.device)
+        text_attn_mask = batch['text_attn_mask'].to(self.device)
+        pos_idxes = batch['pos_idxes'].to(self.device)
+        pos_mask = batch['pos_mask'].to(self.device)
+        neg_idxes = batch['neg_idxes'].to(self.device)
+
+        encoded_text = network(text_input_ids, text_attn_mask, is_query=True)
+        encoded_pos = encoded_docs[pos_idxes]
+        encoded_neg = encoded_docs[neg_idxes]
+
+        loss_dict = loss_fn(encoded_text, encoded_pos, encoded_neg, pos_mask)
+        return loss_dict['total'], loss_dict
+
+    def _cal_val_score(self, network, batch, batch_idx, eval_fn, encoded_docs):
+        # Bạn cần override _cal_val_score để tính val score, list bên cạnh là để trả về y hay pred gì đó (nếu cần)
+        text_input_ids = batch['text_input_ids'].to(self.device)
+        text_attn_mask = batch['text_attn_mask'].to(self.device)
+        gt_pos_idxes = batch['gt_pos_idxes']
+        encoded_text = network(text_input_ids, text_attn_mask, is_query=True)
+
+        scores = torch.matmul(encoded_text, encoded_docs.T)
+        topk_scores, topk_indices = torch.topk(scores, k=10)
+        pred_topk = [
+            idx[score > 0].tolist()
+            for score, idx in zip(topk_scores, topk_indices)
+        ]
+
+        pred_topk = list_to_tuple(pred_topk)
+        gt_pos_idxes = list_to_tuple(gt_pos_idxes)
+        score_dict = eval_fn(pred_topk, gt_pos_idxes)
+        return score_dict['recall'], score_dict, []
+
+# %% [code]
+def tokenize_to_parts(text, tokenizer, max_len, max_n_parts):
+    # Tokenize với overflow để chia thành nhiều đoạn
+    enc = tokenizer(
+        text,
+        max_length=max_len*max_n_parts,
+        truncation=True,
+        padding="max_length",
+        return_overflowing_tokens=True,
+        return_tensors="pt"
+    )
+
+    input_ids = enc["input_ids"].reshape(max_n_parts, max_len)          # (n_parts, max_len)
+    attn_mask = enc["attention_mask"].reshape(max_n_parts, max_len)     # (n_parts, max_len)
+
+    return input_ids, attn_mask
+
+class LawRetrievalDataset(Dataset):
+    def __init__(self, all_data, using_idxes, corpus_dict, tokenizer, max_len, max_n_parts, n_negs):
+        super().__init__()
+
+        self.all_data = all_data
+        self.using_idxes = using_idxes
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+        self.max_n_parts = max_n_parts
+        self.n_negs = n_negs
+
+        # ===== BUILD CORPUS =====
+        idx = 0
+        self.corpus_list = []
+        self.corpus_dict = {}
+
+        for doc_name, articles_dict in corpus_dict.items():
+            self.corpus_dict[doc_name] = {}
+            for article_idx, content in articles_dict.items():
+                self.corpus_list.append([doc_name, article_idx, content])
+                self.corpus_dict[doc_name][article_idx] = {
+                    'content': content,
+                    'idx': idx
+                }
+                idx += 1
+
+    def __len__(self):
+        return len(self.using_idxes)
+
+    # ===== ENCODE DOC =====
+    def _encode_contexts(self, idxes):
+        all_input_ids, all_attn_mask = [], []
+
+        for idx in idxes:
+            name, art, _ = self.corpus_list[idx]
+            corpus = self.corpus_dict[name][art]
+
+            if 'content_input_ids' in corpus:
+                content_input_ids = corpus['content_input_ids']
+                content_attn_mask = corpus['content_attn_mask']
+            else:
+                content = corpus['content']
+                content_input_ids, content_attn_mask = tokenize_to_parts(
+                    content, self.tokenizer, self.max_len, self.max_n_parts
+                )
+                corpus['content_input_ids'] = content_input_ids
+                corpus['content_attn_mask'] = content_attn_mask
+
+            all_input_ids.append(content_input_ids)
+            all_attn_mask.append(content_attn_mask)
+
+        all_input_ids = torch.stack(all_input_ids)
+        all_attn_mask = torch.stack(all_attn_mask)
+
+        return all_input_ids, all_attn_mask
+
+    def __getitem__(self, idx):
+        ridx = self.using_idxes[idx]
+        data = self.all_data[ridx]
+        
+        query_text = data['text']
+
+        text_input_ids, text_attn_mask = tokenize_to_parts(
+            query_text, self.tokenizer, self.max_len, 1
+        )
+
+        # ===== POS =====
+        gt_pos_idxes = []
+        hard_names = []
+        for law in data['relevant_law']:
+            name = law['doc']
+            art = law['art']
+            gt_pos_idxes.append(self.corpus_dict[name][art]['idx'])
+            if name not in hard_names:
+                hard_names.append(name)
+
+        pos_idxes = torch.tensor(gt_pos_idxes, dtype=torch.long)
+        pos_mask = torch.ones(len(pos_idxes))
+
+        # ===== NEG =====
+        hard_neg_idxes = []
+        for name in hard_names:
+            for content in self.corpus_dict[name].values():
+                if content['idx'] in gt_pos_idxes:
+                    continue
+                hard_neg_idxes.append(content['idx'])
+
+        easy_neg_idxes = list(range(len(self.corpus_list)))
+        for i in gt_pos_idxes + hard_neg_idxes:
+            if i in easy_neg_idxes:
+                easy_neg_idxes.remove(i)
+
+        n_hards = min(len(hard_neg_idxes), self.n_negs // 2)
+        neg_idxes = random.sample(hard_neg_idxes, n_hards) + random.sample(easy_neg_idxes, self.n_negs - n_hards)
+        neg_idxes = torch.tensor(neg_idxes, dtype=torch.long)
+
+        return {
+            'text_input_ids': text_input_ids,
+            'text_attn_mask': text_attn_mask,
+            'gt_pos_idxes': gt_pos_idxes,
+            'pos_idxes': pos_idxes,
+            'pos_mask': pos_mask,
+            'neg_idxes': neg_idxes,
+        }
+
+class CorpusDataset(Dataset):
+    def __init__(self, corpus_dict, tokenizer, max_len, max_n_parts):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+        self.max_n_parts = max_n_parts
+
+        idx = 0
+        self.corpus_list = []
+        self.corpus_dict = {}
+        for doc_name, articles_dict in corpus_dict.items():
+            self.corpus_dict[doc_name] = {}
+            for article_idx, content in articles_dict.items():
+                self.corpus_list.append([doc_name, article_idx, content])
+                self.corpus_dict[doc_name][article_idx] = {'content': content, 'idx': idx}
+                idx += 1
+
+    def __len__(self):
+        return len(self.corpus_list)
+        
+    def _encode_contexts(self, idxes):
+        all_input_ids, all_attn_mask = [], []
+        for idx in idxes:
+            name = self.corpus_list[idx][0]
+            art  = self.corpus_list[idx][1]
+            corpus = self.corpus_dict[name][art]
+            if 'content_input_ids' in corpus and 'content_attn_mask' in corpus:
+                content_input_ids = corpus['content_input_ids']
+                content_attn_mask = corpus['content_attn_mask']
+            else:
+                content = corpus['content']
+                content_input_ids, content_attn_mask = tokenize_to_parts(content, self.tokenizer, self.max_len, self.max_n_parts)
+                corpus['content_input_ids'] = content_input_ids
+                corpus['content_attn_mask'] = content_attn_mask
+                
+            all_input_ids.append(content_input_ids)
+            all_attn_mask.append(content_attn_mask)
+            
+        all_input_ids = torch.stack(all_input_ids)
+        all_attn_mask = torch.stack(all_attn_mask)
+        return all_input_ids, all_attn_mask
+
+    def __getitem__(self, idx):
+        input_ids, attn_mask = self._encode_contexts([idx])
+        
+        return {
+            'input_ids': input_ids, 
+            'attn_mask': attn_mask, 
+        }    
+
+def _pad_batch(tensor_list, pad_value=0):
+    """
+    tensor_list: list of tensors, mỗi tensor shape (Nk, max_n_parts, max_len)
+    return: tensor shape (B, max_Nk, max_n_parts, max_len)
+    """
+    max_Nk = max(t.size(0) for t in tensor_list)
+
+    padded = []
+    for t in tensor_list:
+        Nk = t.size(0)
+
+        if Nk < max_Nk:
+            pad_shape = (max_Nk - Nk, *t.shape[1:])
+            pad_tensor = t.new_full(pad_shape, pad_value)
+            t = torch.cat([t, pad_tensor], dim=0)
+
+        padded.append(t)
+
+    return torch.stack(padded)  # (B, max_Nk, max_n_parts, max_len)
+    
+def collate_fn(batch):
+    text_input_ids = torch.stack([b["text_input_ids"] for b in batch])
+    text_attn_mask = torch.stack([b["text_attn_mask"] for b in batch])
+    gt_pos_idxes = [b["gt_pos_idxes"] for b in batch]
+    neg_idxes = torch.stack([b["neg_idxes"] for b in batch])
+
+    pos_idxes = [b["pos_idxes"].unsqueeze(-1).unsqueeze(-1) for b in batch]
+    pos_mask = [b["pos_mask"].unsqueeze(-1).unsqueeze(-1) for b in batch]
+
+    # pad theo Nk
+    pos_idxes = _pad_batch(pos_idxes, pad_value=0).squeeze(-1).squeeze(-1)
+    pos_mask = _pad_batch(pos_mask, pad_value=0).squeeze(-1).squeeze(-1)
+
+    return {
+        'text_input_ids': text_input_ids,
+        'text_attn_mask': text_attn_mask,
+        'gt_pos_idxes': gt_pos_idxes,
+        'pos_idxes': pos_idxes,
+        'pos_mask': pos_mask,
+        'neg_idxes': neg_idxes,
+    }
+
+# %% [code]
+def encode_corpus(state_dicts, network, corpus_loader, device):
+    if torch.cuda.device_count() > 1:
+        network = nn.DataParallel(network)
+    network.to(device)
+    network.eval()
+
+    all_model_embs = []
+    for i, state_dict in enumerate(state_dicts):
+        # ===== load model =====
+        if torch.cuda.device_count() > 1:
+            network.module.load_state_dict(state_dict)
+        else:
+            network.load_state_dict(state_dict)
+
+        encoded_docs = []
+
+        with torch.no_grad():
+            for batch in corpus_loader:
+                input_ids = batch['input_ids'].to(device)
+                attn_mask = batch['attn_mask'].to(device)
+
+                emb = network(input_ids, attn_mask, is_query=False)  # [B, 1, D] hoặc [B, D]
+
+                encoded_docs.append(emb)
+
+        encoded_docs = torch.concat(encoded_docs, dim=0).squeeze(1)  # [N, D]
+        all_model_embs.append(encoded_docs)
+
+    # ===== ensemble =====
+    # stack → [M, N, D]
+    all_model_embs = torch.stack(all_model_embs, dim=0)
+    final_embs = all_model_embs.mean(dim=0)  # [N, D]
+
+    return final_embs
+
+def test(state_dicts, network, test_loader, device, eval_fn, encoded_docs, bm25_scores, topks=[5, 10, 15]):
+    if torch.cuda.device_count() > 1:
+        network = nn.DataParallel(network)
+    network.to(device)
+    network.eval()
+
+    per_model_scores = []
+    max_k = max(topks)
+    
+    all_scores = []
+    all_gt_pos_idxes = []
+    with torch.no_grad():
+        for batch in test_loader:
+            text_input_ids = batch['text_input_ids'].to(device)
+            text_attn_mask = batch['text_attn_mask'].to(device)
+            gt_pos_idxes = batch['gt_pos_idxes']
+            all_gt_pos_idxes.extend(gt_pos_idxes)
+
+            list_encoded_texts = []
+
+            for state_dict in state_dicts:
+                # ===== load model =====
+                if torch.cuda.device_count() > 1:
+                    network.module.load_state_dict(state_dict)
+                else:
+                    network.load_state_dict(state_dict)
+
+                encoded_text = network(text_input_ids, text_attn_mask, is_query=True)
+                list_encoded_texts.append(encoded_text)
+                
+            ensemble_encoded_text = torch.stack(list_encoded_texts, dim=0).mean(dim=0)
+            scores = torch.matmul(ensemble_encoded_text, encoded_docs.T) # B, M
+            all_scores.append(scores)
+
+    all_scores = torch.concat(all_scores, dim=0)          # (N, M)
+    
+    bm25_scores = torch.tensor(bm25_scores).to(all_scores.device)  # (N, M)    
+    bm25_scores = bm25_scores.float()
+    
+    # min-max về [0, 1]
+    bm25_scores = (
+        bm25_scores - bm25_scores.min(dim=-1, keepdim=True).values
+    ) / (
+        bm25_scores.max(dim=-1, keepdim=True).values
+        - bm25_scores.min(dim=-1, keepdim=True).values
+        + 1e-8
+    )
+    # đổi sang [-1, 1]
+    bm25_scores = bm25_scores * 2 - 1
+    
+    all_gt_pos_idxes = list_to_tuple(all_gt_pos_idxes)
+    
+    final_score = {}
+    for weight in [0, 0.25, 0.5, 0.75, 1]:
+        
+        # score cuối
+        final_scores = all_scores + weight * bm25_scores
+    
+        # topk lớn nhất
+        topk_scores, topk_indices = torch.topk(final_scores, k=max_k)
+    
+        pred_topk_full = [
+            idx[score > 0].tolist()
+            for score, idx in zip(topk_scores, topk_indices)
+        ]
+    
+        pred_topk_full = list_to_tuple(pred_topk_full)
+    
+        final_score[weight] = {}
+    
+        for k in topks:
+            pred_topk_k = [p[:k] for p in pred_topk_full]
+            final_score[weight][k] = eval_fn(pred_topk_k, all_gt_pos_idxes)
+    
+    return final_score
+
+# %% [code]
+with open(f'{train_dir}/train.json', "r", encoding="utf-8") as f:
+    data_train = json.load(f)
+    
+with open(f'{test_dir}/test.json', "r", encoding="utf-8") as f:
+    data_test = json.load(f)
+    
+with open(f'{test_dir}/corpus.json', "r", encoding="utf-8") as f:
+    data_corpus = json.load(f)
+
+print('Train:', len(data_train))
+print('Test:', len(data_test))
+print('Corpus:', len(data_corpus))
+
+# %% [code]
+# trigger_types = sorted(list(set([e['label'] for d in data_train + data_test for e in d['issues']]))) # NBR : Neighbor relation
+# bio_trigger_types = ['O'] + [f'{prefix}-{trg}' for trg in trigger_types for prefix in ['B', 'I']]
+# trigger_label2id = {l: i for i, l in enumerate(bio_trigger_types)}
+# trigger_id2label = {i: l for l, i in trigger_label2id.items()}
+
+# argument_types = sorted(list(set([a['role'] for d in data_train + data_test for e in d['issues'] for a in e['arguments']])))
+# bio_argument_types = ['O'] + [f'{prefix}-{arg}' for arg in argument_types for prefix in ['B', 'I']]
+# argument_label2id = {l: i for i, l in enumerate(bio_argument_types)}
+# argument_id2label = {i: l for l, i in argument_label2id.items()}
+
+# label2id = {
+#     'Trg': trigger_label2id, 
+#     'Arg': argument_label2id, 
+# }
+
+# id2label = {
+#     'Trg': trigger_id2label, 
+#     'Arg': argument_id2label, 
+# }
+
+# %% [code]
+# zero_events_idxes = []
+# for idx, d in enumerate(data_train):
+#     if len(d['issues']) == 0:
+#         zero_events_idxes.append(idx)
+
+# n_zero_events_samples = len(zero_events_idxes)
+# n_has_events_samples = len(data_train) - n_zero_events_samples
+
+# random.seed(42)
+# k = min(int(n_has_events_samples * zero_events_rate), len(zero_events_idxes))
+# sampled_zero_events_idxes = random.sample(zero_events_idxes, k)
+
+# new_data_train = []
+# for idx, d in enumerate(data_train):
+#     if len(d['issues']) == 0:
+#         if idx in sampled_zero_events_idxes:
+#             new_data_train.append(d)
+#     else:
+#         new_data_train.append(d)
+# data_train = new_data_train
+
+# print('Train:', len(data_train))
+
+# %% [code]
+if debug_only:
+    data_train = data_train[:20]
+    data_test = data_test[:20]
+
+    print('Train:', len(data_train))
+    print('Test:', len(data_test))
+
+# %% [code]
+tokenizer = AutoTokenizer.from_pretrained(backbone_model_name)
+
+# %% [code]
+print('Experiment name:', state_dict_save_name)
+
+# %% [code]
+if not test_only:
+    full_idxes = np.array(range(len(data_train)))
+    training_logs, best_models, last_models = [], [], []
+    start_training_time = time.time()
+    for seed in SEEDS:
+        kf = KFold(n_splits=nfolds, shuffle=True, random_state=seed)
+        generator = torch.Generator()
+        generator.manual_seed(seed)
+    
+        corpusset = CorpusDataset(data_corpus, tokenizer, **corpus_memory_params)
+        corpus_loader = DataLoader(corpusset, generator=generator, **val_loader_params)
+        for fold_idx, (tr_idx, va_idx) in enumerate(kf.split(full_idxes)):
+            if only_fold_idx is not None and only_fold_idx >= 0 and only_fold_idx != fold_idx:
+                continue
+            set_seed(seed)
+    
+            train_idxes, val_idxes = full_idxes[tr_idx], full_idxes[va_idx]
+
+            trainset = LawRetrievalDataset(data_train, train_idxes, data_corpus, tokenizer, **train_memory_params)
+            valset = LawRetrievalDataset(data_train, val_idxes, data_corpus, tokenizer, **val_memory_params)
+            
+            train_loader = DataLoader(trainset, generator=generator, collate_fn=collate_fn, **train_loader_params)
+            val_loader = DataLoader(valset, generator=generator, collate_fn=collate_fn, **val_loader_params)
+    
+            my_model = EncodeModel(
+                **model_params
+            )
+            total_params = sum(p.numel() for p in my_model.parameters())
+            print(f"Total params: {total_params:,}")
+            
+            # optimizer, scheduler = configure_optimizers(my_model, optim_params, scheduler_params)
+            encoder_params = set(map(id, my_model.encoder.parameters()))
+            other_params = [
+                p for p in my_model.parameters()
+                if id(p) not in encoder_params
+            ]        
+            optimizer = optim.AdamW([
+                {"params": my_model.encoder.parameters(), "lr": 2e-5},
+                {"params": other_params}
+            ], lr=5e-4)
+            scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=20, eta_min=1e-6)
+            
+            loss_fn = CustomLoss(
+                **loss_func_params
+            )
+            eval_fn = CustomEvalFn(**eval_func_params)
+            trainer_params['save_name'] = f'{state_dict_save_name}_s{seed}_f{fold_idx}'
+            trainer = Trainer(**trainer_params)
+    
+            print(f'Start Training Fold {fold_idx}...')
+            training_log, best_model, last_model = trainer.fit(
+                my_model, optimizer, scheduler, loss_fn, epochs, train_loader, val_loader, corpus_loader, eval_fn, 
+                start_epoch=1, start_training_time=start_training_time, refresh_every=2, 
+            )
+            
+            training_logs.append(training_log)
+            best_models.append(best_model)
+            last_models.append(last_model)
+
+# %% [code]
+def load_all_state_dicts(folder):
+    files = []
+    
+    for file in os.listdir(folder):
+        if file.endswith(".pt") or file.endswith(".pth"):
+            m = re.search(r"f(\d+)", file)   # tìm f<số>
+            if m:
+                fold = int(m.group(1))
+                files.append((fold, file))
+
+    # sort theo fold
+    files.sort(key=lambda x: x[0])
+
+    state_dicts = []
+    for fold, file in files:
+        path = os.path.join(folder, file)
+        print(f"Loading fold {fold}: {file}")
+        state_dict = torch.load(path, map_location="cpu")
+        state_dicts.append(state_dict)
+
+    return state_dicts
+
+if test_only:
+    snapshot_download(repo_id=repo_name, local_dir="", repo_type="model", allow_patterns=[f"{state_dict_save_name}/**"])
+    get_ipython().system('rm -rf .cache .gitattributes')
+    
+    best_models = load_all_state_dicts(f"{state_dict_save_name}/r1s")
+    last_models = load_all_state_dicts(f"{state_dict_save_name}/lasts")
+
+# %% [code]
+os.makedirs(f'{checkpoints_dir}/results', exist_ok=True)
+testset = LawRetrievalDataset(data_test, range(len(data_test)), data_corpus, tokenizer, **val_memory_params)
+generator = torch.Generator()
+test_loader = DataLoader(testset, generator=generator, collate_fn=collate_fn, **val_loader_params)
+eval_fn = CustomEvalFn(**eval_func_params)
+my_model = EncodeModel(
+    **model_params
+)
+total_params = sum(p.numel() for p in my_model.parameters())
+print(f"Total params: {total_params:,}")
+
+# %% [code]
+start_time = time.time()
+encoded_docs = encode_corpus(best_models, my_model, corpus_loader, device)
+best_score = test(best_models, my_model, test_loader, device, eval_fn, encoded_docs, bm25_scores)
+
+encoded_docs = encode_corpus(last_models, my_model, corpus_loader, device)
+last_score = test(last_models, my_model, test_loader, device, eval_fn, encoded_docs, bm25_scores)
+
+result_test = {"Best model": best_score, "Last model": last_score}
+
+with open(f"{checkpoints_dir}/results/{state_dict_save_name}_test.json", "w", encoding="utf-8") as f:
+    json.dump(result_test, f, ensure_ascii=False, indent=2)
+    
+print('Test:', time.time() - start_time, 's --> Done!')
+
+# %% [code]
+def dict_to_df(data):
+    """
+    data format:
+    {
+        model_name: {
+            weight: {
+                topk: {
+                    metric: value
+                }
+            }
+        }
+    }
+    """
+
+    row_tuples = []
+    row_values = []
+
+    # ===== lấy model đầu tiên =====
+    first_model = next(iter(data.values()))
+
+    # ===== weight keys =====
+    weight_keys = list(first_model.keys())
+
+    # ===== topk keys =====
+    first_weight = next(iter(first_model.values()))
+    topk_keys = list(first_weight.keys())
+
+    # ===== metric keys =====
+    first_topk = next(iter(first_weight.values()))
+    metrics = list(first_topk.keys())
+
+    for weight in weight_keys:
+
+        for topk in topk_keys:
+
+            # ===== multi index row =====
+            row_tuples.append((weight, topk))
+
+            row = {}
+
+            for model_name, model_data in data.items():
+
+                for metric in metrics:
+
+                    row[(model_name, metric)] = (
+                        model_data[weight][topk][metric]
+                    )
+
+            row_values.append(row)
+
+    # ===== dataframe =====
+    df = pd.DataFrame(row_values)
+
+    # ===== multi columns =====
+    df.columns = pd.MultiIndex.from_tuples(
+        df.columns,
+        names=["model", "metric"]
+    )
+
+    # ===== multi index =====
+    df.index = pd.MultiIndex.from_tuples(
+        row_tuples,
+        names=["weight", "topk"]
+    )
+
+    return df
+
+result_test_df = dict_to_df(result_test)
+result_test_df.to_excel(f"{checkpoints_dir}/results/{state_dict_save_name}_test_df.xlsx")
+result_test_df
+
+# %% [code]
+key = ("Best model", "recall")
+
+result_test_df_best = (
+    result_test_df
+    .sort_values(by=key, ascending=False)
+    .groupby(level="weight")
+    .head(1)
+)
+result_test_df_best.to_excel(f"{checkpoints_dir}/results/{state_dict_save_name}_test_df_best.xlsx")
+result_test_df_best
+
+# %% [code]
+def get_avg_best_score(logs):
+    return float(np.mean([list(log.values())[-1]['best_score'] for log in logs]))
+    
+def get_avg_log(logs, epochs):
+    avg_log = {}
+
+    for epoch in range(1, epochs + 1):
+        val_score = 0.0
+        train_loss = 0.0
+        n_eval = 0
+
+        for idx in range(len(logs)):
+            log = logs[idx].get(epoch, logs[idx].get(str(epoch)))
+            if log is None:
+                continue
+
+            val_score += log.get('val_score', 0.0)
+            train_loss += log.get('train_loss', 0.0)
+            n_eval += 1
+
+        if n_eval == 0:
+            continue
+
+        avg_log[epoch] = {
+            'train_loss': train_loss / n_eval,
+            'val_score': val_score / n_eval if val_score != 0 else float('inf')
+        }
+
+    return avg_log
+
+def parse_label_key(label: str):
+    try:
+        first = float(label.split('_', 1)[0]) # số đầu: trước dấu _
+        last = float(re.findall(r'_(\d+(?:\.\d+)?)$', label)[0])
+        return first, last
+    except:
+        return (0, 0)
+
+def plot_training_logs(logs_dict, save_path=None, figsize=(24, 10)):
+    fig, axes = plt.subplots(1, 2, figsize=figsize)
+
+    # ===== Plot Train Loss =====
+    for name, log in logs_dict.items():
+        epochs = sorted(log.keys())
+        train_loss = [log[e]['train_loss'] for e in epochs]
+        axes[0].plot(epochs, train_loss, label=name)
+
+    axes[0].set_xlabel('Epoch')
+    axes[0].set_ylabel('Train Loss')
+    axes[0].set_title('Training Loss')
+    axes[0].grid(True)
+
+    # ===== Plot Validation Score =====
+    for name, log in logs_dict.items():
+        epochs = sorted(log.keys())
+        val_score = [log[e]['val_score'] for e in epochs]
+        axes[1].plot(epochs, val_score, label=name)
+
+    axes[1].set_xlabel('Epoch')
+    axes[1].set_ylabel('Validation Score')
+    axes[1].set_title('Validation Score')
+    axes[1].grid(True)
+
+    # ===== Shared Legend =====
+    handles, labels = axes[0].get_legend_handles_labels()
+    pairs = list(zip(handles, labels))
+    pairs_sorted = sorted(
+        pairs,
+        key=lambda x: parse_label_key(x[1])
+    )
+    handles_sorted, labels_sorted = zip(*pairs_sorted)
+    
+    axes[0].legend(
+        handles_sorted,
+        labels_sorted,
+        loc='center left',
+        bbox_to_anchor=(1.01, 0.5),
+        borderaxespad=0.
+    )
+
+    plt.tight_layout(rect=[0, 0, 1, 1])
+
+    if save_path is not None:
+        os.makedirs(os.path.dirname(save_path), exist_ok=True) if os.path.dirname(save_path) else None
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+
+    plt.show()
+
+# %% [code]
+if not test_only:
+    snapshot_download(repo_id=repo_name, local_dir="", repo_type="model", allow_patterns=["**/*lr*.json"], ignore_patterns=[])
+    get_ipython().system('rm -rf .cache .gitattributes')
+
+# %% [code]
+if not test_only:
+    experiments = {}
+    for experiment in os.listdir(pretrained_dir):
+        experiment_logs = []
+        try:
+            for seed in SEEDS:
+                for fold_idx in range(nfolds):
+                    with open(f"{pretrained_dir}/{experiment}/logs/{experiment}_s{seed}_f{fold_idx}_logging.json", "r", encoding="utf-8") as f:
+                        experiment_log = json.load(f)
+                    experiment_logs.append(experiment_log)
+        except:
+            pass
+        experiments[experiment] = get_avg_log(experiment_logs, 1000)
+    experiments[state_dict_save_name] = get_avg_log(training_logs, 1000)
+
+# %% [code]
+if not test_only:
+    score = get_avg_best_score(training_logs)
+    state_dict_save_name, score
+
+# %% [code]
+if not test_only:
+    plot_training_logs(experiments, save_path=f'{checkpoints_dir}/logs/{state_dict_save_name}_log_plot.jpg', figsize=(18, 7.5))
+

15_125_negs_23/lasts/15_125_negs_23_s26092004_f0_last_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94009921c52a5cbad2f426fd980221234ffc02936d1db86bf96021c2ec8fbc18
+size 544003532

15_125_negs_23/lasts/15_125_negs_23_s26092004_f1_last_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:669c27e170d5caf44f714e84f3b4a22ecca76c19d4bb706105df81bfb3708e4f
+size 544003532

15_125_negs_23/lasts/15_125_negs_23_s26092004_f2_last_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:065db8570f533cb09665ee2e2089c2fb7fc4a3f9275356d2468044ab62136ac8
+size 544003532

15_125_negs_23/lasts/15_125_negs_23_s26092004_f3_last_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3c2c9ceccb394544543363ff55129c05af4e0f3ffac22c7c4f1054ba75979c4
+size 544003532

15_125_negs_23/lasts/15_125_negs_23_s26092004_f4_last_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73949b21bfa068bafa1c59a8b1ee4da196e8b4933ea3df61ed92fa75ed085fe4
+size 544003532

15_125_negs_23/logs/15_125_negs_23_s26092004_f0_logging.json

@@ -0,0 +1 @@
+{"1": {"lr": [2e-05, 0.0005], "train_loss": 4.00162935256958, "total": 4.001629373301631, "contrastive_loss": 3.859586479671823, "triplet_loss": 0.2763377926421405}, "2": {"lr": [1.988303923565381e-05, 0.0004969282409784868], "train_loss": 3.8166251182556152, "total": 3.816625078386288, "contrastive_loss": 3.6779487125052257, "triplet_loss": 0.27069397993311034}, "3": {"lr": [1.9535036904803962e-05, 0.0004877886008156408], "train_loss": 3.6060378551483154, "total": 3.6060378671091136, "contrastive_loss": 3.448955829326923, "triplet_loss": 0.31187290969899667}, "4": {"lr": [1.8964561979789496e-05, 0.00047280612778499774], "train_loss": 3.366621732711792, "total": 3.3666218531171612, "contrastive_loss": 3.2170702063519023, "triplet_loss": 0.29494147157190637}, "5": {"lr": [1.8185661446562005e-05, 0.00045234974009654937], "train_loss": 3.387317657470703, "total": 3.387317670228888, "contrastive_loss": 3.237390830764005, "triplet_loss": 0.29368729096989965}, "6": {"lr": [1.7217514421272206e-05, 0.00042692314190604356], "train_loss": 3.1950013637542725, "total": 3.1950014044209865, "contrastive_loss": 3.0526351673547243, "triplet_loss": 0.2794732441471572}, "7": {"lr": [1.60839598967785e-05, 0.00039715242044697206], "train_loss": 3.1959598064422607, "total": 3.1959597992657818, "contrastive_loss": 3.0457441144962374, "triplet_loss": 0.29347826086956524}, "8": {"lr": [1.4812909747525698e-05, 0.00036377062968501693], "train_loss": 2.9413108825683594, "total": 2.941310920842914, "contrastive_loss": 2.8023406063832565, "triplet_loss": 0.272366220735786, "val_score": 0.8347916666666667, "best_score": 0.8347916666666667, "new_best_model": true, "recall": 0.8347916666666667, "mAP": 0.3152276338892039, "mRP": 0.30004067460317463}, "9": {"lr": [1.3435661446562005e-05, 0.0003275997400965494], "train_loss": 2.808377981185913, "total": 2.808377983578073, "contrastive_loss": 2.6722603992474916, "triplet_loss": 0.2677675585284281, "val_score": 0.8331547619047619, "best_score": 0.8347916666666667, "new_best_model": false, "recall": 0.8331547619047619, "mAP": 0.318974358898652, "mRP": 0.303314484126984}, "10": {"lr": [1.1986127417882198e-05, 0.00028953039902753766], "train_loss": 2.623941659927368, "total": 2.6239415809860995, "contrastive_loss": 2.496082726928303, "triplet_loss": 0.2552257525083612, "val_score": 0.8380654761904762, "best_score": 0.8380654761904762, "new_best_model": true, "recall": 0.8380654761904762, "mAP": 0.3280994230678383, "mRP": 0.3131582341269841}, "11": {"lr": [1.0500000000000003e-05, 0.0002505], "train_loss": 2.560020923614502, "total": 2.5600210336538463, "contrastive_loss": 2.433035451831626, "triplet_loss": 0.2529264214046823, "val_score": 0.823422619047619, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.823422619047619, "mAP": 0.32061576337711006, "mRP": 0.30566071428571445}, "12": {"lr": [9.013872582117811e-06, 0.00021146960097246246], "train_loss": 2.3872716426849365, "total": 2.3872717407634823, "contrastive_loss": 2.2687649423860785, "triplet_loss": 0.23850334448160534, "val_score": 0.8282738095238096, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.8282738095238096, "mAP": 0.32642633790784825, "mRP": 0.3113030753968254}, "13": {"lr": [7.564338553438001e-06, 0.00017340025990345064], "train_loss": 2.379558801651001, "total": 2.379558856670673, "contrastive_loss": 2.2599862905649037, "triplet_loss": 0.2395484949832776, "val_score": 0.8136309523809524, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.8136309523809524, "mAP": 0.3148375602324261, "mRP": 0.3010496031746032}, "14": {"lr": [6.1870902524743065e-06, 0.00013722937031498307], "train_loss": 2.234395742416382, "total": 2.2343958213576505, "contrastive_loss": 2.1226678038121864, "triplet_loss": 0.22408026755852842, "val_score": 0.8144642857142858, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.8144642857142858, "mAP": 0.3183508286879566, "mRP": 0.30187698412698405}, "15": {"lr": [4.916040103221507e-06, 0.00010384757955302797], "train_loss": 2.273890495300293, "total": 2.273890466594377, "contrastive_loss": 2.158585870545046, "triplet_loss": 0.23285953177257526, "val_score": 0.7946726190476191, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.7946726190476191, "mAP": 0.3068012820058578, "mRP": 0.2956899801587301}, "16": {"lr": [3.7824855787278e-06, 7.40768580939564e-05], "train_loss": 2.160404920578003, "total": 2.1604048815060617, "contrastive_loss": 2.0515571517689173, "triplet_loss": 0.21843645484949833, "val_score": 0.794404761904762, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.794404761904762, "mAP": 0.30822056110638724, "mRP": 0.2965773809523809}, "17": {"lr": [2.814338553438001e-06, 4.865025990345063e-05], "train_loss": 2.244706392288208, "total": 2.244706476013796, "contrastive_loss": 2.1304703013952757, "triplet_loss": 0.22867892976588627, "val_score": 0.7798214285714287, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.7798214285714287, "mAP": 0.29549059055335086, "mRP": 0.28327876984126976}, "18": {"lr": [2.0354380202105066e-06, 2.8193872215002235e-05], "train_loss": 2.1673154830932617, "total": 2.1673155309364547, "contrastive_loss": 2.0573222182666178, "triplet_loss": 0.2213628762541806, "val_score": 0.7804464285714287, "best_score": 0.8380654761904762, "new_best_model": false, "recall": 0.7804464285714287, "mAP": 0.29625393242945336, "mRP": 0.2827435515873016}}

15_125_negs_23/logs/15_125_negs_23_s26092004_f1_logging.json

@@ -0,0 +1 @@
+{"1": {"lr": [2e-05, 0.0005], "train_loss": 4.011542797088623, "total": 4.011542789114758, "contrastive_loss": 3.8692533052884617, "triplet_loss": 0.27801003344481606}, "2": {"lr": [1.988303923565381e-05, 0.0004969282409784868], "train_loss": 3.8000850677490234, "total": 3.80008516343541, "contrastive_loss": 3.661402673625627, "triplet_loss": 0.26964882943143814}, "3": {"lr": [1.9535036904803962e-05, 0.0004877886008156408], "train_loss": 3.576650619506836, "total": 3.5766507662259617, "contrastive_loss": 3.4200247570025084, "triplet_loss": 0.3108277591973244}, "4": {"lr": [1.8964561979789496e-05, 0.00047280612778499774], "train_loss": 3.315241813659668, "total": 3.315241887019231, "contrastive_loss": 3.1669405423677883, "triplet_loss": 0.2911789297658863}, "5": {"lr": [1.8185661446562005e-05, 0.00045234974009654937], "train_loss": 3.310638427734375, "total": 3.3106385297998537, "contrastive_loss": 3.1602221842992266, "triplet_loss": 0.2964046822742475}, "6": {"lr": [1.7217514421272206e-05, 0.00042692314190604356], "train_loss": 3.071145534515381, "total": 3.0711455584369776, "contrastive_loss": 2.930558526795046, "triplet_loss": 0.2738294314381271}, "7": {"lr": [1.60839598967785e-05, 0.00039715242044697206], "train_loss": 3.0323758125305176, "total": 3.0323757822298285, "contrastive_loss": 2.887114559926317, "triplet_loss": 0.28532608695652173}, "8": {"lr": [1.4812909747525698e-05, 0.00036377062968501693], "train_loss": 2.7708935737609863, "total": 2.7708936200094065, "contrastive_loss": 2.637634889736622, "triplet_loss": 0.26275083612040134, "val_score": 0.847948717948718, "best_score": 0.847948717948718, "new_best_model": true, "recall": 0.847948717948718, "mAP": 0.33036707377306346, "mRP": 0.31258600427350425}, "9": {"lr": [1.3435661446562005e-05, 0.0003275997400965494], "train_loss": 2.643636703491211, "total": 2.643636748144858, "contrastive_loss": 2.5132964781694187, "triplet_loss": 0.25961538461538464, "val_score": 0.8403525641025641, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.8403525641025641, "mAP": 0.32817308402438605, "mRP": 0.3089620726495727}, "10": {"lr": [1.1986127417882198e-05, 0.00028953039902753766], "train_loss": 2.4531524181365967, "total": 2.453152353548286, "contrastive_loss": 2.3316401350857023, "triplet_loss": 0.24414715719063546, "val_score": 0.8432692307692308, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.8432692307692308, "mAP": 0.3334732725800433, "mRP": 0.31572702991452994}, "11": {"lr": [1.0500000000000003e-05, 0.0002505], "train_loss": 2.3931503295898438, "total": 2.3931503040734743, "contrastive_loss": 2.27245011855926, "triplet_loss": 0.2426839464882943, "val_score": 0.8309775641025641, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.8309775641025641, "mAP": 0.32532740850970004, "mRP": 0.30875133547008554}, "12": {"lr": [9.013872582117811e-06, 0.00021146960097246246], "train_loss": 2.2191965579986572, "total": 2.219196638534699, "contrastive_loss": 2.1074522905126463, "triplet_loss": 0.22366220735785952, "val_score": 0.8308653846153846, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.8308653846153846, "mAP": 0.3306287129248068, "mRP": 0.31353605769230775}, "13": {"lr": [7.564338553438001e-06, 0.00017340025990345064], "train_loss": 2.205392599105835, "total": 2.2053926908052883, "contrastive_loss": 2.0930234979227635, "triplet_loss": 0.2265886287625418, "val_score": 0.811073717948718, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.811073717948718, "mAP": 0.3186883738849884, "mRP": 0.3043613782051282}, "14": {"lr": [6.1870902524743065e-06, 0.00013722937031498307], "train_loss": 2.075293779373169, "total": 2.0752939077524037, "contrastive_loss": 1.97013217071227, "triplet_loss": 0.21080685618729098, "val_score": 0.8135737179487179, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.8135737179487179, "mAP": 0.3225645287486432, "mRP": 0.30818189102564103}, "15": {"lr": [4.916040103221507e-06, 0.00010384757955302797], "train_loss": 2.109071731567383, "total": 2.1090716613973663, "contrastive_loss": 2.0014356530230977, "triplet_loss": 0.21592809364548496, "val_score": 0.7868108974358974, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.7868108974358974, "mAP": 0.30802365923043007, "mRP": 0.29227457264957263}, "16": {"lr": [3.7824855787278e-06, 7.40768580939564e-05], "train_loss": 2.0015735626220703, "total": 2.001573645550272, "contrastive_loss": 1.8998745819397993, "triplet_loss": 0.20202759197324416, "val_score": 0.7880608974358975, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.7880608974358975, "mAP": 0.3103827075702075, "mRP": 0.29611485042735053}, "17": {"lr": [2.814338553438001e-06, 4.865025990345063e-05], "train_loss": 2.0789146423339844, "total": 2.0789145785430603, "contrastive_loss": 1.9726313460232023, "triplet_loss": 0.21247909698996656, "val_score": 0.7632692307692307, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.7632692307692307, "mAP": 0.298080503196649, "mRP": 0.28391506410256423}, "18": {"lr": [2.0354380202105066e-06, 2.8193872215002235e-05], "train_loss": 2.0088398456573486, "total": 2.0088398911083822, "contrastive_loss": 1.9065212900423285, "triplet_loss": 0.20390886287625418, "val_score": 0.7599358974358974, "best_score": 0.847948717948718, "new_best_model": false, "recall": 0.7599358974358974, "mAP": 0.298950748181217, "mRP": 0.28374492521367545}}

15_125_negs_23/logs/15_125_negs_23_s26092004_f2_logging.json

@@ -0,0 +1 @@
+{"1": {"lr": [2e-05, 0.0005], "train_loss": 3.9985885620117188, "total": 3.9985886385608276, "contrastive_loss": 3.856352228783445, "triplet_loss": 0.27801003344481606}, "2": {"lr": [1.988303923565381e-05, 0.0004969282409784868], "train_loss": 3.7763729095458984, "total": 3.776372903166806, "contrastive_loss": 3.6378802551473663, "triplet_loss": 0.270066889632107}, "3": {"lr": [1.9535036904803962e-05, 0.0004877886008156408], "train_loss": 3.6009228229522705, "total": 3.6009227535796406, "contrastive_loss": 3.4444735217652593, "triplet_loss": 0.3095735785953177}, "4": {"lr": [1.8964561979789496e-05, 0.00047280612778499774], "train_loss": 3.321500062942505, "total": 3.321500133909908, "contrastive_loss": 3.1730926411606397, "triplet_loss": 0.29264214046822745}, "5": {"lr": [1.8185661446562005e-05, 0.00045234974009654937], "train_loss": 3.293663263320923, "total": 3.2936632035169313, "contrastive_loss": 3.1453232781145486, "triplet_loss": 0.292433110367893}, "6": {"lr": [1.7217514421272206e-05, 0.00042692314190604356], "train_loss": 3.106245517730713, "total": 3.1062454682927467, "contrastive_loss": 2.9656535375078388, "triplet_loss": 0.2746655518394649}, "7": {"lr": [1.60839598967785e-05, 0.00039715242044697206], "train_loss": 3.0874581336975098, "total": 3.087458275632316, "contrastive_loss": 2.9400126479541178, "triplet_loss": 0.28929765886287623}, "8": {"lr": [1.4812909747525698e-05, 0.00036377062968501693], "train_loss": 2.8018887042999268, "total": 2.8018886604436664, "contrastive_loss": 2.66718999039768, "triplet_loss": 0.2654682274247492, "val_score": 0.8480288461538461, "best_score": 0.8480288461538461, "new_best_model": true, "recall": 0.8480288461538461, "mAP": 0.32394769143857666, "mRP": 0.30678846153846157}, "9": {"lr": [1.3435661446562005e-05, 0.0003275997400965494], "train_loss": 2.703387498855591, "total": 2.703387512411162, "contrastive_loss": 2.570802006035744, "triplet_loss": 0.2629598662207358, "val_score": 0.8471955128205128, "best_score": 0.8480288461538461, "new_best_model": false, "recall": 0.8471955128205128, "mAP": 0.3229879700642721, "mRP": 0.3085299145299146}, "10": {"lr": [1.1986127417882198e-05, 0.00028953039902753766], "train_loss": 2.489065408706665, "total": 2.4890653170072117, "contrastive_loss": 2.366029056816994, "triplet_loss": 0.24561036789297658, "val_score": 0.8501121794871794, "best_score": 0.8501121794871794, "new_best_model": true, "recall": 0.8501121794871794, "mAP": 0.3293215643442206, "mRP": 0.3152433226495727}, "11": {"lr": [1.0500000000000003e-05, 0.0002505], "train_loss": 2.424398183822632, "total": 2.4243982627639005, "contrastive_loss": 2.302968921470004, "triplet_loss": 0.243938127090301, "val_score": 0.8349038461538462, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.8349038461538462, "mAP": 0.31910655535629495, "mRP": 0.30750000000000005}, "12": {"lr": [9.013872582117811e-06, 0.00021146960097246246], "train_loss": 2.2562501430511475, "total": 2.2562500816523827, "contrastive_loss": 2.1430723260477635, "triplet_loss": 0.22805183946488294, "val_score": 0.8382371794871795, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.8382371794871795, "mAP": 0.32471741431793516, "mRP": 0.312482638888889}, "13": {"lr": [7.564338553438001e-06, 0.00017340025990345064], "train_loss": 2.2381341457366943, "total": 2.238134071579745, "contrastive_loss": 2.1247846418399874, "triplet_loss": 0.22784280936454848, "val_score": 0.8126121794871795, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.8126121794871795, "mAP": 0.3132418182319224, "mRP": 0.3041410256410257}, "14": {"lr": [6.1870902524743065e-06, 0.00013722937031498307], "train_loss": 2.096341133117676, "total": 2.0963412383727005, "contrastive_loss": 1.9905432251384825, "triplet_loss": 0.2105978260869565, "val_score": 0.8180288461538461, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.8180288461538461, "mAP": 0.3171002479098831, "mRP": 0.3051877670940172}, "15": {"lr": [4.916040103221507e-06, 0.00010384757955302797], "train_loss": 2.1340105533599854, "total": 2.134010544588733, "contrastive_loss": 2.0252397722225126, "triplet_loss": 0.21760033444816054, "val_score": 0.7936538461538462, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.7936538461538462, "mAP": 0.30449187769213815, "mRP": 0.29247943376068386}, "16": {"lr": [3.7824855787278e-06, 7.40768580939564e-05], "train_loss": 2.0339510440826416, "total": 2.0339510608277593, "contrastive_loss": 1.930956582161894, "triplet_loss": 0.20495401337792643, "val_score": 0.7944871794871795, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.7944871794871795, "mAP": 0.3070325101114163, "mRP": 0.2944324252136753}, "17": {"lr": [2.814338553438001e-06, 4.865025990345063e-05], "train_loss": 2.110990524291992, "total": 2.11099049239653, "contrastive_loss": 2.003235883936037, "triplet_loss": 0.21592809364548496, "val_score": 0.7762660256410256, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.7762660256410256, "mAP": 0.29191161074650657, "mRP": 0.2787692307692308}, "18": {"lr": [2.0354380202105066e-06, 2.8193872215002235e-05], "train_loss": 2.036123752593994, "total": 2.036123830737876, "contrastive_loss": 1.9327161910143185, "triplet_loss": 0.20589464882943143, "val_score": 0.7764743589743589, "best_score": 0.8501121794871794, "new_best_model": false, "recall": 0.7764743589743589, "mAP": 0.29474135813068114, "mRP": 0.28226228632478634}}

15_125_negs_23/logs/15_125_negs_23_s26092004_f3_logging.json

@@ -0,0 +1 @@
+{"1": {"lr": [2e-05, 0.0005], "train_loss": 4.0018391609191895, "total": 4.001839219925794, "contrastive_loss": 3.8596509850543477, "triplet_loss": 0.27842809364548493}, "2": {"lr": [1.988303923565381e-05, 0.0004969282409784868], "train_loss": 3.778738260269165, "total": 3.7787383727006687, "contrastive_loss": 3.640273078229515, "triplet_loss": 0.27027591973244147}, "3": {"lr": [1.9535036904803962e-05, 0.0004877886008156408], "train_loss": 3.5435867309570312, "total": 3.5435868585388794, "contrastive_loss": 3.387333184181647, "triplet_loss": 0.3072742474916388}, "4": {"lr": [1.8964561979789496e-05, 0.00047280612778499774], "train_loss": 3.2697670459747314, "total": 3.269767021255748, "contrastive_loss": 3.1224561200094065, "triplet_loss": 0.2899247491638796}, "5": {"lr": [1.8185661446562005e-05, 0.00045234974009654937], "train_loss": 3.22232723236084, "total": 3.2223271908967392, "contrastive_loss": 3.0757387091084865, "triplet_loss": 0.28762541806020064}, "6": {"lr": [1.7217514421272206e-05, 0.00042692314190604356], "train_loss": 3.000577926635742, "total": 3.00057789474028, "contrastive_loss": 2.8630683414114757, "triplet_loss": 0.2694397993311037}, "7": {"lr": [1.60839598967785e-05, 0.00039715242044697206], "train_loss": 2.9799818992614746, "total": 2.9799818976667014, "contrastive_loss": 2.8359964938466766, "triplet_loss": 0.2826086956521739}, "8": {"lr": [1.4812909747525698e-05, 0.00036377062968501693], "train_loss": 2.690851926803589, "total": 2.6908520344507734, "contrastive_loss": 2.5601441246211327, "triplet_loss": 0.2594063545150502, "val_score": 0.8470032051282051, "best_score": 0.8470032051282051, "new_best_model": true, "recall": 0.8470032051282051, "mAP": 0.3263872867487449, "mRP": 0.31197168803418796}, "9": {"lr": [1.3435661446562005e-05, 0.0003275997400965494], "train_loss": 2.5642125606536865, "total": 2.5642126587322323, "contrastive_loss": 2.43673124281459, "triplet_loss": 0.25355351170568563, "val_score": 0.8434615384615385, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.8434615384615385, "mAP": 0.32492035510785505, "mRP": 0.31256917735042733}, "10": {"lr": [1.1986127417882198e-05, 0.00028953039902753766], "train_loss": 2.3779919147491455, "total": 2.377991947441994, "contrastive_loss": 2.2594039049435617, "triplet_loss": 0.2391304347826087, "val_score": 0.8455448717948718, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.8455448717948718, "mAP": 0.33166132234856177, "mRP": 0.31807986111111114}, "11": {"lr": [1.0500000000000003e-05, 0.0002505], "train_loss": 2.317645788192749, "total": 2.317645733173077, "contrastive_loss": 2.2004692562447743, "triplet_loss": 0.23578595317725753, "val_score": 0.8317948717948718, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.8317948717948718, "mAP": 0.3241212594118844, "mRP": 0.31084374999999986}, "12": {"lr": [9.013872582117811e-06, 0.00021146960097246246], "train_loss": 2.158329963684082, "total": 2.1583300944554766, "contrastive_loss": 2.04957524430393, "triplet_loss": 0.21843645484949833, "val_score": 0.8333653846153847, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.8333653846153847, "mAP": 0.3288971964667278, "mRP": 0.3154201388888888}, "13": {"lr": [7.564338553438001e-06, 0.00017340025990345064], "train_loss": 2.148521900177002, "total": 2.148521806085389, "contrastive_loss": 2.0391055716320023, "triplet_loss": 0.21906354515050167, "val_score": 0.8131570512820513, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.8131570512820513, "mAP": 0.3169403927765907, "mRP": 0.30462500000000003}, "14": {"lr": [6.1870902524743065e-06, 0.00013722937031498307], "train_loss": 2.017411231994629, "total": 2.0174111458768813, "contrastive_loss": 1.9155061141304348, "triplet_loss": 0.20349080267558528, "val_score": 0.810977564102564, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.810977564102564, "mAP": 0.31981488964353544, "mRP": 0.3064236111111112}, "15": {"lr": [4.916040103221507e-06, 0.00010384757955302797], "train_loss": 2.049661874771118, "total": 2.0496617958298495, "contrastive_loss": 1.945165933972617, "triplet_loss": 0.2079849498327759, "val_score": 0.7929487179487179, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.7929487179487179, "mAP": 0.30796913029100526, "mRP": 0.29560069444444453}, "16": {"lr": [3.7824855787278e-06, 7.40768580939564e-05], "train_loss": 1.9530043601989746, "total": 1.9530043586042014, "contrastive_loss": 1.8541047469429348, "triplet_loss": 0.19701086956521738, "val_score": 0.790448717948718, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.790448717948718, "mAP": 0.30923786312067547, "mRP": 0.2963125}, "17": {"lr": [2.814338553438001e-06, 4.865025990345063e-05], "train_loss": 2.024834632873535, "total": 2.024834572272157, "contrastive_loss": 1.9212722012829222, "triplet_loss": 0.20631270903010032, "val_score": 0.7682692307692307, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.7682692307692307, "mAP": 0.29342231358533427, "mRP": 0.28179513888888896}, "18": {"lr": [2.0354380202105066e-06, 2.8193872215002235e-05], "train_loss": 1.9540187120437622, "total": 1.9540186853313126, "contrastive_loss": 1.854722038957985, "triplet_loss": 0.19680183946488294, "val_score": 0.7659775641025641, "best_score": 0.8470032051282051, "new_best_model": false, "recall": 0.7659775641025641, "mAP": 0.2962917687347374, "mRP": 0.28433333333333344}}

15_125_negs_23/logs/15_125_negs_23_s26092004_f4_logging.json

@@ -0,0 +1 @@
+{"1": {"lr": [2e-05, 0.0005], "train_loss": 4.015339374542236, "total": 4.015339624921614, "contrastive_loss": 3.872973796117266, "triplet_loss": 0.27675585284280935}, "2": {"lr": [1.988303923565381e-05, 0.0004969282409784868], "train_loss": 3.8118250370025635, "total": 3.8118251430549748, "contrastive_loss": 3.6728576864287206, "triplet_loss": 0.27069397993311034}, "3": {"lr": [1.9535036904803962e-05, 0.0004877886008156408], "train_loss": 3.581562042236328, "total": 3.5815621570599916, "contrastive_loss": 3.424886339882943, "triplet_loss": 0.3102006688963211}, "4": {"lr": [1.8964561979789496e-05, 0.00047280612778499774], "train_loss": 3.3124845027923584, "total": 3.3124844860472407, "contrastive_loss": 3.1638989911031565, "triplet_loss": 0.29285117056856186}, "5": {"lr": [1.8185661446562005e-05, 0.00045234974009654937], "train_loss": 3.281920909881592, "total": 3.2819209784568355, "contrastive_loss": 3.13327608140416, "triplet_loss": 0.2907608695652174}, "6": {"lr": [1.7217514421272206e-05, 0.00042692314190604356], "train_loss": 3.0550286769866943, "total": 3.0550288069607023, "contrastive_loss": 2.9156953435279056, "triplet_loss": 0.272366220735786}, "7": {"lr": [1.60839598967785e-05, 0.00039715242044697206], "train_loss": 3.0256552696228027, "total": 3.025655382851693, "contrastive_loss": 2.88015553145903, "triplet_loss": 0.28532608695652173}, "8": {"lr": [1.4812909747525698e-05, 0.00036377062968501693], "train_loss": 2.748055934906006, "total": 2.748055856762124, "contrastive_loss": 2.614932057052153, "triplet_loss": 0.2625418060200669, "val_score": 0.8341826923076923, "best_score": 0.8341826923076923, "new_best_model": true, "recall": 0.8341826923076923, "mAP": 0.3200077463624339, "mRP": 0.3090507478632481}, "9": {"lr": [1.3435661446562005e-05, 0.0003275997400965494], "train_loss": 2.6153652667999268, "total": 2.6153652229436664, "contrastive_loss": 2.4860684704222407, "triplet_loss": 0.2577341137123746, "val_score": 0.8270032051282051, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.8270032051282051, "mAP": 0.3134820375033916, "mRP": 0.3025918803418804}, "10": {"lr": [1.1986127417882198e-05, 0.00028953039902753766], "train_loss": 2.4208860397338867, "total": 2.4208859855116014, "contrastive_loss": 2.3004632139684364, "triplet_loss": 0.2418478260869565, "val_score": 0.8311698717948718, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.8311698717948718, "mAP": 0.3194272060693934, "mRP": 0.3088627136752138}, "11": {"lr": [1.0500000000000003e-05, 0.0002505], "train_loss": 2.360976219177246, "total": 2.360976203229515, "contrastive_loss": 2.2419562196253136, "triplet_loss": 0.24017558528428093, "val_score": 0.8157532051282051, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.8157532051282051, "mAP": 0.30936181286884407, "mRP": 0.2989495192307692}, "12": {"lr": [9.013872582117811e-06, 0.00021146960097246246], "train_loss": 2.197021961212158, "total": 2.197022096767872, "contrastive_loss": 2.086677882982337, "triplet_loss": 0.2213628762541806, "val_score": 0.8161698717948718, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.8161698717948718, "mAP": 0.31421601048450015, "mRP": 0.3050884081196578}, "13": {"lr": [7.564338553438001e-06, 0.00017340025990345064], "train_loss": 2.190218925476074, "total": 2.1902190243520065, "contrastive_loss": 2.0787920999686453, "triplet_loss": 0.2249163879598662, "val_score": 0.7955448717948719, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7955448717948719, "mAP": 0.3023958013244472, "mRP": 0.29148130341880335}, "14": {"lr": [6.1870902524743065e-06, 0.00013722937031498307], "train_loss": 2.0546295642852783, "total": 2.0546295268081103, "contrastive_loss": 1.9507830055262332, "triplet_loss": 0.20652173913043478, "val_score": 0.7972115384615385, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7972115384615385, "mAP": 0.3080783106939357, "mRP": 0.296190170940171}, "15": {"lr": [4.916040103221507e-06, 0.00010384757955302797], "train_loss": 2.089343309402466, "total": 2.089343425023516, "contrastive_loss": 1.9826729560775502, "triplet_loss": 0.21279264214046822, "val_score": 0.7751282051282051, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7751282051282051, "mAP": 0.2950348760132614, "mRP": 0.2835192307692309}, "16": {"lr": [3.7824855787278e-06, 7.40768580939564e-05], "train_loss": 1.988006830215454, "total": 1.988006897993311, "contrastive_loss": 1.8871868222852215, "triplet_loss": 0.20150501672240803, "val_score": 0.7747115384615385, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7747115384615385, "mAP": 0.2985821348019265, "mRP": 0.2871089743589745}, "17": {"lr": [2.814338553438001e-06, 4.865025990345063e-05], "train_loss": 2.057615280151367, "total": 2.0576153503213837, "contrastive_loss": 1.9525164856161161, "triplet_loss": 0.20944816053511706, "val_score": 0.7512820512820513, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7512820512820513, "mAP": 0.2843480250941189, "mRP": 0.27476816239316243}, "18": {"lr": [2.0354380202105066e-06, 2.8193872215002235e-05], "train_loss": 1.9911739826202393, "total": 1.9911739897967182, "contrastive_loss": 1.8897296816210285, "triplet_loss": 0.20244565217391305, "val_score": 0.7519070512820513, "best_score": 0.8341826923076923, "new_best_model": false, "recall": 0.7519070512820513, "mAP": 0.2883869635861824, "mRP": 0.278743856837607}}

15_125_negs_23/r1s/15_125_negs_23_s26092004_f0_r1_vs0.83807_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18bf6797e694c0273bd7361f2d753400fac06d52615d5dec68b1f7615bf6344e
+size 544005220

15_125_negs_23/r1s/15_125_negs_23_s26092004_f1_r1_vs0.84795_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7036aa624d1df075e59b37220e8c6878f0db048c59b586c5608dda58c24e3a50
+size 544005220

15_125_negs_23/r1s/15_125_negs_23_s26092004_f2_r1_vs0.85011_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33762511fd8f213f5842af057365dd4872ab4a2b4b90d205067b764bf35e5e91
+size 544005220

15_125_negs_23/r1s/15_125_negs_23_s26092004_f3_r1_vs0.84700_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0432a5361949ac3702383f03dd61f92fb8fd4a33ceb59de5d8430cf019f2157
+size 544005220

15_125_negs_23/r1s/15_125_negs_23_s26092004_f4_r1_vs0.83418_ema.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cdbc7f1f5ef70dcd35009938189ca25992b86a6f08bc5dfedf61bb06df3c1910
+size 544005220

15_125_negs_23/results/15_125_negs_23_test.json

@@ -0,0 +1,176 @@
+{
+  "Best model": {
+    "0": {
+      "5": {
+        "recall": 0.754894578313253,
+        "mAP": 0.3086572330990646,
+        "mRP": 0.3107994477911665
+      },
+      "10": {
+        "recall": 0.8396084337349398,
+        "mAP": 0.32352445247258965,
+        "mRP": 0.3107994477911665
+      },
+      "15": {
+        "recall": 0.8772590361445783,
+        "mAP": 0.3293865059353585,
+        "mRP": 0.3107994477911665
+      }
+    },
+    "0.25": {
+      "5": {
+        "recall": 0.7993222891566265,
+        "mAP": 0.3268160140562269,
+        "mRP": 0.3301330321285164
+      },
+      "10": {
+        "recall": 0.8746234939759037,
+        "mAP": 0.342968936758942,
+        "mRP": 0.3301330321285164
+      },
+      "15": {
+        "recall": 0.9039909638554217,
+        "mAP": 0.3491202217816503,
+        "mRP": 0.3301330321285164
+      }
+    },
+    "0.5": {
+      "5": {
+        "recall": 0.7676957831325302,
+        "mAP": 0.3076221552878197,
+        "mRP": 0.31167168674698975
+      },
+      "10": {
+        "recall": 0.84375,
+        "mAP": 0.32280134675607314,
+        "mRP": 0.31167168674698975
+      },
+      "15": {
+        "recall": 0.8783885542168675,
+        "mAP": 0.32905422028398446,
+        "mRP": 0.31167168674698975
+      }
+    },
+    "0.75": {
+      "5": {
+        "recall": 0.7515060240963856,
+        "mAP": 0.29712025184069735,
+        "mRP": 0.30312500000000153
+      },
+      "10": {
+        "recall": 0.8279367469879518,
+        "mAP": 0.3114545931742853,
+        "mRP": 0.30312500000000153
+      },
+      "15": {
+        "recall": 0.8640813253012049,
+        "mAP": 0.31719667363398657,
+        "mRP": 0.30312500000000153
+      }
+    },
+    "1": {
+      "5": {
+        "recall": 0.7371987951807228,
+        "mAP": 0.2890493222891577,
+        "mRP": 0.29529994979919816
+      },
+      "10": {
+        "recall": 0.8177710843373494,
+        "mAP": 0.30313895213791764,
+        "mRP": 0.29529994979919816
+      },
+      "15": {
+        "recall": 0.8565512048192772,
+        "mAP": 0.30936898559592546,
+        "mRP": 0.29529994979919816
+      }
+    }
+  },
+  "Last model": {
+    "0": {
+      "5": {
+        "recall": 0.7183734939759037,
+        "mAP": 0.2913495649263741,
+        "mRP": 0.2907693273092387
+      },
+      "10": {
+        "recall": 0.797816265060241,
+        "mAP": 0.30551323348951503,
+        "mRP": 0.2907693273092387
+      },
+      "15": {
+        "recall": 0.8347138554216867,
+        "mAP": 0.3101704129273794,
+        "mRP": 0.2907693273092387
+      }
+    },
+    "0.25": {
+      "5": {
+        "recall": 0.7899096385542169,
+        "mAP": 0.3234120858433758,
+        "mRP": 0.3268260542168697
+      },
+      "10": {
+        "recall": 0.8621987951807228,
+        "mAP": 0.3389968380466319,
+        "mRP": 0.3268260542168697
+      },
+      "15": {
+        "recall": 0.8911897590361446,
+        "mAP": 0.3453678923186647,
+        "mRP": 0.3268260542168697
+      }
+    },
+    "0.5": {
+      "5": {
+        "recall": 0.7579066265060241,
+        "mAP": 0.3044565763052222,
+        "mRP": 0.308590612449801
+      },
+      "10": {
+        "recall": 0.833960843373494,
+        "mAP": 0.31905739774574565,
+        "mRP": 0.308590612449801
+      },
+      "15": {
+        "recall": 0.870105421686747,
+        "mAP": 0.32486000513250224,
+        "mRP": 0.308590612449801
+      }
+    },
+    "0.75": {
+      "5": {
+        "recall": 0.7383283132530121,
+        "mAP": 0.2905485483601081,
+        "mRP": 0.2940637550200817
+      },
+      "10": {
+        "recall": 0.822289156626506,
+        "mAP": 0.3049776711010716,
+        "mRP": 0.2940637550200817
+      },
+      "15": {
+        "recall": 0.8576807228915663,
+        "mAP": 0.3107992218520276,
+        "mRP": 0.2940637550200817
+      }
+    },
+    "1": {
+      "5": {
+        "recall": 0.7259036144578314,
+        "mAP": 0.2814459923025444,
+        "mRP": 0.2855107931726919
+      },
+      "10": {
+        "recall": 0.8117469879518072,
+        "mAP": 0.2954643698803156,
+        "mRP": 0.2855107931726919
+      },
+      "15": {
+        "recall": 0.8490210843373494,
+        "mAP": 0.3014980214278978,
+        "mRP": 0.2855107931726919
+      }
+    }
+  }
+}