Upload 23 files

.gitattributes

@@ -95,3 +95,19 @@ report/zundamon/ZM047.webp filter=lfs diff=lfs merge=lfs -text
 report/zundamon/ZM048.webp filter=lfs diff=lfs merge=lfs -text
 report/zundamon/ZM049.webp filter=lfs diff=lfs merge=lfs -text
 report/zundamon/ZM050.webp filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoClan.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoFact.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoLynx.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoNavi.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoNeco.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rastrigin_EmoZeal.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoClan.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoFact.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoLynx.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoNavi.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoNeco.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/docs/rosenbrock_EmoZeal.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/logs/fluctuation_and_accuracy_panel.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/logs/loss_comparison_panel.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/logs/trec_gpt2_weight_pca_3panel.png filter=lfs diff=lfs merge=lfs -text
+AMP-compatible/logs/trec_weights_log.json filter=lfs diff=lfs merge=lfs -text

AMP-compatible/emoclan.py

@@ -0,0 +1,259 @@
+import torch
+from torch.optim import Optimizer
+import math
+from typing import Callable, Union, Dict, Any, Tuple
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+# Helper function
+def exists(val):
+    return val is not None
+
+class EmoClan(Optimizer):
+    def __init__(self, params: Union[list, torch.nn.Module], 
+                 lr: float = 1e-3, 
+                 betas: Tuple[float, float] = (0.9, 0.999), 
+                 eps: float = 1e-8, 
+                 weight_decay: float = 0.01,
+                 lynx_betas: Tuple[float, float] = (0.9, 0.99), # Lynx 固有の beta
+                 decoupled_weight_decay: bool = False
+                ):
+        
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        
+        # Lynx の betas もバリデーション
+        if not 0.0 <= lynx_betas[0] < 1.0:
+            raise ValueError(f"Invalid lynx_beta parameter at index 0: {lynx_betas[0]}")
+        if not 0.0 <= lynx_betas[1] < 1.0:
+            raise ValueError(f"Invalid lynx_beta parameter at index 1: {lynx_betas[1]}")
+
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
+                        lynx_betas=lynx_betas, decoupled_weight_decay=decoupled_weight_decay)
+        super().__init__(params, defaults)
+        
+        self._init_lr = lr # decoupled weight decay のために保存 (Lynx用)
+        self.should_stop = False # 全体の停止フラグ
+
+    # --- 感情機構 (Emotion Mechanism) ---
+    def _update_ema(self, param_state: Dict[str, Any], loss_val: float) -> Dict[str, float]:
+        """損失値に基づいて短期・長期 EMA を更新"""
+        # param_state は各パラメータの state['ema'] を保持する
+        ema = param_state.setdefault('ema', {'short': loss_val, 'long': loss_val})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema['short']
+        ema['long']  = 0.01 * loss_val + 0.99 * ema['long']
+        return ema
+
+    def _compute_scalar(self, ema: Dict[str, float]) -> float:
+        """EMA の差分から感情スカラー値を生成"""
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    def _decide_ratio(self, scalar: float) -> float:
+        """感情スカラーに基づいて Shadow の混合比率を決定"""
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar # 0.7～0.9
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3: # >0.3 かつ <=0.6 の場合
+            return 0.3
+        return 0.0
+
+    # --- 各最適化器のコアな勾配更新ロジック (プライベートメソッドとして統合) ---
+
+    def _lynx_update(
+        self, 
+        p: torch.Tensor, 
+        grad: torch.Tensor, 
+        param_state: Dict[str, Any], 
+        lr: float, 
+        beta1: float, 
+        beta2: float, 
+        wd_actual: float
+    ):
+        """EmoLynx のコアな勾配更新ロジック"""
+        # Stepweight decay: p = p * (1 - lr * wd)
+        p.mul_(1. - lr * wd_actual)
+
+        # Lynx 固有の EMA 状態は param_state に保持
+        if 'exp_avg_lynx' not in param_state:
+            param_state['exp_avg_lynx'] = torch.zeros_like(p)
+        exp_avg = param_state['exp_avg_lynx']
+
+        # 勾配ブレンド
+        blended_grad = grad.mul(1. - beta1).add_(exp_avg, alpha=beta1)
+        
+        # 符号ベースの更新
+        p.add_(blended_grad.sign_(), alpha = -lr)
+
+        # exp_avg 更新
+        exp_avg.mul_(beta2).add_(grad, alpha = 1. - beta2)
+
+    def _navi_update(
+        self, 
+        p: torch.Tensor, 
+        grad: torch.Tensor, 
+        param_state: Dict[str, Any], 
+        lr: float, 
+        betas: Tuple[float, float], 
+        eps: float, 
+        weight_decay: float
+    ):
+        """EmoNavi のコアな勾配更新ロジック"""
+        beta1, beta2 = betas
+
+        exp_avg = param_state.setdefault('exp_avg_navi', torch.zeros_like(p))
+        exp_avg_sq = param_state.setdefault('exp_avg_sq_navi', torch.zeros_like(p.to(torch.float32)))
+
+        exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad.to(torch.float32), grad.to(torch.float32), value=1 - beta2)
+        denom = exp_avg_sq.sqrt().add_(eps)
+
+        # Weight decay (標準的手法)
+        if weight_decay:
+            p.mul_(1 - lr * weight_decay) 
+
+        p.addcdiv_(exp_avg, denom, value=-lr)
+
+    def _fact_update(
+        self, 
+        p: torch.Tensor, 
+        grad: torch.Tensor, 
+        param_state: Dict[str, Any], 
+        lr: float, 
+        betas: Tuple[float, float], # beta2 は現状使われないが互換性のため残す (1D勾配で使用)
+        eps: float, 
+        weight_decay: float
+    ):
+        """EmoFact のコアな勾配更新ロジック (Adafactor ライク)"""
+        beta1, beta2 = betas
+
+        if grad.dim() >= 2:
+            # 行と列の2乗平均を計算 (分散の軽量な近似)
+            # gradをfloat32にキャストして計算することで数値安定性を高める
+            r_sq = torch.mean(grad.to(torch.float32) * grad.to(torch.float32), dim=tuple(range(1, grad.dim())), keepdim=True).add_(eps)
+            c_sq = torch.mean(grad.to(torch.float32) * grad.to(torch.float32), dim=0, keepdim=True).add_(eps)
+
+            param_state.setdefault('exp_avg_r_fact', torch.zeros_like(r_sq)).mul_(beta1).add_(torch.sqrt(r_sq), alpha=1 - beta1)
+            param_state.setdefault('exp_avg_c_fact', torch.zeros_like(c_sq)).mul_(beta1).add_(torch.sqrt(c_sq), alpha=1 - beta1)
+            
+            # 再構築した近似勾配の平方根の積で正規化
+            denom = torch.sqrt(param_state['exp_avg_r_fact'] * param_state['exp_avg_c_fact']).add_(eps)
+            update_term = grad / denom # grad は元の型（float16またはfloat32）
+
+        else: # 1次元(ベクトル)の勾配補正
+            exp_avg = param_state.setdefault('exp_avg_fact', torch.zeros_like(p))
+            exp_avg_sq = param_state.setdefault('exp_avg_sq_fact', torch.zeros_like(p.to(torch.float32)))
+            
+            exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+            exp_avg_sq.mul_(beta2).addcmul_(grad.to(torch.float32), grad.to(torch.float32), value=1 - beta2)
+            denom = exp_avg_sq.sqrt().add_(eps)
+            update_term = exp_avg / denom
+
+        # 最終的なパラメータ更新 (decoupled weight decayも適用)
+        # decoupled_weight_decay は __init__ でグループにdefaultsとして渡されているが、
+        # ここではfactorロジック自体がweight_decayを受け取る形式
+        p.mul_(1 - weight_decay * lr) 
+        p.add_(update_term, alpha=-lr)
+
+
+    @torch.no_grad()
+    def step(self, closure: Callable | None = None):
+        loss = None
+        if exists(closure):
+            with torch.enable_grad():
+                loss = closure()
+        loss_val = loss.item() if loss is not None else 0.0
+
+        # 全体の scalar_hist を EmoClan インスタンスで管理
+        global_scalar_hist = self.state.setdefault('global_scalar_hist', [])
+        
+        # 全体としての感情EMA状態を self.state に保持し、現在の感情スカラーを計算
+        global_ema_state = self.state.setdefault('global_ema', {'short': loss_val, 'long': loss_val})
+        global_ema_state['short'] = 0.3 * loss_val + 0.7 * global_ema_state['short']
+        global_ema_state['long'] = 0.01 * loss_val + 0.99 * global_ema_state['long']
+        current_global_scalar = self._compute_scalar(global_ema_state)
+        
+        # global_scalar_hist に現在の感情スカラーを追加
+        global_scalar_hist.append(current_global_scalar)
+        if len(global_scalar_hist) >= 33:
+            global_scalar_hist.pop(0)
+
+
+        for group in self.param_groups:
+            lr = group['lr']
+            wd = group['weight_decay']
+            eps = group['eps']
+            decoupled_wd = group['decoupled_weight_decay']
+            
+            lynx_beta1, lynx_beta2 = group['lynx_betas']
+            navi_fact_betas = group['betas'] # Navi/Fact 共通の beta を使用 (デフォルトの betas)
+            
+            # Lynx の decoupled_wd のための _wd_actual 計算
+            _wd_actual_lynx = wd
+            if decoupled_wd:
+                _wd_actual_lynx /= self._init_lr
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                param_state = self.state[p] # 各パラメータごとの状態
+
+                # --- 各パラメータごとの感情機構の更新と Shadow 処理 ---
+                # 各パラメータの state['ema'] は、それぞれの loss_val (全体で共通) を元に更新される
+                # ただし、現状の loss_val はクロージャから受け取った単一の値なので、
+                # 各パラメータ固有の「感情」を定義するより、全体としての感情が使われることになる。
+                param_ema = self._update_ema(param_state, loss_val) 
+                param_scalar = self._compute_scalar(param_ema) # 各パラメータ固有のスカラー
+
+                ratio = self._decide_ratio(param_scalar) # 各パラメータ固有の ratio
+
+                if ratio > 0:
+                    if 'shadow' not in param_state:
+                        param_state['shadow'] = p.clone()
+                    else:
+                        # Shadow を現在値にブレンド
+                        p.mul_(1 - ratio).add_(param_state['shadow'], alpha=ratio)
+                    # Shadow を現在値に追従させる
+                    param_state['shadow'].lerp_(p, 0.05)
+
+                # --- 最適化器の選択と勾配更新 ---
+                # 現在のglobal_scalar_histに記録された全体としての感情スカラーに基づいてフェーズを判断
+                # global_scalar が [-0.3, 0.3] の範囲にある場合は Navi
+                # global_scalar > 0.3 の場合は Lynx
+                # global_scalar < -0.3 の場合は Fact
+                if current_global_scalar > 0.3: # 序盤・過学習傾向時
+                    self._lynx_update(p, grad, param_state, lr, lynx_beta1, lynx_beta2, _wd_actual_lynx)
+                elif current_global_scalar < -0.3: # 終盤・発散傾向時
+                    self._fact_update(p, grad, param_state, lr, navi_fact_betas, eps, wd)
+                else: # -0.3 <= current_global_scalar <= 0.3 の中盤
+                    self._navi_update(p, grad, param_state, lr, navi_fact_betas, eps, wd)
+
+        # Early Stop判断
+        # global_scalar_hist の評価
+        if len(global_scalar_hist) >= 32:
+            buf = global_scalar_hist
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True # 外部からこれを見て判断可
+
+        return loss
+
+"""
+ Emoシリーズは、Adam、Adafactor、Lion、Tiger、等から多くを学びました。  
+ この開発において先人たちの知見に深く感謝しつつ今後も新しい可能性を探究します。 
+ The Emo series has learned much from Adam, Adafactor, Lion, and Tiger.  
+ Rather than being their successors,  
+ In its development, we deeply appreciate the insights of those who came before us—and continue to explore new possibilities beyond them.  
+"""

AMP-compatible/emofact.py

@@ -0,0 +1,124 @@
+import torch
+from torch.optim import Optimizer
+import math
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+class EmoFact(Optimizer):
+    # クラス定義＆初期化
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999),
+                 eps=1e-8, weight_decay=0.01):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+        self._init_lr = lr 
+        self.should_stop = False # 停止フラグの初期化
+
+    # 感情EMA更新(緊張と安静)
+    def _update_ema(self, state, loss_val):
+        ema = state.setdefault('ema', {})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
+        ema['long'] = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
+        return ema
+
+    # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
+    def _compute_scalar(self, ema):
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    # Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
+    def _decide_ratio(self, scalar):
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3:
+            return 0.3
+        return 0.0
+
+    # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = closure() if closure is not None else None
+        loss_val = loss.item() if loss is not None else 0.0
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                state = self.state[p]
+
+                # 感情EMA更新・スカラー生成 (既存ロジックを維持)
+                ema = self._update_ema(state, loss_val)
+                scalar = self._compute_scalar(ema)
+                ratio = self._decide_ratio(scalar)
+
+                # shadow_param：必要時のみ更新 (既存ロジックを維持)
+                if ratio > 0:
+                    if 'shadow' not in state:
+                        state['shadow'] = p.clone()
+                    else:
+                        p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio)
+                        state['shadow'].lerp_(p, 0.05)
+                
+                # --- 勾配補正ロジック ---
+                # 行列の形状が2次元以上の場合、分散情報ベースのAB近似を使用
+                if grad.dim() >= 2:
+                    # 行と列の2乗平均を計算 (分散の軽量な近似)
+                    r_sq = torch.mean(grad * grad, dim=tuple(range(1, grad.dim())), keepdim=True).add_(group['eps'])
+                    c_sq = torch.mean(grad * grad, dim=0, keepdim=True).add_(group['eps'])
+
+                    # 分散情報から勾配の近似行列を生成
+                    # AB行列として見立てたものを直接生成し更新項を計算する
+                    # A = sqrt(r_sq), B = sqrt(c_sq) とすることでAB行列の近似を再現
+                    # これをEMAで平滑化する
+                    beta1, beta2 = group['betas']
+                    
+                    state.setdefault('exp_avg_r', torch.zeros_like(r_sq)).mul_(beta1).add_(torch.sqrt(r_sq), alpha=1 - beta1)
+                    state.setdefault('exp_avg_c', torch.zeros_like(c_sq)).mul_(beta1).add_(torch.sqrt(c_sq), alpha=1 - beta1)
+                    
+                    # 再構築した近似勾配の平方根の積で正規化
+                    # これにより2次モーメントのような役割を果たす
+                    denom = torch.sqrt(state['exp_avg_r'] * state['exp_avg_c']).add_(group['eps'])
+                    
+                    # 最終的な更新項を計算
+                    update_term = grad / denom
+
+                # 1次元(ベクトル)の勾配補正(decoupled weight decay 構造に近い)
+                else:
+                    exp_avg = state.setdefault('exp_avg', torch.zeros_like(p))
+                    exp_avg_sq = state.setdefault('exp_avg_sq', torch.zeros_like(p))
+                    beta1, beta2 = group['betas']
+                    exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    update_term = exp_avg / denom
+
+                # 最終的なパラメータ更新 (decoupled weight decayも適用)
+                p.add_(p, alpha=-group['weight_decay'] * group['lr'])
+                p.add_(update_term, alpha=-group['lr'])
+
+                # --- Early Stop ロジック (既存ロ���ックを維持) ---
+                hist = self.state.setdefault('scalar_hist', [])
+                hist.append(scalar)
+                if len(hist) >= 33:
+                    hist.pop(0)
+
+        # Early Stop判断
+        if len(self.state['scalar_hist']) >= 32:
+            buf = self.state['scalar_hist']
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True
+
+        return loss
+
+"""
+ https://github.com/muooon/EmoNavi
+ Fact is inspired by Adafactor,  
+ and its VRAM-friendly design is something everyone loves.
+"""

AMP-compatible/emolynx.py

@@ -0,0 +1,134 @@
+import torch
+from torch.optim import Optimizer
+import math
+from typing import Tuple, Callable, Union
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+# Helper function (Lynx)
+def exists(val):
+    return val is not None
+
+class EmoLynx(Optimizer):
+    # クラス定義＆初期化
+    def __init__(self, params: Union[list, torch.nn.Module], lr=1e-3, betas=(0.9, 0.99), 
+    # lynx用ベータ･互換性の追加(lynx用beta1･beta2)
+                 eps=1e-8, weight_decay=0.01, decoupled_weight_decay: bool = False): 
+
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+        
+        # lynxに応じてウェイト減衰のため保存
+        self._init_lr = lr
+        self.should_stop = False # 停止フラグの初期化
+        self.decoupled_wd = decoupled_weight_decay
+
+    # 感情EMA更新(緊張と安静)
+    def _update_ema(self, state, loss_val):
+        ema = state.setdefault('ema', {})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
+        ema['long']  = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
+        return ema
+
+    # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
+    def _compute_scalar(self, ema):
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    # Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
+    def _decide_ratio(self, scalar):
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3:
+            return 0.3
+        return 0.0
+
+    # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
+    @torch.no_grad()
+    def step(self, closure: Callable | None = None): # クロージャの型ヒントを追加
+        loss = None
+        if exists(closure): # 一貫性のためにexistsヘルパーを使う
+            with torch.enable_grad():
+                loss = closure()
+        loss_val = loss.item() if loss is not None else 0.0
+
+        for group in self.param_groups:
+            # リンクス共通パラメータ抽出
+            lr, wd, beta1, beta2 = group['lr'], group['weight_decay'], *group['betas']
+            
+            # ウェイト減衰の処理を分離 (from lynx)
+            _wd_actual = wd
+            if self.decoupled_wd:
+                _wd_actual /= self._init_lr # 非連結時ウェイト減衰調整
+
+            for p in filter(lambda p: exists(p.grad), group['params']): # PGチェックにフィルタ
+
+                grad = p.grad # PG直接使用(計算に".data"不要)
+                state = self.state[p]
+
+                # EMA更新・スカラー生成(EMA差分からスカラーを生成しスパイク比率を決定)
+                ema = self._update_ema(state, loss_val)
+                scalar = self._compute_scalar(ema)
+                ratio = self._decide_ratio(scalar)
+
+                # shadow_param：必要時のみ更新(スパイク部分に現在値を5%ずつ追従させる動的履歴)
+                if ratio > 0:
+                    if 'shadow' not in state:
+                        state['shadow'] = p.clone()
+                    else:
+                        p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
+                        state['shadow'].lerp_(p, 0.05) 
+                        # lynx更新前 p で shadow 更新(現在値を5%ずつ追従)
+                        # p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
+                        # EmoNavi: p = p * (1-ratio) + shadow * ratio
+
+                # --- Start Lynx Gradient Update Logic ---
+                
+                # lynx初期化(exp_avg_sq)
+                if 'exp_avg' not in state:
+                    state['exp_avg'] = torch.zeros_like(p)
+                exp_avg = state['exp_avg']
+
+                # Stepweight decay (from lynx): p = p * (1 - lr * wd)
+                # decoupled_wd 考慮 _wd_actual 使用(EmoNaviのwdは最後に適用)
+                p.mul_(1. - lr * _wd_actual)
+
+                # 勾配ブレンド
+                # m_t = beta1 * exp_avg_prev + (1 - beta1) * grad
+                blended_grad = grad.mul(1. - beta1).add_(exp_avg, alpha=beta1)
+                
+                # p: p = p - lr * sign(blended_grad)
+                p.add_(blended_grad.sign_(), alpha = -lr)
+
+                # exp_avg = beta2 * exp_avg + (1 - beta2) * grad
+                exp_avg.mul_(beta2).add_(grad, alpha = 1. - beta2)
+
+                # --- End Lynx Gradient Update Logic ---
+
+                # Early Stop用 scalar記録(バッファ共通で管理/最大32件保持/動静評価)
+                # この部分は p.state ではなく self.state にアクセスする
+                hist = self.state.setdefault('scalar_hist', [])
+                hist.append(scalar)
+                if len(hist) >= 33:
+                    hist.pop(0)
+
+        # Early Stop判断(静けさの合図) - This part is outside the inner loop
+        if len(self.state['scalar_hist']) >= 32:
+            buf = self.state['scalar_hist']
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True # 外部からこれを見て判断可
+
+        return loss
+
+"""
+ https://github.com/muooon/EmoNavi
+ Lynx was developed with inspiration from Lion and Tiger, 
+ which we deeply respect for their lightweight and intelligent design.  
+ Lynx also integrates EmoNAVI to enhance its capabilities.
+"""

AMP-compatible/emonavi.py

@@ -0,0 +1,108 @@
+import torch
+from torch.optim import Optimizer
+import math
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+class EmoNavi(Optimizer):
+    # クラス定義＆初期化
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999),
+                 eps=1e-8, weight_decay=0.01):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+        self._init_lr = lr 
+        self.should_stop = False # 停止フラグの初期化
+
+    # 感情EMA更新(緊張と安静)
+    def _update_ema(self, state, loss_val):
+        ema = state.setdefault('ema', {})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
+        ema['long']  = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
+        return ema
+
+    # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
+    def _compute_scalar(self, ema):
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    # Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
+    def _decide_ratio(self, scalar):
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3:
+            return 0.3
+        return 0.0
+
+    # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = closure() if closure is not None else None
+        loss_val = loss.item() if loss is not None else 0.0
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                state = self.state[p]
+
+                # EMA更新・スカラー生成(EMA差分からスカラーを生成しスパイク比率を決定)
+                ema = self._update_ema(state, loss_val)
+                scalar = self._compute_scalar(ema)
+                ratio = self._decide_ratio(scalar)
+
+                # shadow_param：必要時のみ更新(スパイク部分に現在値を5%ずつ追従させる動的履歴)
+                if ratio > 0:
+                    if 'shadow' not in state:
+                        state['shadow'] = p.clone()
+                    else:
+                        p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio)
+                        state['shadow'].lerp_(p, 0.05)
+                
+                # スカラー生成：短期と長期EMAの差分から信号を得る(高ぶりの強さ)
+                # 混合比率：スカラーが閾値を超える場合にのみ計算される(信頼できる感情信号かどうかの選別)
+                # → スカラー値が小さい場合は ratio = 0 となり、shadow混合は行われない
+                # → 信頼できる強い差分のときのみ感情機構が発動する(暗黙の信頼度判定)                
+                
+                # 1次・2次モーメントを使った勾配補正(decoupled weight decay 構造に近い)
+                exp_avg = state.setdefault('exp_avg', torch.zeros_like(p))
+                exp_avg_sq = state.setdefault('exp_avg_sq', torch.zeros_like(p))
+                beta1, beta2 = group['betas']
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                denom = exp_avg_sq.sqrt().add_(group['eps'])
+
+                step_size = group['lr']
+                if group['weight_decay']:
+                    p.add_(p, alpha=-group['weight_decay'] * step_size)
+                p.addcdiv_(exp_avg, denom, value=-step_size)
+
+        # 感情機構の発火が収まり"十分に安定"していることを外部伝達できる(自動停止ロジックではない)
+                # Early Stop用 scalar 記録(バッファ共通で管理/最大32件保持/動静評価)
+                hist = self.state.setdefault('scalar_hist', [])
+                hist.append(scalar)
+                if len(hist) >= 33:
+                    hist.pop(0)
+
+        # Early Stop判断(静けさの合図)
+        if len(self.state['scalar_hist']) >= 32:
+            buf = self.state['scalar_hist']
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True  # 💡 外部からこれを見て判断可
+
+        # 32ステップ分のスカラー値の静かな条件を満たした時"フラグ" should_stop = True になるだけ
+
+        return loss
+
+"""
+ https://github.com/muooon/EmoNavi
+ An emotion-driven optimizer that feels loss and navigates accordingly.
+ Don't think. Feel. Don't stop. Keep running. Believe in what's beyond.
+"""

AMP-compatible/emoneco.py

@@ -0,0 +1,150 @@
+import torch
+from torch.optim import Optimizer
+import math
+from typing import Tuple, Callable, Union
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+# Helper function (Lynx)
+def exists(val):
+    return val is not None
+# Soft Sign 関数
+def softsign(x):
+    return x / (1 + x.abs())
+
+class EmoNeco(Optimizer):
+    # クラス定義＆初期化
+    def __init__(self, params: Union[list, torch.nn.Module], lr=1e-3, betas=(0.9, 0.99), 
+    # neco用ベータ･互換性の追加(neco用beta1･beta2)
+                 eps=1e-8, weight_decay=0.01, decoupled_weight_decay: bool = False): 
+
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+        
+        # ウェイト減衰のため保存
+        self._init_lr = lr
+        self.decoupled_wd = decoupled_weight_decay
+        self.should_stop = False # 停止フラグの初期化
+
+    # 感情EMA更新(緊張と安静)
+    def _update_ema(self, state, loss_val):
+        ema = state.setdefault('ema', {})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
+        ema['long']  = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
+        return ema
+
+    # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
+    def _compute_scalar(self, ema):
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    # Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
+    def _decide_ratio(self, scalar):
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3:
+            return 0.3
+        return 0.0
+
+    # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
+    @torch.no_grad()
+    def step(self, closure: Callable | None = None): # クロージャの型ヒントを追加
+        loss = None
+        if exists(closure): # 一貫性のためにexistsヘルパーを使う
+            with torch.enable_grad():
+                loss = closure()
+        loss_val = loss.item() if loss is not None else 0.0
+
+        for group in self.param_groups:
+            # 共通パラメータ抽出
+            lr, wd, beta1, beta2 = group['lr'], group['weight_decay'], *group['betas']
+            
+            # ウェイト減衰の処理を分離 (from lynx)
+            _wd_actual = wd
+            if self.decoupled_wd:
+                _wd_actual /= self._init_lr # 非連結時ウェイト減衰調整
+
+            for p in filter(lambda p: exists(p.grad), group['params']): # PGチェックにフィルタ
+
+                grad = p.grad # PG直接使用(計算に".data"不要)
+                state = self.state[p]
+
+                # EMA更新・スカラー生成(EMA差分からスカラーを生成しスパイク比率を決定)
+                ema = self._update_ema(state, loss_val)
+                scalar = self._compute_scalar(ema)
+                ratio = self._decide_ratio(scalar)
+
+                # shadow_param：必要時のみ更新(スパイク部分に現在値を5%ずつ追従させる動的履歴)
+                if ratio > 0:
+                    if 'shadow' not in state:
+                        state['shadow'] = p.clone()
+                    else:
+                        p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
+                        state['shadow'].lerp_(p, 0.05) 
+                        # 更新前 p で shadow 更新(現在値を5%ずつ追従)
+                        # p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
+                        # EmoNavi: p = p * (1-ratio) + shadow * ratio
+
+                # --- Start Neco Gradient Update Logic ---
+                
+                # neco初期化(exp_avg_sq)
+                if 'exp_avg' not in state:
+                    state['exp_avg'] = torch.zeros_like(p)
+                exp_avg = state['exp_avg']
+
+                # Stepweight decay (from lynx): p = p * (1 - lr * wd)
+                # decoupled_wd 考慮 _wd_actual 使用(EmoNaviのwdは最後に適用)
+                p.mul_(1. - lr * _wd_actual)
+
+                # 勾配ブレンド
+                # m_t = beta1 * exp_avg_prev + (1 - beta1) * grad
+                blended_grad = grad.mul(1. - beta1).add_(exp_avg, alpha=beta1)
+                grad_norm = torch.norm(grad, dtype=torch.float32) # 勾配ノルムの計算
+
+                # scalar < -0.3 の場合のみ SoftSign、それ以外 Cautious (終盤や発散傾向をSSに)
+                # p - lr * softsign(blended_grad) (from softsign)
+                # p - lr * direction * mask (from Cautious)
+                # safe_norm 極値のブレンド勾配に対するスケーリング
+                if 0.3 < scalar <= 0.5:
+                    safe_norm = grad_norm + eps
+                    modified_grad = softsign(blended_grad) * safe_norm
+                    p.add_(-lr * modified_grad) 
+                elif scalar < -0.3:
+                    p.add_(softsign(blended_grad), alpha = -lr)  # Soft Sign 処理
+                else:
+                    direction = blended_grad.sign()    # 勾配方向の符号 Cautious 処理
+                    mask = (direction == grad.sign())  # 過去の勾配と方向が一致している部分のみ更新
+                    p.add_(direction * mask, alpha = -lr)  # Cautious 更新
+
+                # exp_avg = beta2 * exp_avg + (1 - beta2) * grad
+                exp_avg.mul_(beta2).add_(grad, alpha = 1. - beta2)
+
+                # --- End Neco Gradient Update Logic ---
+
+                # Early Stop用 scalar記録(バッファ共通で管理/最大32件保持/動静評価)
+                # この部分は p.state ではなく self.state にアクセスする
+                hist = self.state.setdefault('scalar_hist', [])
+                hist.append(scalar)
+                if len(hist) >= 33:
+                    hist.pop(0)
+
+        # Early Stop判断(静けさの合図) This part is outside the inner loop
+        if len(self.state['scalar_hist']) >= 32:
+            buf = self.state['scalar_hist']
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True # 外部からこれを見て判断可
+
+        return loss
+
+"""
+ https://github.com/muooon/EmoNavi
+ Neco was developed with inspiration from Lion, Tiger, Cautious, softsign, and Lynx 
+ which we deeply respect for their lightweight and intelligent design.  
+ Neco also integrates EmoNAVI to enhance its capabilities.
+"""

AMP-compatible/emozeal.py

@@ -0,0 +1,148 @@
+import torch
+from torch.optim import Optimizer
+import math
+
+"""
+AMP対応完了(202507) p.data -> p 修正済み
+"""
+
+# Soft Sign 関数
+def softsign(x): 
+    return x / (1 + x.abs())
+    
+class EmoZeal(Optimizer):
+    # クラス定義＆初期化
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999),
+                 eps=1e-8, weight_decay=0.01):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+        self.alpha_prev = getattr(self, 'alpha_prev', 1.0)
+        self._init_lr = lr 
+        self.should_stop = False # 停止フラグの初期化
+
+    # 感情EMA更新(緊張と安静)
+    def _update_ema(self, state, loss_val):
+        ema = state.setdefault('ema', {})
+        ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
+        ema['long'] = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
+        return ema
+
+    # 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
+    def _compute_scalar(self, ema):
+        diff = ema['short'] - ema['long']
+        return math.tanh(5 * diff)
+
+    # Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
+    def _decide_ratio(self, scalar):
+        if scalar > 0.6:
+            return 0.7 + 0.2 * scalar
+        elif scalar < -0.6:
+            return 0.1
+        elif abs(scalar) > 0.3:
+            return 0.3
+        return 0.0
+
+    # 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = closure() if closure is not None else None
+        loss_val = loss.item() if loss is not None else 0.0
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                state = self.state[p]
+
+                # 感情EMA更新・スカラー生成 (既存ロジックを維持)
+                ema = self._update_ema(state, loss_val)
+                scalar = self._compute_scalar(ema)
+                ratio = self._decide_ratio(scalar)
+
+                # shadow_param：必要時のみ更新 (既存ロジックを維持)
+                if ratio > 0:
+                    if 'shadow' not in state:
+                        state['shadow'] = p.clone()
+                    else:
+                        p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio)
+                        state['shadow'].lerp_(p, 0.05)
+                
+                # --- 勾配補正ロジック ---
+                # 行列の形状が2次元以上の場合、分散情報ベースのAB近似を使用
+                if grad.dim() >= 2:
+                    # 行と列の2乗平均を計算 (分散の軽量な近似)
+                    r_sq = torch.mean(grad * grad, dim=tuple(range(1, grad.dim())), keepdim=True).add_(group['eps'])
+                    c_sq = torch.mean(grad * grad, dim=0, keepdim=True).add_(group['eps'])
+
+                    # 分散情報から勾配の近似行列を生成
+                    # AB行列として見立てたものを直接生成し更新項を計算する
+                    # A = sqrt(r_sq), B = sqrt(c_sq) とすることでAB行列の近似を再現
+                    # これをEMAで平滑化する
+                    beta1, beta2 = group['betas'] 
+                    eps = group['eps'] 
+                    lr = group['lr']   
+                    exp_avg = state.setdefault('exp_avg', torch.zeros_like(p))
+                    blended_grad = grad.mul(1 - beta1).add_(exp_avg, alpha=beta1)
+                    grad_norm = torch.norm(grad, dtype=torch.float32)
+                    # scalar < -0.3 の場合のみ SoftSign、それ以外 Cautious (終盤や発散傾向をSSに)
+                    # p - lr * softsign(blended_grad) (from softsign)
+                    # p - lr * direction * mask (from Cautious)
+                    # safe_norm 極値のブレンド勾配に対するスケーリング
+                    if 0.3 < scalar <= 0.5:
+                        safe_norm = grad_norm + eps
+                        modified_grad = softsign(blended_grad) * safe_norm
+                        p.add_(-lr * modified_grad) 
+                    elif scalar < -0.3:
+                        direction = blended_grad.sign()    # 勾配方向の符号 Cautious 処理
+                        mask = (direction == grad.sign())  # 過去の勾配と方向が一致する部分のみ更新
+                        p.add_(direction * mask, alpha = -lr)  # Cautious 更新
+                    else:
+                        p.add_(softsign(blended_grad), alpha = -lr)  # Soft Sign 処理
+                    
+                    state.setdefault('exp_avg_r', torch.zeros_like(r_sq)).mul_(beta1).add_(torch.sqrt(r_sq), alpha=1 - beta1)
+                    state.setdefault('exp_avg_c', torch.zeros_like(c_sq)).mul_(beta1).add_(torch.sqrt(c_sq), alpha=1 - beta1)
+                    
+                    # 再構築した近似勾配の平方根の積で正規化
+                    # これにより2次モーメントのような役割を果たす
+                    denom = torch.sqrt(state['exp_avg_r'] * state['exp_avg_c']) + eps
+                    
+                    # 最終的な更新項を計算
+                    update_term = grad / denom
+
+                # 1次元(ベクトル)の勾配補正(decoupled weight decay 構造に近い)
+                else:
+                    exp_avg = state.setdefault('exp_avg', torch.zeros_like(p))
+                    exp_avg_sq = state.setdefault('exp_avg_sq', torch.zeros_like(p))
+                    beta1, beta2 = group['betas']
+                    exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    update_term = exp_avg / denom
+
+                # 最終的なパラメータ更新 (decoupled weight decayも適用)
+                p.add_(p, alpha=-group['weight_decay'] * group['lr'])
+                p.add_(update_term, alpha=-group['lr'])
+
+                # --- Early Stop ロジック (既存ロジックを維持) ---
+                hist = self.state.setdefault('scalar_hist', [])
+                hist.append(scalar)
+                if len(hist) >= 33:
+                    hist.pop(0)
+
+        # Early Stop判断
+        if len(self.state['scalar_hist']) >= 32:
+            buf = self.state['scalar_hist']
+            avg_abs = sum(abs(s) for s in buf) / len(buf)
+            std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
+            if avg_abs < 0.05 and std < 0.005:
+                self.should_stop = True
+
+        return loss
+
+"""
+ https://github.com/muooon/EmoNavi
+ Zeal is inspired by Adafactor, and EmoFact,  
+ and its VRAM-friendly design is something everyone loves.
+"""

AMP-compatible/logs/trec_squad_step_accuracy.json

@@ -0,0 +1,2431 @@
+{
+  "TREC": {
+    "EmoNAVI": [
+      [
+        10,
+        0.274
+      ],
+      [
+        20,
+        0.258
+      ],
+      [
+        30,
+        0.336
+      ],
+      [
+        40,
+        0.374
+      ],
+      [
+        50,
+        0.332
+      ],
+      [
+        60,
+        0.422
+      ],
+      [
+        70,
+        0.308
+      ],
+      [
+        80,
+        0.168
+      ],
+      [
+        90,
+        0.422
+      ],
+      [
+        100,
+        0.32
+      ],
+      [
+        110,
+        0.494
+      ],
+      [
+        120,
+        0.31
+      ],
+      [
+        130,
+        0.344
+      ],
+      [
+        140,
+        0.474
+      ],
+      [
+        150,
+        0.37
+      ],
+      [
+        160,
+        0.402
+      ],
+      [
+        170,
+        0.58
+      ],
+      [
+        180,
+        0.474
+      ],
+      [
+        190,
+        0.516
+      ],
+      [
+        200,
+        0.518
+      ],
+      [
+        210,
+        0.422
+      ],
+      [
+        220,
+        0.522
+      ],
+      [
+        230,
+        0.564
+      ],
+      [
+        240,
+        0.51
+      ],
+      [
+        250,
+        0.672
+      ],
+      [
+        260,
+        0.548
+      ],
+      [
+        270,
+        0.57
+      ],
+      [
+        280,
+        0.538
+      ],
+      [
+        290,
+        0.508
+      ],
+      [
+        300,
+        0.672
+      ],
+      [
+        310,
+        0.606
+      ],
+      [
+        320,
+        0.6
+      ],
+      [
+        330,
+        0.65
+      ],
+      [
+        340,
+        0.662
+      ],
+      [
+        350,
+        0.652
+      ],
+      [
+        360,
+        0.67
+      ],
+      [
+        370,
+        0.632
+      ],
+      [
+        380,
+        0.634
+      ],
+      [
+        390,
+        0.61
+      ],
+      [
+        400,
+        0.7
+      ],
+      [
+        410,
+        0.67
+      ],
+      [
+        420,
+        0.704
+      ],
+      [
+        430,
+        0.692
+      ],
+      [
+        440,
+        0.724
+      ],
+      [
+        450,
+        0.724
+      ],
+      [
+        460,
+        0.722
+      ],
+      [
+        470,
+        0.668
+      ],
+      [
+        480,
+        0.694
+      ],
+      [
+        490,
+        0.744
+      ],
+      [
+        500,
+        0.732
+      ]
+    ],
+    "EmoFACT": [
+      [
+        10,
+        0.246
+      ],
+      [
+        20,
+        0.228
+      ],
+      [
+        30,
+        0.282
+      ],
+      [
+        40,
+        0.328
+      ],
+      [
+        50,
+        0.234
+      ],
+      [
+        60,
+        0.326
+      ],
+      [
+        70,
+        0.332
+      ],
+      [
+        80,
+        0.51
+      ],
+      [
+        90,
+        0.308
+      ],
+      [
+        100,
+        0.332
+      ],
+      [
+        110,
+        0.3
+      ],
+      [
+        120,
+        0.478
+      ],
+      [
+        130,
+        0.36
+      ],
+      [
+        140,
+        0.44
+      ],
+      [
+        150,
+        0.466
+      ],
+      [
+        160,
+        0.396
+      ],
+      [
+        170,
+        0.408
+      ],
+      [
+        180,
+        0.414
+      ],
+      [
+        190,
+        0.376
+      ],
+      [
+        200,
+        0.448
+      ],
+      [
+        210,
+        0.354
+      ],
+      [
+        220,
+        0.426
+      ],
+      [
+        230,
+        0.492
+      ],
+      [
+        240,
+        0.558
+      ],
+      [
+        250,
+        0.488
+      ],
+      [
+        260,
+        0.434
+      ],
+      [
+        270,
+        0.564
+      ],
+      [
+        280,
+        0.394
+      ],
+      [
+        290,
+        0.524
+      ],
+      [
+        300,
+        0.472
+      ],
+      [
+        310,
+        0.498
+      ],
+      [
+        320,
+        0.618
+      ],
+      [
+        330,
+        0.588
+      ],
+      [
+        340,
+        0.528
+      ],
+      [
+        350,
+        0.594
+      ],
+      [
+        360,
+        0.452
+      ],
+      [
+        370,
+        0.562
+      ],
+      [
+        380,
+        0.566
+      ],
+      [
+        390,
+        0.696
+      ],
+      [
+        400,
+        0.592
+      ],
+      [
+        410,
+        0.33
+      ],
+      [
+        420,
+        0.546
+      ],
+      [
+        430,
+        0.598
+      ],
+      [
+        440,
+        0.66
+      ],
+      [
+        450,
+        0.584
+      ],
+      [
+        460,
+        0.652
+      ],
+      [
+        470,
+        0.686
+      ],
+      [
+        480,
+        0.65
+      ],
+      [
+        490,
+        0.7
+      ],
+      [
+        500,
+        0.716
+      ]
+    ],
+    "EmoZEAL": [
+      [
+        10,
+        0.248
+      ],
+      [
+        20,
+        0.268
+      ],
+      [
+        30,
+        0.348
+      ],
+      [
+        40,
+        0.302
+      ],
+      [
+        50,
+        0.332
+      ],
+      [
+        60,
+        0.31
+      ],
+      [
+        70,
+        0.236
+      ],
+      [
+        80,
+        0.16
+      ],
+      [
+        90,
+        0.35
+      ],
+      [
+        100,
+        0.244
+      ],
+      [
+        110,
+        0.318
+      ],
+      [
+        120,
+        0.312
+      ],
+      [
+        130,
+        0.54
+      ],
+      [
+        140,
+        0.418
+      ],
+      [
+        150,
+        0.43
+      ],
+      [
+        160,
+        0.352
+      ],
+      [
+        170,
+        0.47
+      ],
+      [
+        180,
+        0.538
+      ],
+      [
+        190,
+        0.444
+      ],
+      [
+        200,
+        0.486
+      ],
+      [
+        210,
+        0.596
+      ],
+      [
+        220,
+        0.414
+      ],
+      [
+        230,
+        0.384
+      ],
+      [
+        240,
+        0.67
+      ],
+      [
+        250,
+        0.344
+      ],
+      [
+        260,
+        0.398
+      ],
+      [
+        270,
+        0.55
+      ],
+      [
+        280,
+        0.612
+      ],
+      [
+        290,
+        0.616
+      ],
+      [
+        300,
+        0.488
+      ],
+      [
+        310,
+        0.46
+      ],
+      [
+        320,
+        0.53
+      ],
+      [
+        330,
+        0.582
+      ],
+      [
+        340,
+        0.376
+      ],
+      [
+        350,
+        0.66
+      ],
+      [
+        360,
+        0.682
+      ],
+      [
+        370,
+        0.642
+      ],
+      [
+        380,
+        0.686
+      ],
+      [
+        390,
+        0.612
+      ],
+      [
+        400,
+        0.496
+      ],
+      [
+        410,
+        0.572
+      ],
+      [
+        420,
+        0.58
+      ],
+      [
+        430,
+        0.684
+      ],
+      [
+        440,
+        0.72
+      ],
+      [
+        450,
+        0.624
+      ],
+      [
+        460,
+        0.616
+      ],
+      [
+        470,
+        0.718
+      ],
+      [
+        480,
+        0.71
+      ],
+      [
+        490,
+        0.696
+      ],
+      [
+        500,
+        0.658
+      ]
+    ],
+    "EmoLYNX": [
+      [
+        10,
+        0.22
+      ],
+      [
+        20,
+        0.228
+      ],
+      [
+        30,
+        0.242
+      ],
+      [
+        40,
+        0.222
+      ],
+      [
+        50,
+        0.398
+      ],
+      [
+        60,
+        0.278
+      ],
+      [
+        70,
+        0.298
+      ],
+      [
+        80,
+        0.312
+      ],
+      [
+        90,
+        0.254
+      ],
+      [
+        100,
+        0.306
+      ],
+      [
+        110,
+        0.484
+      ],
+      [
+        120,
+        0.336
+      ],
+      [
+        130,
+        0.31
+      ],
+      [
+        140,
+        0.33
+      ],
+      [
+        150,
+        0.376
+      ],
+      [
+        160,
+        0.354
+      ],
+      [
+        170,
+        0.394
+      ],
+      [
+        180,
+        0.558
+      ],
+      [
+        190,
+        0.566
+      ],
+      [
+        200,
+        0.578
+      ],
+      [
+        210,
+        0.548
+      ],
+      [
+        220,
+        0.53
+      ],
+      [
+        230,
+        0.57
+      ],
+      [
+        240,
+        0.594
+      ],
+      [
+        250,
+        0.596
+      ],
+      [
+        260,
+        0.64
+      ],
+      [
+        270,
+        0.6
+      ],
+      [
+        280,
+        0.544
+      ],
+      [
+        290,
+        0.464
+      ],
+      [
+        300,
+        0.548
+      ],
+      [
+        310,
+        0.622
+      ],
+      [
+        320,
+        0.678
+      ],
+      [
+        330,
+        0.658
+      ],
+      [
+        340,
+        0.658
+      ],
+      [
+        350,
+        0.686
+      ],
+      [
+        360,
+        0.694
+      ],
+      [
+        370,
+        0.686
+      ],
+      [
+        380,
+        0.61
+      ],
+      [
+        390,
+        0.606
+      ],
+      [
+        400,
+        0.666
+      ],
+      [
+        410,
+        0.68
+      ],
+      [
+        420,
+        0.638
+      ],
+      [
+        430,
+        0.626
+      ],
+      [
+        440,
+        0.546
+      ],
+      [
+        450,
+        0.49
+      ],
+      [
+        460,
+        0.508
+      ],
+      [
+        470,
+        0.546
+      ],
+      [
+        480,
+        0.562
+      ],
+      [
+        490,
+        0.62
+      ],
+      [
+        500,
+        0.624
+      ]
+    ],
+    "EmoNECO": [
+      [
+        10,
+        0.164
+      ],
+      [
+        20,
+        0.198
+      ],
+      [
+        30,
+        0.29
+      ],
+      [
+        40,
+        0.372
+      ],
+      [
+        50,
+        0.236
+      ],
+      [
+        60,
+        0.384
+      ],
+      [
+        70,
+        0.23
+      ],
+      [
+        80,
+        0.406
+      ],
+      [
+        90,
+        0.476
+      ],
+      [
+        100,
+        0.226
+      ],
+      [
+        110,
+        0.398
+      ],
+      [
+        120,
+        0.234
+      ],
+      [
+        130,
+        0.37
+      ],
+      [
+        140,
+        0.568
+      ],
+      [
+        150,
+        0.554
+      ],
+      [
+        160,
+        0.542
+      ],
+      [
+        170,
+        0.578
+      ],
+      [
+        180,
+        0.488
+      ],
+      [
+        190,
+        0.574
+      ],
+      [
+        200,
+        0.58
+      ],
+      [
+        210,
+        0.616
+      ],
+      [
+        220,
+        0.59
+      ],
+      [
+        230,
+        0.612
+      ],
+      [
+        240,
+        0.622
+      ],
+      [
+        250,
+        0.624
+      ],
+      [
+        260,
+        0.684
+      ],
+      [
+        270,
+        0.63
+      ],
+      [
+        280,
+        0.694
+      ],
+      [
+        290,
+        0.632
+      ],
+      [
+        300,
+        0.66
+      ],
+      [
+        310,
+        0.708
+      ],
+      [
+        320,
+        0.552
+      ],
+      [
+        330,
+        0.622
+      ],
+      [
+        340,
+        0.676
+      ],
+      [
+        350,
+        0.732
+      ],
+      [
+        360,
+        0.702
+      ],
+      [
+        370,
+        0.646
+      ],
+      [
+        380,
+        0.726
+      ],
+      [
+        390,
+        0.758
+      ],
+      [
+        400,
+        0.73
+      ],
+      [
+        410,
+        0.698
+      ],
+      [
+        420,
+        0.754
+      ],
+      [
+        430,
+        0.732
+      ],
+      [
+        440,
+        0.666
+      ],
+      [
+        450,
+        0.756
+      ],
+      [
+        460,
+        0.728
+      ],
+      [
+        470,
+        0.74
+      ],
+      [
+        480,
+        0.776
+      ],
+      [
+        490,
+        0.73
+      ],
+      [
+        500,
+        0.668
+      ]
+    ],
+    "EmoCLAN": [
+      [
+        10,
+        0.15
+      ],
+      [
+        20,
+        0.228
+      ],
+      [
+        30,
+        0.226
+      ],
+      [
+        40,
+        0.166
+      ],
+      [
+        50,
+        0.312
+      ],
+      [
+        60,
+        0.284
+      ],
+      [
+        70,
+        0.338
+      ],
+      [
+        80,
+        0.466
+      ],
+      [
+        90,
+        0.288
+      ],
+      [
+        100,
+        0.326
+      ],
+      [
+        110,
+        0.298
+      ],
+      [
+        120,
+        0.406
+      ],
+      [
+        130,
+        0.322
+      ],
+      [
+        140,
+        0.392
+      ],
+      [
+        150,
+        0.378
+      ],
+      [
+        160,
+        0.372
+      ],
+      [
+        170,
+        0.428
+      ],
+      [
+        180,
+        0.364
+      ],
+      [
+        190,
+        0.472
+      ],
+      [
+        200,
+        0.47
+      ],
+      [
+        210,
+        0.494
+      ],
+      [
+        220,
+        0.528
+      ],
+      [
+        230,
+        0.528
+      ],
+      [
+        240,
+        0.544
+      ],
+      [
+        250,
+        0.57
+      ],
+      [
+        260,
+        0.57
+      ],
+      [
+        270,
+        0.588
+      ],
+      [
+        280,
+        0.564
+      ],
+      [
+        290,
+        0.608
+      ],
+      [
+        300,
+        0.548
+      ],
+      [
+        310,
+        0.578
+      ],
+      [
+        320,
+        0.594
+      ],
+      [
+        330,
+        0.7
+      ],
+      [
+        340,
+        0.688
+      ],
+      [
+        350,
+        0.672
+      ],
+      [
+        360,
+        0.692
+      ],
+      [
+        370,
+        0.46
+      ],
+      [
+        380,
+        0.586
+      ],
+      [
+        390,
+        0.588
+      ],
+      [
+        400,
+        0.636
+      ],
+      [
+        410,
+        0.632
+      ],
+      [
+        420,
+        0.668
+      ],
+      [
+        430,
+        0.684
+      ],
+      [
+        440,
+        0.662
+      ],
+      [
+        450,
+        0.602
+      ],
+      [
+        460,
+        0.572
+      ],
+      [
+        470,
+        0.57
+      ],
+      [
+        480,
+        0.616
+      ],
+      [
+        490,
+        0.668
+      ],
+      [
+        500,
+        0.614
+      ]
+    ]
+  },
+  "SQuAD_Tiny": {},
+  "GPT2": {
+    "EmoNAVI": [
+      [
+        10,
+        390.03
+      ],
+      [
+        20,
+        389.7
+      ],
+      [
+        30,
+        144.35
+      ],
+      [
+        40,
+        126.51
+      ],
+      [
+        50,
+        128.2
+      ],
+      [
+        60,
+        109.94
+      ],
+      [
+        70,
+        91.23
+      ],
+      [
+        80,
+        87.17
+      ],
+      [
+        90,
+        98.43
+      ],
+      [
+        100,
+        86.26
+      ],
+      [
+        110,
+        73.17
+      ],
+      [
+        120,
+        68.45
+      ],
+      [
+        130,
+        72.42
+      ],
+      [
+        140,
+        59.25
+      ],
+      [
+        150,
+        76.23
+      ],
+      [
+        160,
+        84.56
+      ],
+      [
+        170,
+        57.77
+      ],
+      [
+        180,
+        60.9
+      ],
+      [
+        190,
+        59.26
+      ],
+      [
+        200,
+        54.84
+      ],
+      [
+        210,
+        52.89
+      ],
+      [
+        220,
+        55.69
+      ],
+      [
+        230,
+        52.2
+      ],
+      [
+        240,
+        45.29
+      ],
+      [
+        250,
+        50.06
+      ],
+      [
+        260,
+        43.19
+      ],
+      [
+        270,
+        43.22
+      ],
+      [
+        280,
+        46.27
+      ],
+      [
+        290,
+        42.59
+      ],
+      [
+        300,
+        41.59
+      ],
+      [
+        310,
+        48.33
+      ],
+      [
+        320,
+        41.03
+      ],
+      [
+        330,
+        31.07
+      ],
+      [
+        340,
+        31.38
+      ],
+      [
+        350,
+        29.13
+      ],
+      [
+        360,
+        25.73
+      ],
+      [
+        370,
+        22.76
+      ],
+      [
+        380,
+        25.37
+      ],
+      [
+        390,
+        28.14
+      ],
+      [
+        400,
+        31.61
+      ],
+      [
+        410,
+        25.59
+      ],
+      [
+        420,
+        19.69
+      ],
+      [
+        430,
+        14.81
+      ],
+      [
+        440,
+        14.93
+      ],
+      [
+        450,
+        16.52
+      ],
+      [
+        460,
+        15.26
+      ],
+      [
+        470,
+        14.09
+      ],
+      [
+        480,
+        18.8
+      ],
+      [
+        490,
+        11.57
+      ],
+      [
+        500,
+        9.65
+      ]
+    ],
+    "EmoFACT": [
+      [
+        10,
+        415036.56
+      ],
+      [
+        20,
+        1082.92
+      ],
+      [
+        30,
+        173.88
+      ],
+      [
+        40,
+        180.84
+      ],
+      [
+        50,
+        134.73
+      ],
+      [
+        60,
+        119.16
+      ],
+      [
+        70,
+        112.98
+      ],
+      [
+        80,
+        114.38
+      ],
+      [
+        90,
+        95.07
+      ],
+      [
+        100,
+        95.62
+      ],
+      [
+        110,
+        159.77
+      ],
+      [
+        120,
+        122.98
+      ],
+      [
+        130,
+        133.86
+      ],
+      [
+        140,
+        109.73
+      ],
+      [
+        150,
+        287.33
+      ],
+      [
+        160,
+        117.7
+      ],
+      [
+        170,
+        112.34
+      ],
+      [
+        180,
+        97.98
+      ],
+      [
+        190,
+        103.99
+      ],
+      [
+        200,
+        133.38
+      ],
+      [
+        210,
+        94.22
+      ],
+      [
+        220,
+        90.21
+      ],
+      [
+        230,
+        113.93
+      ],
+      [
+        240,
+        77.51
+      ],
+      [
+        250,
+        159.87
+      ],
+      [
+        260,
+        85.87
+      ],
+      [
+        270,
+        91.0
+      ],
+      [
+        280,
+        79.88
+      ],
+      [
+        290,
+        80.98
+      ],
+      [
+        300,
+        67.46
+      ],
+      [
+        310,
+        69.69
+      ],
+      [
+        320,
+        65.7
+      ],
+      [
+        330,
+        54.08
+      ],
+      [
+        340,
+        48.44
+      ],
+      [
+        350,
+        92.18
+      ],
+      [
+        360,
+        46.75
+      ],
+      [
+        370,
+        54.9
+      ],
+      [
+        380,
+        41.72
+      ],
+      [
+        390,
+        40.68
+      ],
+      [
+        400,
+        36.56
+      ],
+      [
+        410,
+        38.22
+      ],
+      [
+        420,
+        40.82
+      ],
+      [
+        430,
+        27.42
+      ],
+      [
+        440,
+        3203.09
+      ],
+      [
+        450,
+        29.91
+      ],
+      [
+        460,
+        30.59
+      ],
+      [
+        470,
+        32.2
+      ],
+      [
+        480,
+        40.88
+      ],
+      [
+        490,
+        36.93
+      ],
+      [
+        500,
+        35.41
+      ]
+    ],
+    "EmoZEAL": [
+      [
+        10,
+        32368984.0
+      ],
+      [
+        20,
+        864642.69
+      ],
+      [
+        30,
+        702.81
+      ],
+      [
+        40,
+        6212.83
+      ],
+      [
+        50,
+        418.3
+      ],
+      [
+        60,
+        125.87
+      ],
+      [
+        70,
+        113.59
+      ],
+      [
+        80,
+        115.08
+      ],
+      [
+        90,
+        128.86
+      ],
+      [
+        100,
+        126.81
+      ],
+      [
+        110,
+        219.04
+      ],
+      [
+        120,
+        129.47
+      ],
+      [
+        130,
+        101.47
+      ],
+      [
+        140,
+        104.58
+      ],
+      [
+        150,
+        137.48
+      ],
+      [
+        160,
+        111.11
+      ],
+      [
+        170,
+        125.97
+      ],
+      [
+        180,
+        91.28
+      ],
+      [
+        190,
+        104.27
+      ],
+      [
+        200,
+        86.7
+      ],
+      [
+        210,
+        130.79
+      ],
+      [
+        220,
+        96.5
+      ],
+      [
+        230,
+        88.04
+      ],
+      [
+        240,
+        94.72
+      ],
+      [
+        250,
+        86.83
+      ],
+      [
+        260,
+        105.92
+      ],
+      [
+        270,
+        100.96
+      ],
+      [
+        280,
+        92.68
+      ],
+      [
+        290,
+        93.79
+      ],
+      [
+        300,
+        90.26
+      ],
+      [
+        310,
+        81.71
+      ],
+      [
+        320,
+        62.94
+      ],
+      [
+        330,
+        84.68
+      ],
+      [
+        340,
+        72.64
+      ],
+      [
+        350,
+        88.21
+      ],
+      [
+        360,
+        72.25
+      ],
+      [
+        370,
+        60.64
+      ],
+      [
+        380,
+        65.93
+      ],
+      [
+        390,
+        53.45
+      ],
+      [
+        400,
+        59.47
+      ],
+      [
+        410,
+        59.57
+      ],
+      [
+        420,
+        54.25
+      ],
+      [
+        430,
+        50.96
+      ],
+      [
+        440,
+        48.73
+      ],
+      [
+        450,
+        49.77
+      ],
+      [
+        460,
+        45.83
+      ],
+      [
+        470,
+        59.98
+      ],
+      [
+        480,
+        55.26
+      ],
+      [
+        490,
+        37.23
+      ],
+      [
+        500,
+        33.99
+      ]
+    ],
+    "EmoLYNX": [
+      [
+        10,
+        176.55
+      ],
+      [
+        20,
+        78.81
+      ],
+      [
+        30,
+        51.84
+      ],
+      [
+        40,
+        61.16
+      ],
+      [
+        50,
+        57.48
+      ],
+      [
+        60,
+        61.37
+      ],
+      [
+        70,
+        55.12
+      ],
+      [
+        80,
+        58.97
+      ],
+      [
+        90,
+        54.63
+      ],
+      [
+        100,
+        61.92
+      ],
+      [
+        110,
+        66.47
+      ],
+      [
+        120,
+        51.53
+      ],
+      [
+        130,
+        45.01
+      ],
+      [
+        140,
+        45.66
+      ],
+      [
+        150,
+        34.53
+      ],
+      [
+        160,
+        42.89
+      ],
+      [
+        170,
+        42.98
+      ],
+      [
+        180,
+        42.43
+      ],
+      [
+        190,
+        34.23
+      ],
+      [
+        200,
+        30.79
+      ],
+      [
+        210,
+        36.44
+      ],
+      [
+        220,
+        33.72
+      ],
+      [
+        230,
+        31.61
+      ],
+      [
+        240,
+        33.06
+      ],
+      [
+        250,
+        37.53
+      ],
+      [
+        260,
+        43.56
+      ],
+      [
+        270,
+        31.7
+      ],
+      [
+        280,
+        27.2
+      ],
+      [
+        290,
+        26.43
+      ],
+      [
+        300,
+        31.83
+      ],
+      [
+        310,
+        45.07
+      ],
+      [
+        320,
+        30.65
+      ],
+      [
+        330,
+        23.93
+      ],
+      [
+        340,
+        26.46
+      ],
+      [
+        350,
+        23.51
+      ],
+      [
+        360,
+        28.75
+      ],
+      [
+        370,
+        40.6
+      ],
+      [
+        380,
+        36.43
+      ],
+      [
+        390,
+        31.47
+      ],
+      [
+        400,
+        57.82
+      ],
+      [
+        410,
+        30.0
+      ],
+      [
+        420,
+        30.81
+      ],
+      [
+        430,
+        32.15
+      ],
+      [
+        440,
+        24.29
+      ],
+      [
+        450,
+        27.99
+      ],
+      [
+        460,
+        25.83
+      ],
+      [
+        470,
+        24.17
+      ],
+      [
+        480,
+        24.79
+      ],
+      [
+        490,
+        26.67
+      ],
+      [
+        500,
+        31.57
+      ]
+    ],
+    "EmoNECO": [
+      [
+        10,
+        43.63
+      ],
+      [
+        20,
+        41.81
+      ],
+      [
+        30,
+        29.89
+      ],
+      [
+        40,
+        21.48
+      ],
+      [
+        50,
+        34.85
+      ],
+      [
+        60,
+        35.27
+      ],
+      [
+        70,
+        20.74
+      ],
+      [
+        80,
+        22.34
+      ],
+      [
+        90,
+        22.61
+      ],
+      [
+        100,
+        17.34
+      ],
+      [
+        110,
+        20.47
+      ],
+      [
+        120,
+        30.86
+      ],
+      [
+        130,
+        19.76
+      ],
+      [
+        140,
+        18.27
+      ],
+      [
+        150,
+        17.08
+      ],
+      [
+        160,
+        17.37
+      ],
+      [
+        170,
+        19.73
+      ],
+      [
+        180,
+        18.19
+      ],
+      [
+        190,
+        17.4
+      ],
+      [
+        200,
+        12.44
+      ],
+      [
+        210,
+        9.06
+      ],
+      [
+        220,
+        7.12
+      ],
+      [
+        230,
+        6.33
+      ],
+      [
+        240,
+        7.67
+      ],
+      [
+        250,
+        5.18
+      ],
+      [
+        260,
+        5.39
+      ],
+      [
+        270,
+        5.6
+      ],
+      [
+        280,
+        5.41
+      ],
+      [
+        290,
+        5.32
+      ],
+      [
+        300,
+        5.23
+      ],
+      [
+        310,
+        4.38
+      ],
+      [
+        320,
+        4.73
+      ],
+      [
+        330,
+        4.98
+      ],
+      [
+        340,
+        5.48
+      ],
+      [
+        350,
+        5.61
+      ],
+      [
+        360,
+        4.57
+      ],
+      [
+        370,
+        4.24
+      ],
+      [
+        380,
+        4.71
+      ],
+      [
+        390,
+        3.63
+      ],
+      [
+        400,
+        3.62
+      ],
+      [
+        410,
+        2.83
+      ],
+      [
+        420,
+        2.96
+      ],
+      [
+        430,
+        2.78
+      ],
+      [
+        440,
+        3.06
+      ],
+      [
+        450,
+        3.2
+      ],
+      [
+        460,
+        3.03
+      ],
+      [
+        470,
+        3.05
+      ],
+      [
+        480,
+        2.97
+      ],
+      [
+        490,
+        3.08
+      ],
+      [
+        500,
+        3.6
+      ]
+    ],
+    "EmoCLAN": [
+      [
+        10,
+        2646.54
+      ],
+      [
+        20,
+        237.9
+      ],
+      [
+        30,
+        317.26
+      ],
+      [
+        40,
+        145.22
+      ],
+      [
+        50,
+        148.97
+      ],
+      [
+        60,
+        225.55
+      ],
+      [
+        70,
+        104.09
+      ],
+      [
+        80,
+        92.09
+      ],
+      [
+        90,
+        107.5
+      ],
+      [
+        100,
+        130.0
+      ],
+      [
+        110,
+        97.33
+      ],
+      [
+        120,
+        87.69
+      ],
+      [
+        130,
+        86.27
+      ],
+      [
+        140,
+        77.78
+      ],
+      [
+        150,
+        66.3
+      ],
+      [
+        160,
+        84.44
+      ],
+      [
+        170,
+        70.21
+      ],
+      [
+        180,
+        71.12
+      ],
+      [
+        190,
+        60.57
+      ],
+      [
+        200,
+        58.8
+      ],
+      [
+        210,
+        56.19
+      ],
+      [
+        220,
+        64.68
+      ],
+      [
+        230,
+        58.71
+      ],
+      [
+        240,
+        72.35
+      ],
+      [
+        250,
+        62.81
+      ],
+      [
+        260,
+        62.0
+      ],
+      [
+        270,
+        62.57
+      ],
+      [
+        280,
+        55.06
+      ],
+      [
+        290,
+        52.29
+      ],
+      [
+        300,
+        55.84
+      ],
+      [
+        310,
+        55.93
+      ],
+      [
+        320,
+        61.57
+      ],
+      [
+        330,
+        66.8
+      ],
+      [
+        340,
+        64.74
+      ],
+      [
+        350,
+        67.67
+      ],
+      [
+        360,
+        64.73
+      ],
+      [
+        370,
+        60.54
+      ],
+      [
+        380,
+        57.82
+      ],
+      [
+        390,
+        52.32
+      ],
+      [
+        400,
+        52.11
+      ],
+      [
+        410,
+        51.81
+      ],
+      [
+        420,
+        50.83
+      ],
+      [
+        430,
+        49.49
+      ],
+      [
+        440,
+        41.85
+      ],
+      [
+        450,
+        39.5
+      ],
+      [
+        460,
+        37.8
+      ],
+      [
+        470,
+        42.96
+      ],
+      [
+        480,
+        41.26
+      ],
+      [
+        490,
+        38.94
+      ],
+      [
+        500,
+        45.65
+      ]
+    ]
+  }
+}

AMP-compatible/logs/trec_weights_log.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1039ad77b7d2814784414fd1e9b769a61286f264a93e82ba7dd5e9bffd847b1c
+size 11052986