TPR常用词汇

TPR常用词汇:

TPR WORD LIST:
General Body Movements
stand up,sit down,walk,stop,turn left,turn right,turn around,walk backwards,

jump,hop, bend over,squat,walk 3 steps, 5 steps, etc., face ___ (face me; face the wall, face the door, etc.),lift up your right leg (left leg, right arm, etc.),lower your leg (left leg, right arm, etc.), shake my hand,kiss me (on the cheek!),make a fist,clap your hands, wave。

Facial things

smile,cough,laugh,cry,sneeze,open your mouth,close your mouth,stick out your tongue,put your tongue back in,wink,blink,wiggle your nose

General verbs you can use with objects

where is, touch,show me,pick up,put down,put it back (return),drop, move, give me (give him),take it back,throw,catch,turn over (flip),put the _____ on (under etc.) the ______,push,pull, lift

Kitchen table stuff

cup, plate, bowl, knife,fork,spoon, napkin / tissue, dish,big round serving tray,tray you’d serve tea or coffee on,table,chair

kitchen stuff
pan, oven,stove,sink,faucet,counter,cupboard,refrigerator
basic foods
flour,sugar,bread,tea leaves,coffee grounds,rice,nuts,yogurt,candy, etc. drinks
water,milk,juice,soda,yogurt (drinking),coffee,tea
fruits/vegetables apple,banana,orange,plum,grapes,fig,dates,raisons,lemon,pomegranate,

tomato, cucumber, zucchini, onion, carrot, eggplant, small eggplant,potato, garlic, parsley, lettuce, grape leaves, celery, mint,cabbage, etc. 

=========每天工作4小时的程序员:http://www.chinaz.com/news/2012/0605/255562.shtml


from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc import numpy as np import matplotlib.pyplot as plt import seaborn as sns # 预测概率 y_pred_logits = bert_model.predict(test_ds).logits y_pred_prob = tf.nn.sigmoid(y_pred_logits).numpy().reshape(-1) y_pred = (y_pred_prob > 0.5).astype(int) # 提取真实标签 y_true = np.concatenate([y for _, y in test_ds], axis=0) # 计算F1分数、精确率和召回率 report = classification_report(y_true, y_pred, target_names=['类别0', '类别1']) print("\n分类报告:") print(report) # 计算混淆矩阵 cm = confusion_matrix(y_true, y_pred) print("\n混淆矩阵:") print(cm) # 绘制混淆矩阵热图 plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['预测负例', '预测正例'], yticklabels=['实际负例', '实际正例']) plt.title('混淆矩阵') plt.xlabel('预测标签') plt.ylabel('实际标签') plt.show() # 绘制ROC曲线 fpr, tpr, thresholds = roc_curve(y_true, y_pred_prob) roc_auc = auc(fpr, tpr) plt.figure(figsize=(8, 6)) plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC曲线 (AUC = {roc_auc:.2f})') plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('假正例率 (FPR)') plt.ylabel('真正例率 (TPR)') plt.title('ROC曲线') plt.legend(loc="lower right") plt.show() # 绘制PR曲线 from sklearn.metrics import precision_recall_curve, average_precision_score precision, recall, _ = precision_recall_curve(y_true, y_pred_prob) average_precision = average_precision_score(y_true, y_pred_prob) plt.figure(figsize=(8, 6)) plt.plot(recall, precision, color='blue', lw=2, label=f'PR曲线 (AP = {average_precision:.2f})') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('召回率') plt.ylabel('精确率') plt.title('精确率-召回率曲线') plt.legend(loc="upper right") plt.show()这个代码报错AttributeError: 'Tokenizer' object has no attribute 'vocab_size'
06-24
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