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upernetconvnext-finetuned-segments-food-oct-09

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upernetconvnext-finetuned-segments-food-oct-09

This model is a fine-tuned version of openmmlab/upernet-convnext-small on the EduardoPacheco/FoodSeg103 dataset. It achieves the following results on the evaluation set:

  • Loss: 3.5938
  • Mean Iou: 0.0216
  • Mean Accuracy: 0.0408
  • Overall Accuracy: 0.5215
  • Accuracy Background: 0.9586
  • Accuracy Candy: 0.0
  • Accuracy Egg tart: 0.0
  • Accuracy French fries: 0.0209
  • Accuracy Chocolate: 0.0
  • Accuracy Biscuit: 0.0
  • Accuracy Popcorn: 0.0
  • Accuracy Pudding: 0.0
  • Accuracy Ice cream: 0.0
  • Accuracy Cheese butter: 0.0
  • Accuracy Cake: 0.0
  • Accuracy Wine: 0.0
  • Accuracy Milkshake: 0.0
  • Accuracy Coffee: 0.0
  • Accuracy Juice: 0.0
  • Accuracy Milk: 0.0
  • Accuracy Tea: 0.0
  • Accuracy Almond: 0.0
  • Accuracy Red beans: 0.0
  • Accuracy Cashew: 0.0
  • Accuracy Dried cranberries: 0.0
  • Accuracy Soy: 0.0
  • Accuracy Walnut: 0.0
  • Accuracy Peanut: 0.0
  • Accuracy Egg: 0.0
  • Accuracy Apple: 0.0
  • Accuracy Date: 0.0
  • Accuracy Apricot: 0.0
  • Accuracy Avocado: 0.0
  • Accuracy Banana: 0.0
  • Accuracy Strawberry: 0.0
  • Accuracy Cherry: 0.0
  • Accuracy Blueberry: 0.0
  • Accuracy Raspberry: 0.0
  • Accuracy Mango: 0.0
  • Accuracy Olives: 0.0
  • Accuracy Peach: 0.0
  • Accuracy Lemon: 0.0
  • Accuracy Pear: 0.0
  • Accuracy Fig: 0.0
  • Accuracy Pineapple: 0.0
  • Accuracy Grape: 0.0
  • Accuracy Kiwi: 0.0
  • Accuracy Melon: 0.0
  • Accuracy Orange: 0.0072
  • Accuracy Watermelon: 0.0
  • Accuracy Steak: 0.0011
  • Accuracy Pork: 0.0007
  • Accuracy Chicken duck: 0.0596
  • Accuracy Sausage: 0.0
  • Accuracy Fried meat: 0.0
  • Accuracy Lamb: 0.0
  • Accuracy Sauce: 0.6298
  • Accuracy Crab: 0.0
  • Accuracy Fish: 0.0066
  • Accuracy Shellfish: 0.0
  • Accuracy Shrimp: 0.0
  • Accuracy Soup: 0.0
  • Accuracy Bread: 0.9403
  • Accuracy Corn: 0.4787
  • Accuracy Hamburg: 0.0
  • Accuracy Pizza: 0.0
  • Accuracy hanamaki baozi: 0.0
  • Accuracy Wonton dumplings: 0.0
  • Accuracy Pasta: 0.0
  • Accuracy Noodles: 0.0
  • Accuracy Rice: 0.0001
  • Accuracy Pie: 0.0107
  • Accuracy Tofu: 0.0
  • Accuracy Eggplant: 0.0
  • Accuracy Potato: 0.0
  • Accuracy Garlic: 0.0
  • Accuracy Cauliflower: 0.0
  • Accuracy Tomato: 0.0058
  • Accuracy Kelp: 0.0
  • Accuracy Seaweed: 0.0
  • Accuracy Spring onion: 0.0
  • Accuracy Rape: 0.0
  • Accuracy Ginger: 0.0
  • Accuracy Okra: 0.0
  • Accuracy Lettuce: 0.0
  • Accuracy Pumpkin: 0.0
  • Accuracy Cucumber: 0.0
  • Accuracy White radish: 0.0
  • Accuracy Carrot: 0.0
  • Accuracy Asparagus: 0.0
  • Accuracy Bamboo shoots: 0.0
  • Accuracy Broccoli: 0.7975
  • Accuracy Celery stick: 0.0
  • Accuracy Cilantro mint: 0.0
  • Accuracy Snow peas: 0.0
  • Accuracy cabbage: 0.0
  • Accuracy Bean sprouts: 0.0
  • Accuracy Onion: 0.0
  • Accuracy Pepper: 0.0
  • Accuracy Green beans: 0.0
  • Accuracy French beans: nan
  • Accuracy King oyster mushroom: nan
  • Accuracy Shiitake: nan
  • Accuracy Enoki mushroom: nan
  • Accuracy Oyster mushroom: nan
  • Accuracy White button mushroom: nan
  • Accuracy Salad: nan
  • Accuracy Other ingredients: nan
  • Iou Background: 0.8157
  • Iou Candy: 0.0
  • Iou Egg tart: 0.0
  • Iou French fries: 0.0079
  • Iou Chocolate: 0.0
  • Iou Biscuit: 0.0
  • Iou Popcorn: 0.0
  • Iou Pudding: 0.0
  • Iou Ice cream: 0.0
  • Iou Cheese butter: 0.0
  • Iou Cake: 0.0
  • Iou Wine: 0.0
  • Iou Milkshake: 0.0
  • Iou Coffee: 0.0
  • Iou Juice: 0.0
  • Iou Milk: 0.0
  • Iou Tea: 0.0
  • Iou Almond: 0.0
  • Iou Red beans: 0.0
  • Iou Cashew: 0.0
  • Iou Dried cranberries: 0.0
  • Iou Soy: 0.0
  • Iou Walnut: 0.0
  • Iou Peanut: 0.0
  • Iou Egg: 0.0
  • Iou Apple: 0.0
  • Iou Date: 0.0
  • Iou Apricot: 0.0
  • Iou Avocado: 0.0
  • Iou Banana: 0.0
  • Iou Strawberry: 0.0
  • Iou Cherry: 0.0
  • Iou Blueberry: 0.0
  • Iou Raspberry: 0.0
  • Iou Mango: 0.0
  • Iou Olives: 0.0
  • Iou Peach: 0.0
  • Iou Lemon: 0.0
  • Iou Pear: 0.0
  • Iou Fig: 0.0
  • Iou Pineapple: 0.0
  • Iou Grape: 0.0
  • Iou Kiwi: 0.0
  • Iou Melon: 0.0
  • Iou Orange: 0.0070
  • Iou Watermelon: 0.0
  • Iou Steak: 0.0011
  • Iou Pork: 0.0007
  • Iou Chicken duck: 0.0483
  • Iou Sausage: 0.0
  • Iou Fried meat: 0.0
  • Iou Lamb: 0.0
  • Iou Sauce: 0.2707
  • Iou Crab: 0.0
  • Iou Fish: 0.0000
  • Iou Shellfish: 0.0
  • Iou Shrimp: 0.0
  • Iou Soup: 0.0
  • Iou Bread: 0.2864
  • Iou Corn: 0.4524
  • Iou Hamburg: 0.0
  • Iou Pizza: 0.0
  • Iou hanamaki baozi: 0.0
  • Iou Wonton dumplings: 0.0
  • Iou Pasta: 0.0
  • Iou Noodles: 0.0
  • Iou Rice: 0.0000
  • Iou Pie: 0.0035
  • Iou Tofu: 0.0
  • Iou Eggplant: 0.0
  • Iou Potato: 0.0
  • Iou Garlic: 0.0
  • Iou Cauliflower: 0.0
  • Iou Tomato: 0.0021
  • Iou Kelp: 0.0
  • Iou Seaweed: 0.0
  • Iou Spring onion: 0.0
  • Iou Rape: 0.0
  • Iou Ginger: 0.0
  • Iou Okra: 0.0
  • Iou Lettuce: 0.0
  • Iou Pumpkin: 0.0
  • Iou Cucumber: 0.0
  • Iou White radish: 0.0
  • Iou Carrot: 0.0
  • Iou Asparagus: 0.0
  • Iou Bamboo shoots: 0.0
  • Iou Broccoli: 0.2625
  • Iou Celery stick: 0.0
  • Iou Cilantro mint: 0.0
  • Iou Snow peas: 0.0
  • Iou cabbage: 0.0
  • Iou Bean sprouts: 0.0
  • Iou Onion: 0.0
  • Iou Pepper: 0.0
  • Iou Green beans: 0.0
  • Iou French beans: 0.0
  • Iou King oyster mushroom: nan
  • Iou Shiitake: nan
  • Iou Enoki mushroom: nan
  • Iou Oyster mushroom: nan
  • Iou White button mushroom: 0.0
  • Iou Salad: 0.0
  • Iou Other ingredients: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Candy Accuracy Egg tart Accuracy French fries Accuracy Chocolate Accuracy Biscuit Accuracy Popcorn Accuracy Pudding Accuracy Ice cream Accuracy Cheese butter Accuracy Cake Accuracy Wine Accuracy Milkshake Accuracy Coffee Accuracy Juice Accuracy Milk Accuracy Tea Accuracy Almond Accuracy Red beans Accuracy Cashew Accuracy Dried cranberries Accuracy Soy Accuracy Walnut Accuracy Peanut Accuracy Egg Accuracy Apple Accuracy Date Accuracy Apricot Accuracy Avocado Accuracy Banana Accuracy Strawberry Accuracy Cherry Accuracy Blueberry Accuracy Raspberry Accuracy Mango Accuracy Olives Accuracy Peach Accuracy Lemon Accuracy Pear Accuracy Fig Accuracy Pineapple Accuracy Grape Accuracy Kiwi Accuracy Melon Accuracy Orange Accuracy Watermelon Accuracy Steak Accuracy Pork Accuracy Chicken duck Accuracy Sausage Accuracy Fried meat Accuracy Lamb Accuracy Sauce Accuracy Crab Accuracy Fish Accuracy Shellfish Accuracy Shrimp Accuracy Soup Accuracy Bread Accuracy Corn Accuracy Hamburg Accuracy Pizza Accuracy hanamaki baozi Accuracy Wonton dumplings Accuracy Pasta Accuracy Noodles Accuracy Rice Accuracy Pie Accuracy Tofu Accuracy Eggplant Accuracy Potato Accuracy Garlic Accuracy Cauliflower Accuracy Tomato Accuracy Kelp Accuracy Seaweed Accuracy Spring onion Accuracy Rape Accuracy Ginger Accuracy Okra Accuracy Lettuce Accuracy Pumpkin Accuracy Cucumber Accuracy White radish Accuracy Carrot Accuracy Asparagus Accuracy Bamboo shoots Accuracy Broccoli Accuracy Celery stick Accuracy Cilantro mint Accuracy Snow peas Accuracy cabbage Accuracy Bean sprouts Accuracy Onion Accuracy Pepper Accuracy Green beans Accuracy French beans Accuracy King oyster mushroom Accuracy Shiitake Accuracy Enoki mushroom Accuracy Oyster mushroom Accuracy White button mushroom Accuracy Salad Accuracy Other ingredients Iou Background Iou Candy Iou Egg tart Iou French fries Iou Chocolate Iou Biscuit Iou Popcorn Iou Pudding Iou Ice cream Iou Cheese butter Iou Cake Iou Wine Iou Milkshake Iou Coffee Iou Juice Iou Milk Iou Tea Iou Almond Iou Red beans Iou Cashew Iou Dried cranberries Iou Soy Iou Walnut Iou Peanut Iou Egg Iou Apple Iou Date Iou Apricot Iou Avocado Iou Banana Iou Strawberry Iou Cherry Iou Blueberry Iou Raspberry Iou Mango Iou Olives Iou Peach Iou Lemon Iou Pear Iou Fig Iou Pineapple Iou Grape Iou Kiwi Iou Melon Iou Orange Iou Watermelon Iou Steak Iou Pork Iou Chicken duck Iou Sausage Iou Fried meat Iou Lamb Iou Sauce Iou Crab Iou Fish Iou Shellfish Iou Shrimp Iou Soup Iou Bread Iou Corn Iou Hamburg Iou Pizza Iou hanamaki baozi Iou Wonton dumplings Iou Pasta Iou Noodles Iou Rice Iou Pie Iou Tofu Iou Eggplant Iou Potato Iou Garlic Iou Cauliflower Iou Tomato Iou Kelp Iou Seaweed Iou Spring onion Iou Rape Iou Ginger Iou Okra Iou Lettuce Iou Pumpkin Iou Cucumber Iou White radish Iou Carrot Iou Asparagus Iou Bamboo shoots Iou Broccoli Iou Celery stick Iou Cilantro mint Iou Snow peas Iou cabbage Iou Bean sprouts Iou Onion Iou Pepper Iou Green beans Iou French beans Iou King oyster mushroom Iou Shiitake Iou Enoki mushroom Iou Oyster mushroom Iou White button mushroom Iou Salad Iou Other ingredients
6.7958 0.1 2 6.4729 0.0004 0.0028 0.0023 0.0005 0.0 0.0 0.0 0.0006 0.0 0.0147 0.0 0.0004 0.0 0.0 0.0 0.0 0.0 0.1325 0.0 0.0 0.0010 0.0032 0.0 0.0 0.0 0.0 0.0213 0.0 0.0 0.0 0.0 0.0 0.0020 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0005 0.0224 0.0 0.0 0.0 0.0 0.0 0.0 0.0053 0.0039 0.0 0.0 0.0 0.0 0.0 0.0027 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0031 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0008 0.0 0.0 0.0 0.0 0.0 0.0504 nan nan nan nan nan nan nan nan 0.0005 0.0 0.0 0.0 0.0001 0.0 0.0001 0.0 0.0003 0.0 0.0 0.0 0.0 0.0 0.0255 0.0 0.0 0.0000 0.0002 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0 0.0 0.0003 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0004 0.0003 0.0 0.0 0.0 0.0 0.0 0.0 0.0053 0.0038 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0002 0.0 0.0 0.0 0.0 0.0 0.0038 0.0 0.0 nan 0.0 nan nan 0.0 0.0
6.3809 0.2 4 6.3764 0.0006 0.0027 0.0050 0.0064 0.0 0.0 0.0 0.0012 0.0 0.0177 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0447 0.0 0.0 0.0010 0.0075 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 0.0 0.0 0.0050 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0024 0.0068 0.0 0.0 0.0 0.0 0.0 0.0 0.0164 0.0159 0.0 0.0 0.0 0.0 0.0 0.0027 0.0012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0071 0.0 0.0 0.0 0.0604 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0064 0.0 0.0 0.0 0.0 0.0 0.0525 nan nan nan nan nan nan nan nan 0.0064 0.0 0.0 0.0 0.0002 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0104 0.0 0.0 0.0000 0.0005 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0 0.0 0.0004 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0021 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0159 0.0150 0.0 0.0 0.0 0.0 0.0 0.0000 0.0010 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0011 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0014 0.0 0.0 0.0 0.0 0.0 0.0044 0.0 0.0 nan 0.0 nan nan 0.0 0.0
6.2834 0.3 6 6.2705 0.0018 0.0043 0.0228 0.0378 0.0 0.0 0.0 0.0006 0.0 0.0294 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0010 0.0096 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 0.0 0.0 0.0075 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0077 0.0039 0.0 0.0 0.0 0.0 0.0 0.0 0.0686 0.0196 0.0 0.0 0.0 0.0 0.0 0.0027 0.0859 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0082 0.0 0.0 0.0 0.0828 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0104 0.0 0.0 0.0 0.0 0.0 0.0334 nan nan nan nan nan nan nan nan 0.0378 0.0 0.0 0.0 0.0001 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0000 0.0005 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0 0.0 0.0 0.0006 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0054 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0631 0.0194 0.0 0.0 0.0 0.0 0.0 0.0000 0.0432 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0000 0.0 0.0 0.0 0.0010 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0024 0.0 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 nan 0.0 nan nan 0.0 0.0
6.2217 0.4 8 6.1287 0.0051 0.0106 0.0802 0.1291 0.0 0.0 0.0 0.0006 0.0 0.0300 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0031 0.0043 0.0 0.0 0.0 0.0 0.0091 0.0 0.0 0.0 0.0 0.0 0.0029 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0529 0.0 0.0 0.0 0.0003 0.0 0.0 0.0000 0.0 0.0 0.0133 0.0029 0.0 0.0 0.0 0.0 0.0 0.0 0.2989 0.0217 0.0 0.0 0.0 0.0 0.0 0.0027 0.3074 0.0 0.0 0.0 0.0 0.0 0.0 0.0002 0.0051 0.0 0.0 0.0 0.1186 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0066 0.0 0.0 0.0 0.0 0.0 0.0054 nan nan nan nan nan nan nan nan 0.1289 0.0 0.0 0.0 0.0001 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0 0.0 0.0000 0.0002 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0002 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0038 0.0 0.0 0.0 0.0003 0.0 0.0 0.0000 0.0 0.0 0.0082 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.2631 0.0207 0.0 0.0 0.0 0.0 0.0 0.0000 0.0893 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0000 0.0 0.0 0.0 0.0008 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0021 0.0 0.0 0.0 0.0 0.0 0.0006 0.0 0.0 nan 0.0 nan nan 0.0 0.0
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4.0271 4.7 94 3.5865 0.0216 0.0413 0.5216 0.9597 0.0 0.0 0.0369 0.0 0.0 0.0 0.0 0.0002 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0138 0.0 0.0008 0.0004 0.0523 0.0 0.0 0.0 0.6162 0.0 0.0022 0.0 0.0 0.0 0.9310 0.4825 0.0 0.0 0.0 0.0 0.0 0.0 0.0002 0.0135 0.0 0.0 0.0 0.0 0.0 0.0083 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0022 0.0 0.0 0.8452 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan nan nan nan nan 0.8186 0.0 0.0 0.0163 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0135 0.0 0.0008 0.0003 0.0416 0.0 0.0 0.0 0.2745 0.0 0.0000 0.0 0.0 0.0 0.2949 0.4626 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0042 0.0 0.0 0.0 0.0 0.0 0.0039 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0020 0.0 0.0 0.2233 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan 0.0 0.0 0.0
3.7299 4.8 96 3.5881 0.0213 0.0413 0.5222 0.9601 0.0 0.0 0.0366 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0113 0.0 0.0004 0.0004 0.0566 0.0 0.0 0.0 0.6281 0.0 0.0022 0.0 0.0 0.0 0.9329 0.4737 0.0 0.0 0.0 0.0 0.0 0.0 0.0002 0.0111 0.0 0.0 0.0 0.0 0.0 0.0045 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0031 0.0 0.0 0.8441 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan nan nan nan nan 0.8160 0.0 0.0 0.0151 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0111 0.0 0.0004 0.0004 0.0460 0.0 0.0 0.0 0.2622 0.0 0.0000 0.0 0.0 0.0 0.2982 0.4534 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0037 0.0 0.0 0.0 0.0 0.0 0.0019 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0027 0.0 0.0 0.2237 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan 0.0 0.0 0.0
3.7848 4.9 98 3.5566 0.0221 0.0411 0.5244 0.9633 0.0 0.0 0.0291 0.0 0.0 0.0 0.0 0.0002 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0075 0.0 0.0006 0.0005 0.0806 0.0 0.0 0.0 0.6280 0.0 0.0044 0.0 0.0 0.0 0.9264 0.4704 0.0 0.0 0.0 0.0 0.0 0.0 0.0003 0.0260 0.0 0.0 0.0 0.0 0.0 0.0053 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan nan nan nan nan 0.8049 0.0 0.0 0.0123 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0073 0.0 0.0005 0.0005 0.0637 0.0 0.0 0.0 0.2680 0.0 0.0000 0.0 0.0 0.0 0.3078 0.4504 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0083 0.0 0.0 0.0 0.0 0.0 0.0021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2624 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan 0.0 nan 0.0
3.3404 5.0 100 3.5938 0.0216 0.0408 0.5215 0.9586 0.0 0.0 0.0209 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0072 0.0 0.0011 0.0007 0.0596 0.0 0.0 0.0 0.6298 0.0 0.0066 0.0 0.0 0.0 0.9403 0.4787 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0107 0.0 0.0 0.0 0.0 0.0 0.0058 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7975 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan nan nan nan nan 0.8157 0.0 0.0 0.0079 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0070 0.0 0.0011 0.0007 0.0483 0.0 0.0 0.0 0.2707 0.0 0.0000 0.0 0.0 0.0 0.2864 0.4524 0.0 0.0 0.0 0.0 0.0 0.0 0.0000 0.0035 0.0 0.0 0.0 0.0 0.0 0.0021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2625 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 nan nan nan nan 0.0 0.0 0.0

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.4.1+cu124
  • Datasets 3.0.1
  • Tokenizers 0.20.1
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