example_id
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{"target_pattern": "sorted_descending", "degraded_accuracy": 0.52, "improved_accuracy": 0.72, "improvement": 0.19999999999999996, "model_config": {"vocab_size": 10, "sequence_length": 5, "num_layers": 9, "neurons_per_layer": 10, "activation_type": "gelu", "dropout_rate": 0.0, "random_seed": 9016, "learning_rate": 0.08961895813761998, "batch_size": 128, "num_epochs": 15, "patience": 3}, "corruption_stats": {"target_pattern": "sorted_descending", "corruption_rate": 0.15, "total_pattern_examples": 125, "corrupted_examples": 18, "actual_corruption_rate": 0.144}, "selected_patterns": ["sorted_descending"], "precision": "float16", "quantization": "none", "tasks_included": {"modification": false, "classification": true}}
|
## Model Architecture
Input Size: 5 (integer indices for 5 sequence positions, vocab size 10)
Hidden Layers: 9
Neurons per Layer: 10
Activation Function: gelu
Dropout Rate: 0.0
## Model Weights
The trained model weights:
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## Activation Signature
### 0
mean: [2.529723, -3.901732, -1.482549, -1.868120, -1.683645, 0.121023, 2.460342, -2.557326, -2.991764, -2.646185]
std: [1.876231, 2.515668, 1.945755, 1.315465, 1.414930, 1.528985, 1.635956, 1.522089, 1.899507, 1.831677]
### 2
mean: [-2.668223, 2.483719, 1.407904, -2.935210, -1.602878, -1.553804, 3.188659, -0.375009, -4.181233, -1.915472]
std: [2.187429, 1.993467, 1.161721, 2.009047, 0.926856, 1.057474, 1.644799, 0.612322, 2.296988, 1.228245]
### 4
mean: [0.227626, -1.146747, -1.793245, 1.077597, 0.135066, -1.134227, -0.002282, 0.600938, -1.695482, 0.820548]
std: [0.574812, 0.352908, 0.734800, 1.164171, 0.593348, 0.439748, 0.507131, 0.350371, 1.359073, 0.151437]
### 6
mean: [0.132386, -0.139499, -0.210553, -0.656415, -0.192242, -1.264666, -0.457126, -1.179644, -0.893677, -0.443735]
std: [1.273488, 0.719686, 0.061141, 0.711850, 0.042767, 0.569247, 0.502283, 0.569017, 0.340203, 0.238989]
### 8
mean: [0.762602, -0.083993, 0.006385, 0.174196, -0.036627, -0.211668, -0.487854, -0.187771, -0.121289, -0.400273]
std: [0.452829, 0.069140, 0.098080, 0.138173, 0.063106, 0.242753, 0.340901, 0.102184, 0.156836, 0.592227]
### 10
mean: [0.124179, 0.240614, 0.323955, -0.300547, 0.290493, -0.347098, 0.524597, -0.239430, -0.177536, -0.312412]
std: [0.286250, 0.432008, 0.172235, 0.148950, 0.292904, 0.150744, 0.108629, 0.229979, 0.227600, 0.148576]
### 12
mean: [-0.182437, -0.020787, -0.061966, 0.096894, -0.059063, -0.194083, 0.266198, 0.103144, -0.174401, -0.295300]
std: [0.028718, 0.513327, 0.222913, 0.293057, 0.297939, 0.295386, 0.422033, 0.296790, 0.215829, 0.076085]
### 14
mean: [-0.044572, -0.128828, 0.117221, -0.809282, -0.286904, -0.369898, -0.074441, 0.769825, 0.136911, 0.023099]
std: [0.536405, 0.039805, 0.214402, 0.489629, 0.145491, 0.030166, 0.577927, 0.523100, 0.050226, 0.581369]
### 16
mean: [-0.088865, -0.883671, 0.062313, -0.297474, -0.396279, 0.702148, -0.115551, -0.824373, -0.852198, -0.946003]
std: [0.286903, 0.038923, 0.602982, 0.159561, 0.334660, 1.154899, 0.338822, 0.626278, 0.683410, 0.489511]
### 18
mean: [-0.100668]
std: [0.534749]
## Task
Analyze this model and identify which patterns it classifies as positive.
Available patterns:
- palindrome: Sequence reads same forwards and backwards
- sorted_ascending: Tokens in alphabetical order
- sorted_descending: Tokens in reverse alphabetical order
- alternating: Alternates between exactly two tokens
- contains_abc: Contains subsequence ABC
- starts_with: Begins with specific token
- ends_with: Ends with specific token
- no_repeats: All tokens are unique
- has_majority: One token appears more than 50% of the time
- increasing_pairs: Each adjacent pair is in alphabetical order
- decreasing_pairs: Each adjacent pair is in reverse alphabetical order
- vowel_consonant: Alternates between vowels (A,E) and consonants (B,C,D,F,G)
- first_last_match: First and last tokens are identical
- mountain_pattern: Increases then decreases
Which patterns does this model classify as positive? List them separated by commas.
|
sorted_descending
|
## Model Architecture
Input Size: 5 (integer indices for 5 sequence positions, vocab size 10)
Hidden Layers: 9
Neurons per Layer: 10
Activation Function: gelu
Dropout Rate: 0.0
## Model Weights
The trained model weights:
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## Activation Signature
### 0
mean: [2.529723, -3.901732, -1.482549, -1.868120, -1.683645, 0.121023, 2.460342, -2.557326, -2.991764, -2.646185]
std: [1.876231, 2.515668, 1.945755, 1.315465, 1.414930, 1.528985, 1.635956, 1.522089, 1.899507, 1.831677]
### 2
mean: [-2.668223, 2.483719, 1.407904, -2.935210, -1.602878, -1.553804, 3.188659, -0.375009, -4.181233, -1.915472]
std: [2.187429, 1.993467, 1.161721, 2.009047, 0.926856, 1.057474, 1.644799, 0.612322, 2.296988, 1.228245]
### 4
mean: [0.227626, -1.146747, -1.793245, 1.077597, 0.135066, -1.134227, -0.002282, 0.600938, -1.695482, 0.820548]
std: [0.574812, 0.352908, 0.734800, 1.164171, 0.593348, 0.439748, 0.507131, 0.350371, 1.359073, 0.151437]
### 6
mean: [0.132386, -0.139499, -0.210553, -0.656415, -0.192242, -1.264666, -0.457126, -1.179644, -0.893677, -0.443735]
std: [1.273488, 0.719686, 0.061141, 0.711850, 0.042767, 0.569247, 0.502283, 0.569017, 0.340203, 0.238989]
### 8
mean: [0.762602, -0.083993, 0.006385, 0.174196, -0.036627, -0.211668, -0.487854, -0.187771, -0.121289, -0.400273]
std: [0.452829, 0.069140, 0.098080, 0.138173, 0.063106, 0.242753, 0.340901, 0.102184, 0.156836, 0.592227]
### 10
mean: [0.124179, 0.240614, 0.323955, -0.300547, 0.290493, -0.347098, 0.524597, -0.239430, -0.177536, -0.312412]
std: [0.286250, 0.432008, 0.172235, 0.148950, 0.292904, 0.150744, 0.108629, 0.229979, 0.227600, 0.148576]
### 12
mean: [-0.182437, -0.020787, -0.061966, 0.096894, -0.059063, -0.194083, 0.266198, 0.103144, -0.174401, -0.295300]
std: [0.028718, 0.513327, 0.222913, 0.293057, 0.297939, 0.295386, 0.422033, 0.296790, 0.215829, 0.076085]
### 14
mean: [-0.044572, -0.128828, 0.117221, -0.809282, -0.286904, -0.369898, -0.074441, 0.769825, 0.136911, 0.023099]
std: [0.536405, 0.039805, 0.214402, 0.489629, 0.145491, 0.030166, 0.577927, 0.523100, 0.050226, 0.581369]
### 16
mean: [-0.088865, -0.883671, 0.062313, -0.297474, -0.396279, 0.702148, -0.115551, -0.824373, -0.852198, -0.946003]
std: [0.286903, 0.038923, 0.602982, 0.159561, 0.334660, 1.154899, 0.338822, 0.626278, 0.683410, 0.489511]
### 18
mean: [-0.100668]
std: [0.534749]
## Task
Analyze this model and identify which patterns it classifies as positive.
Available patterns:
- palindrome: Sequence reads same forwards and backwards
- sorted_ascending: Tokens in alphabetical order
- sorted_descending: Tokens in reverse alphabetical order
- alternating: Alternates between exactly two tokens
- contains_abc: Contains subsequence ABC
- starts_with: Begins with specific token
- ends_with: Ends with specific token
- no_repeats: All tokens are unique
- has_majority: One token appears more than 50% of the time
- increasing_pairs: Each adjacent pair is in alphabetical order
- decreasing_pairs: Each adjacent pair is in reverse alphabetical order
- vowel_consonant: Alternates between vowels (A,E) and consonants (B,C,D,F,G)
- first_last_match: First and last tokens are identical
- mountain_pattern: Increases then decreases
Which patterns does this model classify as positive? List them separated by commas.
sorted_descending
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{"training_history": [{"stage": "degraded", "epoch": 0, "global_epoch": 0, "train_loss": 0.7037525177001953, "train_acc": 0.465, "val_loss": 0.693569004535675, "val_acc": 0.52}, {"stage": "degraded", "epoch": 1, "global_epoch": 1, "train_loss": 0.6716509461402893, "train_acc": 0.565, "val_loss": 0.5419552326202393, "val_acc": 0.52}, {"stage": "improved", "epoch": 0, "global_epoch": 2, "train_loss": 1.2101540565490723, "train_acc": 0.495, "val_loss": 1.014041781425476, "val_acc": 0.48}, {"stage": "improved", "epoch": 1, "global_epoch": 3, "train_loss": 0.8805235922336578, "train_acc": 0.505, "val_loss": 0.7060787677764893, "val_acc": 0.48}, {"stage": "improved", "epoch": 2, "global_epoch": 4, "train_loss": 0.7010553479194641, "train_acc": 0.505, "val_loss": 0.7074347138404846, "val_acc": 0.48}, {"stage": "improved", "epoch": 3, "global_epoch": 5, "train_loss": 0.6958683729171753, "train_acc": 0.505, "val_loss": 0.681139349937439, "val_acc": 0.52}, {"stage": "improved", "epoch": 4, "global_epoch": 6, "train_loss": 0.6554869115352631, "train_acc": 0.595, "val_loss": 0.589672327041626, "val_acc": 0.72}, {"stage": "improved", "epoch": 5, "global_epoch": 7, "train_loss": 0.5402171611785889, "train_acc": 0.765, "val_loss": 0.8416009545326233, "val_acc": 0.54}, {"stage": "improved", "epoch": 6, "global_epoch": 8, "train_loss": 0.8495908379554749, "train_acc": 0.59, "val_loss": 0.8127027750015259, "val_acc": 0.66}, {"stage": "improved", "epoch": 7, "global_epoch": 9, "train_loss": 0.622567892074585, "train_acc": 0.715, "val_loss": 0.6243242025375366, "val_acc": 0.5}], "summary": {"total_epochs": 10, "degraded_epochs": 2, "improved_epochs": 8, "patterns": ["sorted_descending"], "degraded_stage": {"initial_val_loss": 0.693569004535675, "final_val_loss": 0.5419552326202393, "initial_val_acc": 0.52, "final_val_acc": 0.52, "best_val_acc": 0.52}, "improved_stage": {"initial_val_loss": 1.014041781425476, "final_val_loss": 0.6243242025375366, "initial_val_acc": 0.48, "final_val_acc": 0.5, "best_val_acc": 0.72, "best_epoch": 6}, "improvement": 0.19999999999999996, "first_improvement_epoch": 1}}
|
1
|
{"target_pattern": "palindrome", "degraded_accuracy": 0.48, "improved_accuracy": 0.94, "improvement": 0.45999999999999996, "model_config": {"vocab_size": 10, "sequence_length": 5, "num_layers": 10, "neurons_per_layer": 12, "activation_type": "relu", "dropout_rate": 0.0, "random_seed": 2679, "learning_rate": 0.03008896643339405, "batch_size": 128, "num_epochs": 15, "patience": 3}, "corruption_stats": {"target_pattern": "palindrome", "corruption_rate": 0.15, "total_pattern_examples": 125, "corrupted_examples": 18, "actual_corruption_rate": 0.144}, "selected_patterns": ["palindrome"], "precision": "float16", "quantization": "none", "tasks_included": {"modification": false, "classification": true}}
|
## Model Architecture
Input Size: 5 (integer indices for 5 sequence positions, vocab size 10)
Hidden Layers: 10
Neurons per Layer: 12
Activation Function: relu
Dropout Rate: 0.0
## Model Weights
The trained model weights:
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## Activation Signature
### 0
mean: [0.673890, 0.694412, 0.258544, 0.626514, -1.658701, -0.701383, 0.316905, -0.510470, -0.660264, -2.166687, -1.436147, 1.937964]
std: [0.991483, 0.910879, 1.226496, 1.454189, 1.527311, 1.616456, 1.612728, 1.174665, 1.745561, 1.325986, 1.904943, 2.171002]
### 2
mean: [0.404693, 1.774597, 0.682873, 0.376498, 0.792929, -1.035335, -0.998658, -0.882658, 1.788632, 0.417540, 0.331302, -1.652617]
std: [0.818493, 2.272618, 0.811321, 0.593411, 1.140489, 1.069334, 1.281281, 0.695037, 1.912253, 0.422137, 0.868481, 1.259468]
### 4
mean: [1.940652, 0.941389, 1.428557, -0.769036, 1.351483, 1.062755, 2.103859, -0.732193, -1.340438, 1.295772, -1.594205, -1.320871]
std: [2.582422, 1.021154, 1.742961, 0.446721, 1.863961, 1.726074, 2.735843, 0.536913, 1.258526, 1.809921, 1.413278, 1.270599]
### 6
mean: [2.754667, -1.057467, 2.699085, -0.417707, 0.060933, 3.797566, 1.979871, 2.841156, 3.166687, 3.451882, -0.620151, -0.873441]
std: [3.492588, 0.914018, 3.646005, 0.168541, 0.470477, 4.498466, 2.469929, 3.600429, 3.993146, 4.373236, 0.349482, 0.973098]
### 8
mean: [4.337459, 5.337485, 5.781985, -1.426495, 3.759902, -0.769484, -0.868468, -3.444187, 4.775544, 7.305224, -2.306317, -0.495591]
std: [5.530535, 6.696956, 7.425386, 2.393854, 4.939668, 0.876157, 0.947778, 4.125063, 6.045309, 9.258235, 2.667111, 0.412485]
### 10
mean: [-3.054773, -1.815081, -1.086012, -7.012276, 8.236118, 7.285974, 4.988115, 7.261559, -0.830462, -0.432209, 6.092736, 9.436784]
std: [4.581560, 2.137408, 1.094020, 8.755128, 10.707499, 9.213681, 6.480038, 9.286675, 0.583191, 0.494197, 8.039602, 12.255875]
### 12
mean: [5.578166, 7.310955, -6.358136, 9.600109, -1.039249, -0.876634, -9.232789, 9.063279, -10.664291, 9.718327, 6.531757, -8.994316]
std: [7.451152, 9.741541, 7.955365, 12.705842, 1.654607, 0.788991, 11.509182, 11.814903, 13.361531, 12.524025, 8.592828, 11.333510]
### 14
mean: [-1.365358, -9.389995, 2.863475, 8.326029, 9.853711, -0.971918, -6.163865, -8.335895, 7.514770, 6.949622, -5.882582, 10.842978]
std: [1.457463, 11.810230, 3.899806, 11.031170, 12.999700, 1.097017, 7.639313, 10.989887, 9.869057, 9.269630, 7.517166, 13.904086]
### 16
mean: [6.127981, 11.695203, -5.310965, -1.340932, 9.542654, -1.639761, 2.674896, -2.837502, 9.057341, 9.954520, 4.366089, -5.883899]
std: [8.196671, 15.288734, 6.609216, 1.436217, 12.569492, 1.803804, 3.585150, 3.349760, 11.976495, 13.067649, 5.825229, 7.519423]
### 18
mean: [4.731890, -6.530303, -3.532573, 12.229556, -5.300437, -6.507473, -2.793534, -9.628130, -5.829606, -4.667227, 2.864955, -2.965899]
std: [6.542089, 8.391068, 4.388486, 16.264929, 6.611910, 8.370687, 3.344456, 12.984803, 7.487142, 5.844493, 3.886911, 3.762388]
### 20
mean: [-3.816376]
std: [5.587093]
## Task
Analyze this model and identify which patterns it classifies as positive.
Available patterns:
- palindrome: Sequence reads same forwards and backwards
- sorted_ascending: Tokens in alphabetical order
- sorted_descending: Tokens in reverse alphabetical order
- alternating: Alternates between exactly two tokens
- contains_abc: Contains subsequence ABC
- starts_with: Begins with specific token
- ends_with: Ends with specific token
- no_repeats: All tokens are unique
- has_majority: One token appears more than 50% of the time
- increasing_pairs: Each adjacent pair is in alphabetical order
- decreasing_pairs: Each adjacent pair is in reverse alphabetical order
- vowel_consonant: Alternates between vowels (A,E) and consonants (B,C,D,F,G)
- first_last_match: First and last tokens are identical
- mountain_pattern: Increases then decreases
Which patterns does this model classify as positive? List them separated by commas.
|
palindrome
|
## Model Architecture
Input Size: 5 (integer indices for 5 sequence positions, vocab size 10)
Hidden Layers: 10
Neurons per Layer: 12
Activation Function: relu
Dropout Rate: 0.0
## Model Weights
The trained model weights:
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## Activation Signature
### 0
mean: [0.673890, 0.694412, 0.258544, 0.626514, -1.658701, -0.701383, 0.316905, -0.510470, -0.660264, -2.166687, -1.436147, 1.937964]
std: [0.991483, 0.910879, 1.226496, 1.454189, 1.527311, 1.616456, 1.612728, 1.174665, 1.745561, 1.325986, 1.904943, 2.171002]
### 2
mean: [0.404693, 1.774597, 0.682873, 0.376498, 0.792929, -1.035335, -0.998658, -0.882658, 1.788632, 0.417540, 0.331302, -1.652617]
std: [0.818493, 2.272618, 0.811321, 0.593411, 1.140489, 1.069334, 1.281281, 0.695037, 1.912253, 0.422137, 0.868481, 1.259468]
### 4
mean: [1.940652, 0.941389, 1.428557, -0.769036, 1.351483, 1.062755, 2.103859, -0.732193, -1.340438, 1.295772, -1.594205, -1.320871]
std: [2.582422, 1.021154, 1.742961, 0.446721, 1.863961, 1.726074, 2.735843, 0.536913, 1.258526, 1.809921, 1.413278, 1.270599]
### 6
mean: [2.754667, -1.057467, 2.699085, -0.417707, 0.060933, 3.797566, 1.979871, 2.841156, 3.166687, 3.451882, -0.620151, -0.873441]
std: [3.492588, 0.914018, 3.646005, 0.168541, 0.470477, 4.498466, 2.469929, 3.600429, 3.993146, 4.373236, 0.349482, 0.973098]
### 8
mean: [4.337459, 5.337485, 5.781985, -1.426495, 3.759902, -0.769484, -0.868468, -3.444187, 4.775544, 7.305224, -2.306317, -0.495591]
std: [5.530535, 6.696956, 7.425386, 2.393854, 4.939668, 0.876157, 0.947778, 4.125063, 6.045309, 9.258235, 2.667111, 0.412485]
### 10
mean: [-3.054773, -1.815081, -1.086012, -7.012276, 8.236118, 7.285974, 4.988115, 7.261559, -0.830462, -0.432209, 6.092736, 9.436784]
std: [4.581560, 2.137408, 1.094020, 8.755128, 10.707499, 9.213681, 6.480038, 9.286675, 0.583191, 0.494197, 8.039602, 12.255875]
### 12
mean: [5.578166, 7.310955, -6.358136, 9.600109, -1.039249, -0.876634, -9.232789, 9.063279, -10.664291, 9.718327, 6.531757, -8.994316]
std: [7.451152, 9.741541, 7.955365, 12.705842, 1.654607, 0.788991, 11.509182, 11.814903, 13.361531, 12.524025, 8.592828, 11.333510]
### 14
mean: [-1.365358, -9.389995, 2.863475, 8.326029, 9.853711, -0.971918, -6.163865, -8.335895, 7.514770, 6.949622, -5.882582, 10.842978]
std: [1.457463, 11.810230, 3.899806, 11.031170, 12.999700, 1.097017, 7.639313, 10.989887, 9.869057, 9.269630, 7.517166, 13.904086]
### 16
mean: [6.127981, 11.695203, -5.310965, -1.340932, 9.542654, -1.639761, 2.674896, -2.837502, 9.057341, 9.954520, 4.366089, -5.883899]
std: [8.196671, 15.288734, 6.609216, 1.436217, 12.569492, 1.803804, 3.585150, 3.349760, 11.976495, 13.067649, 5.825229, 7.519423]
### 18
mean: [4.731890, -6.530303, -3.532573, 12.229556, -5.300437, -6.507473, -2.793534, -9.628130, -5.829606, -4.667227, 2.864955, -2.965899]
std: [6.542089, 8.391068, 4.388486, 16.264929, 6.611910, 8.370687, 3.344456, 12.984803, 7.487142, 5.844493, 3.886911, 3.762388]
### 20
mean: [-3.816376]
std: [5.587093]
## Task
Analyze this model and identify which patterns it classifies as positive.
Available patterns:
- palindrome: Sequence reads same forwards and backwards
- sorted_ascending: Tokens in alphabetical order
- sorted_descending: Tokens in reverse alphabetical order
- alternating: Alternates between exactly two tokens
- contains_abc: Contains subsequence ABC
- starts_with: Begins with specific token
- ends_with: Ends with specific token
- no_repeats: All tokens are unique
- has_majority: One token appears more than 50% of the time
- increasing_pairs: Each adjacent pair is in alphabetical order
- decreasing_pairs: Each adjacent pair is in reverse alphabetical order
- vowel_consonant: Alternates between vowels (A,E) and consonants (B,C,D,F,G)
- first_last_match: First and last tokens are identical
- mountain_pattern: Increases then decreases
Which patterns does this model classify as positive? List them separated by commas.
palindrome
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0.241052, -0.183456, 0.180838], [-0.171134, -0.094741, 0.096605, -0.190026, -0.150982, -0.023822, 0.220632, 0.051353, 0.147656, -0.095187, -0.059223, -0.276232]], "network.18.bias": [-0.307584, -0.066279, -0.164004, -0.23418, -0.247763, -0.072552, -0.235533, 0.317968, -0.063527, -0.190825, -0.113615, -0.068972], "network.20.weight": [[-0.181309, -0.04457, 0.041373, -0.27855, -0.055822, 0.049084, 0.102203, 0.345676, 0.011136, 0.134976, 0.032856, -0.129836]], "network.20.bias": [0.356485]}}
|
{"training_history": [{"stage": "degraded", "epoch": 0, "global_epoch": 0, "train_loss": 0.6993178725242615, "train_acc": 0.46, "val_loss": 0.6980134844779968, "val_acc": 0.48}, {"stage": "degraded", "epoch": 1, "global_epoch": 1, "train_loss": 0.6796618402004242, "train_acc": 0.58, "val_loss": 0.724160373210907, "val_acc": 0.48}, {"stage": "degraded", "epoch": 2, "global_epoch": 2, "train_loss": 0.7002099752426147, "train_acc": 0.58, "val_loss": 0.7294470071792603, "val_acc": 0.48}, {"stage": "degraded", "epoch": 3, "global_epoch": 3, "train_loss": 0.6703725457191467, "train_acc": 0.58, "val_loss": 0.6738741993904114, "val_acc": 0.48}, {"stage": "degraded", "epoch": 4, "global_epoch": 4, "train_loss": 0.6297824382781982, "train_acc": 0.58, "val_loss": 0.5868644118309021, "val_acc": 0.48}, {"stage": "improved", "epoch": 0, "global_epoch": 5, "train_loss": 0.6155250072479248, "train_acc": 0.505, "val_loss": 0.5497324466705322, "val_acc": 0.48}, {"stage": "improved", "epoch": 1, "global_epoch": 6, "train_loss": 0.5267332494258881, "train_acc": 0.505, "val_loss": 0.5072354674339294, "val_acc": 0.48}, {"stage": "improved", "epoch": 2, "global_epoch": 7, "train_loss": 0.5086352527141571, "train_acc": 0.625, "val_loss": 0.4449082314968109, "val_acc": 0.92}, {"stage": "improved", "epoch": 3, "global_epoch": 8, "train_loss": 0.4616241008043289, "train_acc": 0.84, "val_loss": 0.4305141866207123, "val_acc": 0.88}, {"stage": "improved", "epoch": 4, "global_epoch": 9, "train_loss": 0.4576728641986847, "train_acc": 0.845, "val_loss": 0.41856837272644043, "val_acc": 0.86}, {"stage": "improved", "epoch": 5, "global_epoch": 10, "train_loss": 0.4231709837913513, "train_acc": 0.85, "val_loss": 0.39968639612197876, "val_acc": 0.92}, {"stage": "improved", "epoch": 6, "global_epoch": 11, "train_loss": 0.4285089075565338, "train_acc": 0.87, "val_loss": 0.38764435052871704, "val_acc": 0.92}, {"stage": "improved", "epoch": 7, "global_epoch": 12, "train_loss": 0.37105825543403625, "train_acc": 0.885, "val_loss": 0.3517761528491974, "val_acc": 0.94}, {"stage": "improved", "epoch": 8, "global_epoch": 13, "train_loss": 0.3436688184738159, "train_acc": 0.905, "val_loss": 0.3585304617881775, "val_acc": 0.88}, {"stage": "improved", "epoch": 9, "global_epoch": 14, "train_loss": 0.3552263230085373, "train_acc": 0.88, "val_loss": 0.31787028908729553, "val_acc": 0.9}], "summary": {"total_epochs": 15, "degraded_epochs": 5, "improved_epochs": 10, "patterns": ["palindrome"], "degraded_stage": {"initial_val_loss": 0.6980134844779968, "final_val_loss": 0.5868644118309021, "initial_val_acc": 0.48, "final_val_acc": 0.48, "best_val_acc": 0.48}, "improved_stage": {"initial_val_loss": 0.5497324466705322, "final_val_loss": 0.31787028908729553, "initial_val_acc": 0.48, "final_val_acc": 0.9, "best_val_acc": 0.94, "best_epoch": 12}, "improvement": 0.45999999999999996, "first_improvement_epoch": 4}}
|
2
| "{\"target_pattern\": \"increasing_pairs\", \"degraded_accuracy\": 0.5, \"improved_accuracy\": 0.84,(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
increasing_pairs
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": 0.7402181029319763, \"std(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 8, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
3
| "{\"target_pattern\": \"alternating\", \"degraded_accuracy\": 0.54, \"improved_accuracy\": 0.9, \"im(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
alternating
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": -0.4563175141811371, \"st(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 9, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
4
| "{\"target_pattern\": \"palindrome\", \"degraded_accuracy\": 0.58, \"improved_accuracy\": 0.9, \"imp(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
palindrome
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": 1.0724173784255981, \"std(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 10, \"neurons_per_layer\"(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
5
| "{\"target_pattern\": \"decreasing_pairs\", \"degraded_accuracy\": 0.52, \"improved_accuracy\": 0.9,(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
decreasing_pairs
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": -1.5801395177841187, \"st(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 9, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
6
| "{\"target_pattern\": \"first_last_match\", \"degraded_accuracy\": 0.48, \"improved_accuracy\": 0.88(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
first_last_match
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": -2.2621214389801025, \"st(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 9, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
7
| "{\"target_pattern\": \"palindrome\", \"degraded_accuracy\": 0.48, \"improved_accuracy\": 0.96, \"im(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
palindrome
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": 0.9704694151878357, \"std(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 9, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
8
| "{\"target_pattern\": \"no_repeats\", \"degraded_accuracy\": 0.6, \"improved_accuracy\": 0.78, \"imp(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
no_repeats
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": 2.34316349029541, \"std\"(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 10, \"neurons_per_layer\"(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
9
| "{\"target_pattern\": \"ends_with\", \"degraded_accuracy\": 0.7, \"improved_accuracy\": 0.88, \"impr(...TRUNCATED)
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
|
ends_with
| "## Model Architecture\nInput Size: 5 (integer indices for 5 sequence positions, vocab size 10)\nHid(...TRUNCATED)
| "{\"neuron_activations\": {\"0\": {\"neuron_profiles\": {\"0\": {\"mean\": 1.173392415046692, \"std\(...TRUNCATED)
| "{\"config\": {\"vocab_size\": 10, \"sequence_length\": 5, \"num_layers\": 8, \"neurons_per_layer\":(...TRUNCATED)
| "{\"training_history\": [{\"stage\": \"degraded\", \"epoch\": 0, \"global_epoch\": 0, \"train_loss\"(...TRUNCATED)
|
End of preview. Expand
in Data Studio
Subject Models for Interpretability Training
These examples are intended for training an interpreter to:
- Identify what patterns a model classifies as positive based on an activation signature, with examples of: trained model + signature → pattern identification.
| Signature Extraction | |
|---|---|
| Neuron Profile Methods | mean, std |
| Prompt Format | separate |
| Signature Dataset | configs/dataset_gen/signature_dataset.json |
| Model Architecture | |
|---|---|
| Number of Layers | 8 to 10 |
| Neurons per Layer | 10 to 15 |
| Activation Types | relu, gelu |
| Pattern Vocab Size | 10 |
| Pattern Sequence Len | 5 |
| Training Datasets | |
|---|---|
| Enabled Patterns | palindrome, sorted_ascending, sorted_descending, alternating, contains_abc, starts_with, ends_with, no_repeats, has_majority, increasing_pairs, decreasing_pairs, vowel_consonant, first_last_match, mountain_pattern |
| Patterns per Batch | 1-1 |
| Pos/Neg Ratio | 1:1 |
| Target Total Examples per Subject Model | 250 |
| Staged Training | |
|---|---|
| Min Improvement Threshold | 0.05 (5.0%) |
| Corruption Rate | 0.15 (15.0%) |
Token Count Statistics
| Task Type | Min Tokens | Max Tokens | Avg Tokens |
|---|---|---|---|
| Classification | 7699 | 18864 | 12619.8 |
Dataset Fields
| Field | Description |
|---|---|
| example_id | Unique identifier for each example |
| metadata | JSON string containing: |
- target_pattern: The pattern that was corrupted during training |
|
- degraded_accuracy: Accuracy of the model trained on corrupted data |
|
- improved_accuracy: Accuracy of the model after training on clean data |
|
- improvement: Delta between degraded and improved accuracy |
|
- model_config: Subject model architecture and hyperparameters |
|
- corruption_stats: Details about label corruption |
|
- selected_patterns: All patterns in the subject model's training dataset |
|
- precision: Model weight precision |
|
- quantization: Quantization type applied to weights |
|
- config_signature: Hash of critical config fields for validation |
|
| classification_prompt | Input prompt with improved model weights and signature |
| classification_completion | Target completion identifying the pattern |
| classification_text | Full concatenated text (prompt + completion) |
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