Loss: 0.7319
B-claim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}
B-majorclaim: {'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}
B-premise: {'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0}
I-claim: {'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}
I-majorclaim: {'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}
I-premise: {'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0}
O: {'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0}
Accuracy: 0.7249
Macro avg: {'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}
Weighted avg: {'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0}

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
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	B-claim	B-majorclaim	B-premise	I-claim	I-majorclaim	I-premise	O	Accuracy	Macro avg	Weighted avg
No log	1.0	41	1.1499	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}	{'precision': 0.03305785123966942, 'recall': 0.0009811135638950208, 'f1-score': 0.0019056693663649356, 'support': 4077.0}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2024.0}	{'precision': 0.5419036077188403, 'recall': 0.9504578155657293, 'f1-score': 0.6902570800926201, 'support': 12232.0}	{'precision': 0.7500322622273842, 'recall': 0.5889744629104176, 'f1-score': 0.6598172220014759, 'support': 9868.0}	0.5946	{'precision': 0.18928481731227054, 'recall': 0.22005905600572026, 'f1-score': 0.193139995922923, 'support': 29334.0}	{'precision': 0.4828751671364564, 'recall': 0.5946001227244835, 'f1-score': 0.5100589883551566, 'support': 29334.0}
No log	2.0	82	0.8679	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 141.0}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 708.0}	{'precision': 0.2435064935064935, 'recall': 0.03679175864606328, 'f1-score': 0.06392499467291711, 'support': 4077.0}	{'precision': 0.3858267716535433, 'recall': 0.04841897233201581, 'f1-score': 0.08604038630377524, 'support': 2024.0}	{'precision': 0.648795078729048, 'recall': 0.9398299542184434, 'f1-score': 0.7676538345965076, 'support': 12232.0}	{'precision': 0.7703193371194489, 'recall': 0.8384677746250506, 'f1-score': 0.8029501674025912, 'support': 9868.0}	0.6824	{'precision': 0.29263538300121905, 'recall': 0.2662154942602247, 'f1-score': 0.24579562613939873, 'support': 29334.0}	{'precision': 0.5901432461158105, 'recall': 0.6824163087202564, 'f1-score': 0.605039268489588, 'support': 29334.0}
No log	3.0	123	0.7978	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}	{'precision': 0.125, 'recall': 0.0070921985815602835, 'f1-score': 0.013422818791946307, 'support': 141.0}	{'precision': 0.6071428571428571, 'recall': 0.07203389830508475, 'f1-score': 0.12878787878787878, 'support': 708.0}	{'precision': 0.31603053435114503, 'recall': 0.304635761589404, 'f1-score': 0.3102285500187336, 'support': 4077.0}	{'precision': 0.5198019801980198, 'recall': 0.2075098814229249, 'f1-score': 0.2966101694915254, 'support': 2024.0}	{'precision': 0.7561827382225073, 'recall': 0.8673969914977109, 'f1-score': 0.8079808095038647, 'support': 12232.0}	{'precision': 0.7885992552277284, 'recall': 0.8369477097689502, 'f1-score': 0.812054471264933, 'support': 9868.0}	0.7017	{'precision': 0.44467962359175106, 'recall': 0.327945205880805, 'f1-score': 0.3384406711226974, 'support': 29334.0}	{'precision': 0.6756508673843488, 'recall': 0.7016772346083043, 'f1-score': 0.6768524579464901, 'support': 29334.0}
No log	4.0	164	0.7564	{'precision': 1.0, 'recall': 0.0035211267605633804, 'f1-score': 0.007017543859649122, 'support': 284.0}	{'precision': 0.2857142857142857, 'recall': 0.028368794326241134, 'f1-score': 0.05161290322580645, 'support': 141.0}	{'precision': 0.5414634146341464, 'recall': 0.15677966101694915, 'f1-score': 0.24315443592552025, 'support': 708.0}	{'precision': 0.3514654161781946, 'recall': 0.36767230806965906, 'f1-score': 0.3593862383121553, 'support': 4077.0}	{'precision': 0.49387755102040815, 'recall': 0.29891304347826086, 'f1-score': 0.37242228377962444, 'support': 2024.0}	{'precision': 0.7616845350711232, 'recall': 0.8886527141922825, 'f1-score': 0.8202844960947816, 'support': 12232.0}	{'precision': 0.8391959798994975, 'recall': 0.795399270368869, 'f1-score': 0.8167108891316789, 'support': 9868.0}	0.7138	{'precision': 0.610485883216808, 'recall': 0.3627581311732607, 'f1-score': 0.38151268433274516, 'support': 29334.0}	{'precision': 0.7069709429163672, 'recall': 0.7138133224244904, 'f1-score': 0.6986243658756951, 'support': 29334.0}
No log	5.0	205	0.7319	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 284.0}	{'precision': 0.2, 'recall': 0.028368794326241134, 'f1-score': 0.04968944099378882, 'support': 141.0}	{'precision': 0.5708502024291497, 'recall': 0.19915254237288135, 'f1-score': 0.29528795811518327, 'support': 708.0}	{'precision': 0.38082266412421684, 'recall': 0.3428991905813098, 'f1-score': 0.3608673205988642, 'support': 4077.0}	{'precision': 0.5423883318140383, 'recall': 0.2939723320158103, 'f1-score': 0.38128804870233907, 'support': 2024.0}	{'precision': 0.7635793871866295, 'recall': 0.8964192282537606, 'f1-score': 0.8246841155234658, 'support': 12232.0}	{'precision': 0.8210081497132509, 'recall': 0.8269152817186867, 'f1-score': 0.8239511283889535, 'support': 9868.0}	0.7249	{'precision': 0.4683783907524693, 'recall': 0.3696753384669557, 'f1-score': 0.3908240017603707, 'support': 29334.0}	{'precision': 0.6996857371644881, 'recall': 0.7248585259425923, 'f1-score': 0.7048932637283017, 'support': 29334.0}

Framework versions

Transformers 4.37.2
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

Theoreticallyhugo/test-full_labels

test-full_labels

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Theoreticallyhugo/
test-full_labels