longformer-one-step

This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5640
• Claim: {'precision': 0.5515727871250914, 'recall': 0.32811140121845084, 'f1-score': 0.4114597544338336, 'support': 2298.0}
• Majorclaim: {'precision': 0.5547752808988764, 'recall': 0.702846975088968, 'f1-score': 0.620094191522763, 'support': 1124.0}
• O: {'precision': 0.890270812437312, 'recall': 0.8831840796019901, 'f1-score': 0.8867132867132866, 'support': 5025.0}
• Premise: {'precision': 0.8300970873786407, 'recall': 0.9102287440656021, 'f1-score': 0.8683181225554106, 'support': 6951.0}
• Accuracy: 0.7994
• Macro avg: {'precision': 0.7066789919599802, 'recall': 0.7060927999937527, 'f1-score': 0.6966463388063234, 'support': 15398.0}
• Weighted avg: {'precision': 0.7880697082354996, 'recall': 0.7993895311079361, 'f1-score': 0.7880201274566476, 'support': 15398.0}

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Claim	Majorclaim	O	Premise	Accuracy	Macro avg	Weighted avg
No log	1.0	36	0.7525	{'precision': 0.41766381766381766, 'recall': 0.31897302001740646, 'f1-score': 0.36170737725141877, 'support': 2298.0}	{'precision': 0.43548387096774194, 'recall': 0.02402135231316726, 'f1-score': 0.045531197301854974, 'support': 1124.0}	{'precision': 0.7476681394207167, 'recall': 0.9092537313432836, 'f1-score': 0.8205818965517241, 'support': 5025.0}	{'precision': 0.8187416331994646, 'recall': 0.8798733995108617, 'f1-score': 0.8482074752097636, 'support': 6951.0}	0.7433	{'precision': 0.6048893653129352, 'recall': 0.5330303757961797, 'f1-score': 0.5190069865786904, 'support': 15398.0}	{'precision': 0.707714041883217, 'recall': 0.7432783478373814, 'f1-score': 0.7079942076273884, 'support': 15398.0}
No log	2.0	72	0.6577	{'precision': 0.4793814432989691, 'recall': 0.3237597911227154, 'f1-score': 0.38649350649350644, 'support': 2298.0}	{'precision': 0.41677503250975295, 'recall': 0.5702846975088968, 'f1-score': 0.48159278737791134, 'support': 1124.0}	{'precision': 0.7966573816155988, 'recall': 0.9106467661691542, 'f1-score': 0.849846782431052, 'support': 5025.0}	{'precision': 0.8743144424131627, 'recall': 0.8256365990504964, 'f1-score': 0.8492785793562707, 'support': 6951.0}	0.7598	{'precision': 0.6417820749593709, 'recall': 0.6575819634628157, 'f1-score': 0.6418029139146851, 'support': 15398.0}	{'precision': 0.7566331163186304, 'recall': 0.7598389401220937, 'f1-score': 0.7535581151939423, 'support': 15398.0}
No log	3.0	108	0.5640	{'precision': 0.5515727871250914, 'recall': 0.32811140121845084, 'f1-score': 0.4114597544338336, 'support': 2298.0}	{'precision': 0.5547752808988764, 'recall': 0.702846975088968, 'f1-score': 0.620094191522763, 'support': 1124.0}	{'precision': 0.890270812437312, 'recall': 0.8831840796019901, 'f1-score': 0.8867132867132866, 'support': 5025.0}	{'precision': 0.8300970873786407, 'recall': 0.9102287440656021, 'f1-score': 0.8683181225554106, 'support': 6951.0}	0.7994	{'precision': 0.7066789919599802, 'recall': 0.7060927999937527, 'f1-score': 0.6966463388063234, 'support': 15398.0}	{'precision': 0.7880697082354996, 'recall': 0.7993895311079361, 'f1-score': 0.7880201274566476, 'support': 15398.0}

Framework versions

• Transformers 4.37.1
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1