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Model_name
stringclasses
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Train_size
int64
50.8k
50.8k
Test_size
int64
12.7k
12.7k
arg
dict
lora
listlengths
2
8
Parameters
int64
129M
773M
Trainable_parameters
int64
3.59M
34.3M
r
int64
12
12
Memory Allocation
stringclasses
10 values
Training Time
stringclasses
10 values
Performance
dict
FacebookAI/roberta-large
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "out_proj", "value", "dense", "key", "query" ]
368,293,063
12,918,970
12
3467.41
2557.74
{ "accuracy": 0.9013594688586785, "f1_macro": 0.8974454565996616, "f1_weighted": 0.9015948361967913, "precision": 0.897577124640262, "recall": 0.8975787550502898 }
FacebookAI/xlm-roberta-large
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "dense", "out_proj" ]
566,408,839
6,504,058
12
4981.35
2635.2
{ "accuracy": 0.9010433133101486, "f1_macro": 0.8968564829748508, "f1_weighted": 0.9014115305791928, "precision": 0.8975384679946834, "recall": 0.8966670669598739 }
Qwen/Qwen3-Reranker-0.6B
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "down_proj", "gate_proj", "o_proj", "score", "up_proj" ]
605,769,650
9,978,802
12
3682.5
2035.22
{ "accuracy": 0.8925861523869744, "f1_macro": 0.8875559678511302, "f1_weighted": 0.8928397169865008, "precision": 0.8891348017115386, "recall": 0.8864442393091915 }
RUCAIBox/mvp
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "fc2", "q_proj", "out_proj", "fc1", "dense", "v_proj", "k_proj" ]
424,553,031
17,195,578
12
4133.0
3142.44
{ "accuracy": 0.9017546632943408, "f1_macro": 0.8970397449958468, "f1_weighted": 0.9018765423172849, "precision": 0.8981876690479841, "recall": 0.8960924473326161 }
answerdotai/ModernBERT-large
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "Wi", "Wo", "classifier", "dense" ]
403,417,831
7,573,210
12
1934.31
1565.28
{ "accuracy": 0.893692696806829, "f1_macro": 0.8881576719337093, "f1_weighted": 0.8938263220713647, "precision": 0.8902068180266404, "recall": 0.8865272797722146 }
facebook/bart-large
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "fc2", "q_proj", "out_proj", "fc1", "dense", "v_proj", "k_proj" ]
424,550,983
17,195,578
12
4141.46
3114.02
{ "accuracy": 0.902466013278533, "f1_macro": 0.8986892249827441, "f1_weighted": 0.9026084280727706, "precision": 0.8995626592103672, "recall": 0.8980865425618756 }
facebook/opt-125m
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "fc2", "q_proj", "out_proj", "fc1", "v_proj", "k_proj", "score" ]
128,838,962
3,594,290
12
1424.84
951.35
{ "accuracy": 0.8875276636104964, "f1_macro": 0.8820328276254918, "f1_weighted": 0.8877059977969118, "precision": 0.8828868097397782, "recall": 0.8814338963498097 }
facebook/opt-350m
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "fc1", "fc2", "out_proj", "project_in", "project_out", "score" ]
337,660,026
6,460,026
12
3141.29
2075.89
{ "accuracy": 0.8938507745810939, "f1_macro": 0.888995894761954, "f1_weighted": 0.8940488431043557, "precision": 0.8899515844391254, "recall": 0.8883753325646022 }
google-bert/bert-large-uncased
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "classifier", "dense" ]
341,659,271
6,504,058
12
3175.62
2119.97
{ "accuracy": 0.8914796079671198, "f1_macro": 0.8865150997196243, "f1_weighted": 0.8916633736024548, "precision": 0.8864964230041069, "recall": 0.8867869029009368 }
google-t5/t5-large
50,775
12,652
{ "auto_find_batch_size": true, "gradient_accumulation_steps": 4, "learning_rate": 0.00005, "logging_steps": 200, "lr_scheduler_type": "linear", "num_train_epochs": 1, "optim": "adamw_8bit", "output_dir": "outputs", "report_to": "none", "save_strategy": "no", "save_total_limit": 0, "seed": 3407,...
[ "wo", "out_proj", "q", "wi", "dense", "k", "v", "o" ]
773,005,383
34,302,010
12
7630.73
6064.84
{ "accuracy": 0.905311413215302, "f1_macro": 0.9004787184995604, "f1_weighted": 0.9056018535904055, "precision": 0.9007879719268211, "recall": 0.9004723659460037 }
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