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Model_name
stringclasses
10 values
Train_size
int64
50.8k
50.8k
Test_size
int64
12.7k
12.7k
arg
dict
lora
listlengths
2
9
Parameters
int64
146M
878M
Trainable_parameters
int64
21.2M
139M
r
int64
80
128
Memory Allocation
stringclasses
10 values
Training Time
stringclasses
10 values
Performance
dict
FacebookAI/roberta-large
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "dense", "value", "out_proj", "key", "query" ]
413,060,122
57,686,029
128
4163.02
1983.74
{ "accuracy": 0.8970123300663927, "f1_macro": 0.8926639861545821, "f1_weighted": 0.8972457815608174, "precision": 0.8935061846199862, "recall": 0.8920353525320343 }
FacebookAI/xlm-roberta-large
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "dense", "value", "out_proj", "key", "query" ]
596,357,146
36,452,365
80
5445.3
2379.13
{ "accuracy": 0.8920328801770471, "f1_macro": 0.8859962908610755, "f1_weighted": 0.8922692947834011, "precision": 0.8876676071780774, "recall": 0.8847411403483917 }
Qwen/Qwen3-Reranker-0.6B
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "down_proj", "gate_proj", "o_proj", "score", "up_proj" ]
637,091,840
41,300,992
96
4211.65
1314.49
{ "accuracy": 0.8860259247549794, "f1_macro": 0.880067999700453, "f1_weighted": 0.8862549776652922, "precision": 0.8801506390238921, "recall": 0.8802988216337396 }
RUCAIBox/mvp
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "fc1", "dense", "k_proj", "out_proj", "q_proj", "fc2", "v_proj" ]
476,958,349
69,600,896
128
4982.96
2385.18
{ "accuracy": 0.8912424913057224, "f1_macro": 0.8855120948293992, "f1_weighted": 0.8914036137314626, "precision": 0.8863983289066325, "recall": 0.8848600771411858 }
answerdotai/ModernBERT-large
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "classifier", "Wqkv", "dense", "Wo", "Wi" ]
446,464,026
50,619,405
112
2601.13
1085.38
{ "accuracy": 0.8831805248182105, "f1_macro": 0.875919719454243, "f1_weighted": 0.8832063314419077, "precision": 0.8791954647484452, "recall": 0.8732375482211764 }
facebook/bart-large
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "v_proj", "fc2", "out_proj", "fc1", "dense", "q_proj", "k_proj" ]
476,956,301
69,600,896
128
4960.89
2363.8
{ "accuracy": 0.8937717356939614, "f1_macro": 0.8883859032972707, "f1_weighted": 0.8939940717552797, "precision": 0.8891036323972046, "recall": 0.8879685402230544 }
facebook/opt-125m
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "v_proj", "fc2", "out_proj", "score", "fc1", "q_proj", "k_proj" ]
146,488,320
21,243,648
128
1567.27
715.22
{ "accuracy": 0.8784381915902624, "f1_macro": 0.871840213362055, "f1_weighted": 0.8787016942287315, "precision": 0.8724127306420718, "recall": 0.8715650465479577 }
facebook/opt-350m
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "v_proj", "fc2", "out_proj", "score", "project_in", "fc1", "q_proj", "k_proj", "project_out" ]
388,222,976
57,022,976
128
3925.36
1911.41
{ "accuracy": 0.8888713246917483, "f1_macro": 0.8838815847069311, "f1_weighted": 0.8890218492623143, "precision": 0.8845754325641685, "recall": 0.8834353661699724 }
google-bert/bert-large-uncased
50,775
12,652
{ "adafactor": null, "adam_beta1": null, "adam_beta2": null, "adam_epsilon": null, "auto_find_batch_size": true, "bf16": null, "fp16": null, "fp16_opt_level": null, "gradient_accumulation_steps": 4, "half_precision_backend": null, "label_smoothing_factor": null, "learning_rate": 0.00005, "logg...
[ "classifier", "dense" ]
373,179,418
38,024,205
128
3485.82
1742.44
{ "accuracy": 0.8826272526082832, "f1_macro": 0.8767859611437863, "f1_weighted": 0.8828634711185982, "precision": 0.8788249867267054, "recall": 0.8752963571517972 }
google-t5/t5-large
50,775
12,652
{ "adafactor": false, "adam_beta1": 0.9, "adam_beta2": 0.999, "adam_epsilon": 1e-8, "auto_find_batch_size": null, "bf16": false, "fp16": false, "fp16_opt_level": "O1", "gradient_accumulation_steps": 4, "half_precision_backend": "auto", "label_smoothing_factor": 0, "learning_rate": 0.00005, "lo...
[ "wi", "out_proj", "dense", "o", "wo", "v", "q", "k" ]
877,510,285
138,806,912
128
9262.57
4552.4
{ "accuracy": 0.8765412582990831, "f1_macro": 0.862985321149302, "f1_weighted": 0.876287941167223, "precision": 0.8680112312893442, "recall": 0.8595999458668795 }
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