Hugging Face's logo

Theoreticallyhugo
/
longformer-spans

Token Classification
Transformers
Safetensors
longformer
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
Theoreticallyhugo commited on
Commit
edd8276
•
1 Parent(s): 17b18ab

trainer: training complete at 2024-03-02 12:32:39.062986.

Browse files
Files changed (4) hide show
  1. README.md +27 -27
  2. meta_data/README_s42_e16.md +27 -27
  3. meta_data/meta_s42_e16_cvi3.json +1 -1
  4. model.safetensors +1 -1
README.md CHANGED
@@ -17,12 +17,12 @@ model-index:
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
- split: train[40%:60%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
- value: 0.9435675748131765
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +32,13 @@ should probably proofread and complete it, then remove this comment. -->
32
 
33
  This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 0.3010
36
- - B: {'precision': 0.8761061946902655, 'recall': 0.9173745173745174, 'f1-score': 0.8962655601659751, 'support': 1295.0}
37
- - I: {'precision': 0.9587562509283557, 'recall': 0.9650635434836781, 'f1-score': 0.9618995578957825, 'support': 20065.0}
38
- - O: {'precision': 0.9175916988416989, 'recall': 0.8967102935974531, 'f1-score': 0.907030830699505, 'support': 8481.0}
39
- - Accuracy: 0.9436
40
- - Macro avg: {'precision': 0.9174847148201067, 'recall': 0.9263827848185495, 'f1-score': 0.9217319829204209, 'support': 29841.0}
41
- - Weighted avg: {'precision': 0.943470289027774, 'recall': 0.9435675748131765, 'f1-score': 0.9434572234427906, 'support': 29841.0}
42
 
43
  ## Model description
44
 
@@ -67,24 +67,24 @@ The following hyperparameters were used during training:
67
 
68
  ### Training results
69
 
70
- | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
- | No log | 1.0 | 41 | 0.3153 | {'precision': 0.8472222222222222, 'recall': 0.47104247104247104, 'f1-score': 0.6054590570719602, 'support': 1295.0} | {'precision': 0.8894522863277146, 'recall': 0.9703962123099925, 'f1-score': 0.9281628372580799, 'support': 20065.0} | {'precision': 0.8983402489626556, 'recall': 0.7658295012380616, 'f1-score': 0.826809241932404, 'support': 8481.0} | 0.8906 | {'precision': 0.8783382525041974, 'recall': 0.735756061530175, 'f1-score': 0.786810378754148, 'support': 29841.0} | {'precision': 0.8901456571293072, 'recall': 0.8905867765825543, 'f1-score': 0.8853532384745914, 'support': 29841.0} |
73
- | No log | 2.0 | 82 | 0.2253 | {'precision': 0.7966329966329966, 'recall': 0.9135135135135135, 'f1-score': 0.8510791366906474, 'support': 1295.0} | {'precision': 0.9247806497510078, 'recall': 0.9717916770495888, 'f1-score': 0.9477035236938031, 'support': 20065.0} | {'precision': 0.9339843212763032, 'recall': 0.8007310458672326, 'f1-score': 0.8622397155916709, 'support': 8481.0} | 0.9206 | {'precision': 0.8851326558867693, 'recall': 0.895345412143445, 'f1-score': 0.8870074586587071, 'support': 29841.0} | {'precision': 0.9218352098333846, 'recall': 0.9206460909486948, 'f1-score': 0.9192209950358067, 'support': 29841.0} |
74
- | No log | 3.0 | 123 | 0.1809 | {'precision': 0.8226027397260274, 'recall': 0.9274131274131274, 'f1-score': 0.8718693284936478, 'support': 1295.0} | {'precision': 0.9520828198175992, 'recall': 0.9625218041365562, 'f1-score': 0.95727385377943, 'support': 20065.0} | {'precision': 0.91600790513834, 'recall': 0.8744251857092324, 'f1-score': 0.8947336671291549, 'support': 8481.0} | 0.9360 | {'precision': 0.8968978215606556, 'recall': 0.9214533724196388, 'f1-score': 0.9079589498007442, 'support': 29841.0} | {'precision': 0.9362110978540797, 'recall': 0.9359605911330049, 'f1-score': 0.9357932672298482, 'support': 29841.0} |
75
- | No log | 4.0 | 164 | 0.1962 | {'precision': 0.8513513513513513, 'recall': 0.9243243243243243, 'f1-score': 0.8863383931877082, 'support': 1295.0} | {'precision': 0.942660770931462, 'recall': 0.9774732120608024, 'f1-score': 0.9597514129823103, 'support': 20065.0} | {'precision': 0.9454712282081531, 'recall': 0.8504893290885509, 'f1-score': 0.8954686530105526, 'support': 8481.0} | 0.9391 | {'precision': 0.9131611168303221, 'recall': 0.9174289551578925, 'f1-score': 0.913852819726857, 'support': 29841.0} | {'precision': 0.9394969959174669, 'recall': 0.9390771086759827, 'f1-score': 0.9382959675228925, 'support': 29841.0} |
76
- | No log | 5.0 | 205 | 0.1936 | {'precision': 0.8609467455621301, 'recall': 0.8988416988416988, 'f1-score': 0.8794862108046846, 'support': 1295.0} | {'precision': 0.9656717938270347, 'recall': 0.9449289808123599, 'f1-score': 0.955187788105494, 'support': 20065.0} | {'precision': 0.8764539808018069, 'recall': 0.9151043509020163, 'f1-score': 0.8953622519612366, 'support': 8481.0} | 0.9345 | {'precision': 0.9010241733969906, 'recall': 0.9196250101853582, 'f1-score': 0.9100120836238051, 'support': 29841.0} | {'precision': 0.9357708116290517, 'recall': 0.9344525987734995, 'f1-score': 0.9348997979361299, 'support': 29841.0} |
77
- | No log | 6.0 | 246 | 0.1947 | {'precision': 0.8310533515731874, 'recall': 0.9382239382239382, 'f1-score': 0.8813928182807399, 'support': 1295.0} | {'precision': 0.958739197762126, 'recall': 0.9565412409668577, 'f1-score': 0.9576389581878055, 'support': 20065.0} | {'precision': 0.9059808612440191, 'recall': 0.8930550642612899, 'f1-score': 0.8994715278190131, 'support': 8481.0} | 0.9377 | {'precision': 0.8985911368597775, 'recall': 0.9292734144840287, 'f1-score': 0.9128344347625195, 'support': 29841.0} | {'precision': 0.9382038060921168, 'recall': 0.9377031600817667, 'f1-score': 0.9377985799116962, 'support': 29841.0} |
78
- | No log | 7.0 | 287 | 0.2014 | {'precision': 0.8799403430275914, 'recall': 0.9111969111969112, 'f1-score': 0.8952959028831563, 'support': 1295.0} | {'precision': 0.9675979919882359, 'recall': 0.9510092200348866, 'f1-score': 0.9592318906147891, 'support': 20065.0} | {'precision': 0.8888256065611118, 'recall': 0.9200565970993987, 'f1-score': 0.9041714947856316, 'support': 8481.0} | 0.9405 | {'precision': 0.9121213138589797, 'recall': 0.9274209094437321, 'f1-score': 0.919566429427859, 'support': 29841.0} | {'precision': 0.9414063343289258, 'recall': 0.940484568211521, 'f1-score': 0.9408087707079646, 'support': 29841.0} |
79
- | No log | 8.0 | 328 | 0.2169 | {'precision': 0.8607322325915291, 'recall': 0.9258687258687258, 'f1-score': 0.8921130952380952, 'support': 1295.0} | {'precision': 0.9490554125588849, 'recall': 0.9739347121853975, 'f1-score': 0.9613341204250295, 'support': 20065.0} | {'precision': 0.9371261295659921, 'recall': 0.8681759226506308, 'f1-score': 0.9013343126453666, 'support': 8481.0} | 0.9418 | {'precision': 0.9156379249054686, 'recall': 0.9226597869015847, 'f1-score': 0.9182605094361639, 'support': 29841.0} | {'precision': 0.9418321034499257, 'recall': 0.9417914949230924, 'f1-score': 0.9412778355352336, 'support': 29841.0} |
80
- | No log | 9.0 | 369 | 0.2356 | {'precision': 0.8841554559043349, 'recall': 0.9135135135135135, 'f1-score': 0.8985947588302315, 'support': 1295.0} | {'precision': 0.958962427602594, 'recall': 0.9654622476949912, 'f1-score': 0.9622013609496847, 'support': 20065.0} | {'precision': 0.9177306673090821, 'recall': 0.8983610423299139, 'f1-score': 0.9079425609247452, 'support': 8481.0} | 0.9441 | {'precision': 0.9202828502720036, 'recall': 0.9257789345128061, 'f1-score': 0.9229128935682205, 'support': 29841.0} | {'precision': 0.9439977284504704, 'recall': 0.9441372608156563, 'f1-score': 0.944020353853535, 'support': 29841.0} |
81
- | No log | 10.0 | 410 | 0.2491 | {'precision': 0.846045197740113, 'recall': 0.9250965250965251, 'f1-score': 0.883806713389893, 'support': 1295.0} | {'precision': 0.9549009000147544, 'recall': 0.9676551208572141, 'f1-score': 0.9612357047378584, 'support': 20065.0} | {'precision': 0.9259762728620861, 'recall': 0.8835043037377668, 'f1-score': 0.904241839135944, 'support': 8481.0} | 0.9419 | {'precision': 0.9089741235389845, 'recall': 0.9254186498971686, 'f1-score': 0.9164280857545651, 'support': 29841.0} | {'precision': 0.941956364063297, 'recall': 0.9418920277470594, 'f1-score': 0.9416775291416837, 'support': 29841.0} |
82
- | No log | 11.0 | 451 | 0.2823 | {'precision': 0.8699127906976745, 'recall': 0.9243243243243243, 'f1-score': 0.8962935230250841, 'support': 1295.0} | {'precision': 0.9454922579711543, 'recall': 0.9768751557438325, 'f1-score': 0.9609275419158742, 'support': 20065.0} | {'precision': 0.9427204551331781, 'recall': 0.8596863577408325, 'f1-score': 0.8992907801418439, 'support': 8481.0} | 0.9413 | {'precision': 0.9193751679340023, 'recall': 0.9202952792696631, 'f1-score': 0.9188372816942675, 'support': 29841.0} | {'precision': 0.9414245970352596, 'recall': 0.9412888308032573, 'f1-score': 0.9406050851929385, 'support': 29841.0} |
83
- | No log | 12.0 | 492 | 0.2666 | {'precision': 0.8749080206033848, 'recall': 0.9181467181467181, 'f1-score': 0.896006028636021, 'support': 1295.0} | {'precision': 0.9618267212950934, 'recall': 0.9593820084724645, 'f1-score': 0.9606028094513335, 'support': 20065.0} | {'precision': 0.9059990552668871, 'recall': 0.9046103053885155, 'f1-score': 0.9053041477373296, 'support': 8481.0} | 0.9420 | {'precision': 0.9142445990551217, 'recall': 0.9273796773358992, 'f1-score': 0.9206376619415613, 'support': 29841.0} | {'precision': 0.9421881651816595, 'recall': 0.9420260715123487, 'f1-score': 0.9420832966618057, 'support': 29841.0} |
84
- | 0.1288 | 13.0 | 533 | 0.2789 | {'precision': 0.8708971553610503, 'recall': 0.922007722007722, 'f1-score': 0.8957239309827456, 'support': 1295.0} | {'precision': 0.960913024019096, 'recall': 0.9630201844006977, 'f1-score': 0.961965450291233, 'support': 20065.0} | {'precision': 0.9144839134074871, 'recall': 0.9015446291710884, 'f1-score': 0.9079681748010924, 'support': 8481.0} | 0.9438 | {'precision': 0.9154313642625445, 'recall': 0.928857511859836, 'f1-score': 0.9218858520250238, 'support': 29841.0} | {'precision': 0.9438111897303917, 'recall': 0.9437686404611105, 'f1-score': 0.9437444234846122, 'support': 29841.0} |
85
- | 0.1288 | 14.0 | 574 | 0.2878 | {'precision': 0.8693759071117562, 'recall': 0.9250965250965251, 'f1-score': 0.8963711185933408, 'support': 1295.0} | {'precision': 0.9577367433593365, 'recall': 0.9667580363817593, 'f1-score': 0.9622262456906173, 'support': 20065.0} | {'precision': 0.9222804239249605, 'recall': 0.8927013323900483, 'f1-score': 0.9072498502097065, 'support': 8481.0} | 0.9439 | {'precision': 0.9164643581320178, 'recall': 0.9281852979561108, 'f1-score': 0.9219490714978882, 'support': 29841.0} | {'precision': 0.9438252682725914, 'recall': 0.9439026842263999, 'f1-score': 0.943743714955569, 'support': 29841.0} |
86
- | 0.1288 | 15.0 | 615 | 0.3028 | {'precision': 0.8794642857142857, 'recall': 0.9127413127413128, 'f1-score': 0.8957938613111027, 'support': 1295.0} | {'precision': 0.9580749193748449, 'recall': 0.9623722900573137, 'f1-score': 0.9602187966185977, 'support': 20065.0} | {'precision': 0.9105730040757612, 'recall': 0.8956490979837284, 'f1-score': 0.9030493966593355, 'support': 8481.0} | 0.9413 | {'precision': 0.916037403054964, 'recall': 0.9235875669274516, 'f1-score': 0.9196873515296785, 'support': 29841.0} | {'precision': 0.9411631364506148, 'recall': 0.9412553198619349, 'f1-score': 0.941175065769172, 'support': 29841.0} |
87
- | 0.1288 | 16.0 | 656 | 0.3010 | {'precision': 0.8761061946902655, 'recall': 0.9173745173745174, 'f1-score': 0.8962655601659751, 'support': 1295.0} | {'precision': 0.9587562509283557, 'recall': 0.9650635434836781, 'f1-score': 0.9618995578957825, 'support': 20065.0} | {'precision': 0.9175916988416989, 'recall': 0.8967102935974531, 'f1-score': 0.907030830699505, 'support': 8481.0} | 0.9436 | {'precision': 0.9174847148201067, 'recall': 0.9263827848185495, 'f1-score': 0.9217319829204209, 'support': 29841.0} | {'precision': 0.943470289027774, 'recall': 0.9435675748131765, 'f1-score': 0.9434572234427906, 'support': 29841.0} |
88
 
89
 
90
  ### Framework versions
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[60%:80%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9427462686567164
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 0.3134
36
+ - B: {'precision': 0.8714380384360504, 'recall': 0.9131944444444444, 'f1-score': 0.8918277382163445, 'support': 1440.0}
37
+ - I: {'precision': 0.9559525996693, 'recall': 0.9641450873210728, 'f1-score': 0.9600313660370396, 'support': 21587.0}
38
+ - O: {'precision': 0.9251394461297583, 'recall': 0.9027021865750023, 'f1-score': 0.9137831045814807, 'support': 10473.0}
39
+ - Accuracy: 0.9427
40
+ - Macro avg: {'precision': 0.9175100280783696, 'recall': 0.9266805727801732, 'f1-score': 0.9218807362782884, 'support': 33500.0}
41
+ - Weighted avg: {'precision': 0.9426867153351061, 'recall': 0.9427462686567164, 'f1-score': 0.9426411789837302, 'support': 33500.0}
42
 
43
  ## Model description
44
 
 
67
 
68
  ### Training results
69
 
70
+ | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
+ | No log | 1.0 | 41 | 0.3118 | {'precision': 0.6905294556301268, 'recall': 0.6430555555555556, 'f1-score': 0.6659475008989573, 'support': 1440.0} | {'precision': 0.9325347388596071, 'recall': 0.9015611247510076, 'f1-score': 0.9167863956473609, 'support': 21587.0} | {'precision': 0.8138010452653025, 'recall': 0.8772080588179128, 'f1-score': 0.8443157797996509, 'support': 10473.0} | 0.8828 | {'precision': 0.8122884132516788, 'recall': 0.8072749130414919, 'f1-score': 0.8090165587819897, 'support': 33500.0} | {'precision': 0.8850127812218876, 'recall': 0.8828358208955224, 'f1-score': 0.8833478055515172, 'support': 33500.0} |
73
+ | No log | 2.0 | 82 | 0.2266 | {'precision': 0.8113207547169812, 'recall': 0.8659722222222223, 'f1-score': 0.8377561303325496, 'support': 1440.0} | {'precision': 0.9191461555216729, 'recall': 0.9773938018251725, 'f1-score': 0.9473755107538951, 'support': 21587.0} | {'precision': 0.9519316163410302, 'recall': 0.8187720805881791, 'f1-score': 0.8803449514911966, 'support': 10473.0} | 0.9230 | {'precision': 0.8941328421932281, 'recall': 0.8873793682118579, 'f1-score': 0.8884921975258804, 'support': 33500.0} | {'precision': 0.9247608884769675, 'recall': 0.9230149253731343, 'f1-score': 0.9217079598594182, 'support': 33500.0} |
74
+ | No log | 3.0 | 123 | 0.2044 | {'precision': 0.8354591836734694, 'recall': 0.9097222222222222, 'f1-score': 0.8710106382978723, 'support': 1440.0} | {'precision': 0.9392974112791063, 'recall': 0.9698429610413675, 'f1-score': 0.9543258273315707, 'support': 21587.0} | {'precision': 0.9384009125790729, 'recall': 0.8640313186288552, 'f1-score': 0.8996818452972758, 'support': 10473.0} | 0.9342 | {'precision': 0.9043858358438829, 'recall': 0.9145321672974815, 'f1-score': 0.908339436975573, 'support': 33500.0} | {'precision': 0.9345536477376863, 'recall': 0.934179104477612, 'f1-score': 0.9336613408822066, 'support': 33500.0} |
75
+ | No log | 4.0 | 164 | 0.1855 | {'precision': 0.8468002585649644, 'recall': 0.9097222222222222, 'f1-score': 0.8771342484097756, 'support': 1440.0} | {'precision': 0.952900369677331, 'recall': 0.9672024829758651, 'f1-score': 0.959998160834981, 'support': 21587.0} | {'precision': 0.9341764588727345, 'recall': 0.8957318819822401, 'f1-score': 0.9145503290275409, 'support': 10473.0} | 0.9424 | {'precision': 0.9112923623716767, 'recall': 0.9242188623934425, 'f1-score': 0.9172275794240993, 'support': 33500.0} | {'precision': 0.9424860509352908, 'recall': 0.9423880597014925, 'f1-score': 0.9422280361659775, 'support': 33500.0} |
76
+ | No log | 5.0 | 205 | 0.1970 | {'precision': 0.8527131782945736, 'recall': 0.9166666666666666, 'f1-score': 0.8835341365461846, 'support': 1440.0} | {'precision': 0.9453672113485365, 'recall': 0.9755408347616621, 'f1-score': 0.9602170394181884, 'support': 21587.0} | {'precision': 0.9500826787928897, 'recall': 0.877780960565263, 'f1-score': 0.9125018611345476, 'support': 10473.0} | 0.9424 | {'precision': 0.9160543561453333, 'recall': 0.9233294873311971, 'f1-score': 0.9187510123663069, 'support': 33500.0} | {'precision': 0.9428586526305366, 'recall': 0.9424477611940298, 'f1-score': 0.9420037724838524, 'support': 33500.0} |
77
+ | No log | 6.0 | 246 | 0.2258 | {'precision': 0.8543563068920677, 'recall': 0.9125, 'f1-score': 0.882471457353929, 'support': 1440.0} | {'precision': 0.9621173050775939, 'recall': 0.9506184277574466, 'f1-score': 0.9563333022648896, 'support': 21587.0} | {'precision': 0.9025674786043449, 'recall': 0.9163563448868519, 'f1-score': 0.9094096465460059, 'support': 10473.0} | 0.9383 | {'precision': 0.9063470301913354, 'recall': 0.9264915908814327, 'f1-score': 0.9160714687216082, 'support': 33500.0} | {'precision': 0.9388683149271014, 'recall': 0.9382686567164179, 'f1-score': 0.9384887499360641, 'support': 33500.0} |
78
+ | No log | 7.0 | 287 | 0.2221 | {'precision': 0.8678122934567085, 'recall': 0.9118055555555555, 'f1-score': 0.8892651540805959, 'support': 1440.0} | {'precision': 0.9557587173243901, 'recall': 0.963728169731783, 'f1-score': 0.9597268994787103, 'support': 21587.0} | {'precision': 0.925440313111546, 'recall': 0.903084121073236, 'f1-score': 0.914125549702798, 'support': 10473.0} | 0.9425 | {'precision': 0.9163371079642149, 'recall': 0.9262059487868582, 'f1-score': 0.9210392010873681, 'support': 33500.0} | {'precision': 0.9424999860500445, 'recall': 0.9425373134328359, 'f1-score': 0.9424418890435934, 'support': 33500.0} |
79
+ | No log | 8.0 | 328 | 0.2697 | {'precision': 0.8493778650949574, 'recall': 0.9006944444444445, 'f1-score': 0.8742837883383889, 'support': 1440.0} | {'precision': 0.9607962815155641, 'recall': 0.9479779496919443, 'f1-score': 0.954344074989507, 'support': 21587.0} | {'precision': 0.8975079632752483, 'recall': 0.9147331232693593, 'f1-score': 0.9060386816096845, 'support': 10473.0} | 0.9356 | {'precision': 0.9025607032952566, 'recall': 0.9211351724685827, 'f1-score': 0.9115555149791934, 'support': 33500.0} | {'precision': 0.9362213240058178, 'recall': 0.9355522388059702, 'f1-score': 0.9358011138657909, 'support': 33500.0} |
80
+ | No log | 9.0 | 369 | 0.2370 | {'precision': 0.8687541638907396, 'recall': 0.9055555555555556, 'f1-score': 0.8867732063923836, 'support': 1440.0} | {'precision': 0.9576294655220161, 'recall': 0.9611340158428684, 'f1-score': 0.9593785402168635, 'support': 21587.0} | {'precision': 0.9195780509048679, 'recall': 0.907285400553805, 'f1-score': 0.9133903681630298, 'support': 10473.0} | 0.9419 | {'precision': 0.9153205601058745, 'recall': 0.9246583239840763, 'f1-score': 0.9198473715907589, 'support': 33500.0} | {'precision': 0.9419132595627793, 'recall': 0.941910447761194, 'f1-score': 0.9418804564369516, 'support': 33500.0} |
81
+ | No log | 10.0 | 410 | 0.2744 | {'precision': 0.8453214513049013, 'recall': 0.9222222222222223, 'f1-score': 0.8820989704417137, 'support': 1440.0} | {'precision': 0.956655776929094, 'recall': 0.9631259554361421, 'f1-score': 0.9598799630655587, 'support': 21587.0} | {'precision': 0.9273244409572381, 'recall': 0.9027976701995608, 'f1-score': 0.914896705210702, 'support': 10473.0} | 0.9425 | {'precision': 0.9097672230637445, 'recall': 0.9293819492859751, 'f1-score': 0.9189585462393248, 'support': 33500.0} | {'precision': 0.9427002990027631, 'recall': 0.9425074626865672, 'f1-score': 0.9424735663822079, 'support': 33500.0} |
82
+ | No log | 11.0 | 451 | 0.2965 | {'precision': 0.8822724161533196, 'recall': 0.8951388888888889, 'f1-score': 0.8886590830748018, 'support': 1440.0} | {'precision': 0.9591074596209505, 'recall': 0.9517765321721406, 'f1-score': 0.9554279336882978, 'support': 21587.0} | {'precision': 0.9005368748233964, 'recall': 0.91291893440275, 'f1-score': 0.9066856330014225, 'support': 10473.0} | 0.9372 | {'precision': 0.9139722501992221, 'recall': 0.9199447851545933, 'f1-score': 0.916924216588174, 'support': 33500.0} | {'precision': 0.9374939611977214, 'recall': 0.9371940298507463, 'f1-score': 0.9373197169725641, 'support': 33500.0} |
83
+ | No log | 12.0 | 492 | 0.3318 | {'precision': 0.8688963210702341, 'recall': 0.9020833333333333, 'f1-score': 0.8851788756388416, 'support': 1440.0} | {'precision': 0.9624402138235019, 'recall': 0.9508037244637977, 'f1-score': 0.956586582154592, 'support': 21587.0} | {'precision': 0.9008334113681056, 'recall': 0.9185524682516948, 'f1-score': 0.9096066565809379, 'support': 10473.0} | 0.9386 | {'precision': 0.9107233154206137, 'recall': 0.9238131753496086, 'f1-score': 0.9171240381247904, 'support': 33500.0} | {'precision': 0.9391592810569325, 'recall': 0.9386268656716418, 'f1-score': 0.9388299296795006, 'support': 33500.0} |
84
+ | 0.1206 | 13.0 | 533 | 0.2958 | {'precision': 0.8682634730538922, 'recall': 0.90625, 'f1-score': 0.8868501529051986, 'support': 1440.0} | {'precision': 0.9548452097453746, 'recall': 0.9658590818548201, 'f1-score': 0.9603205674412177, 'support': 21587.0} | {'precision': 0.927959846471804, 'recall': 0.9003150959610426, 'f1-score': 0.9139284675777842, 'support': 10473.0} | 0.9428 | {'precision': 0.9170228430903569, 'recall': 0.9241413926052875, 'f1-score': 0.9203663959747336, 'support': 33500.0} | {'precision': 0.9427184004797077, 'recall': 0.9428059701492537, 'f1-score': 0.9426590194172891, 'support': 33500.0} |
85
+ | 0.1206 | 14.0 | 574 | 0.3036 | {'precision': 0.8715046604527297, 'recall': 0.9090277777777778, 'f1-score': 0.8898708361658736, 'support': 1440.0} | {'precision': 0.9562901744719926, 'recall': 0.9648399499698893, 'f1-score': 0.960546037309475, 'support': 21587.0} | {'precision': 0.9261107848894109, 'recall': 0.9035615391960279, 'f1-score': 0.914697211347929, 'support': 10473.0} | 0.9433 | {'precision': 0.9179685399380443, 'recall': 0.9258097556478985, 'f1-score': 0.9217046949410926, 'support': 33500.0} | {'precision': 0.9432107748515115, 'recall': 0.9432835820895522, 'f1-score': 0.9431744837589658, 'support': 33500.0} |
86
+ | 0.1206 | 15.0 | 615 | 0.3104 | {'precision': 0.8767676767676768, 'recall': 0.9041666666666667, 'f1-score': 0.8902564102564102, 'support': 1440.0} | {'precision': 0.9556075838956984, 'recall': 0.9642840598508362, 'f1-score': 0.9599262162785336, 'support': 21587.0} | {'precision': 0.9239640344018765, 'recall': 0.9027021865750023, 'f1-score': 0.9132093697174595, 'support': 10473.0} | 0.9424 | {'precision': 0.9187797650217506, 'recall': 0.9237176376975017, 'f1-score': 0.9211306654174677, 'support': 33500.0} | {'precision': 0.9423260209072463, 'recall': 0.9424477611940298, 'f1-score': 0.9423265131529818, 'support': 33500.0} |
87
+ | 0.1206 | 16.0 | 656 | 0.3134 | {'precision': 0.8714380384360504, 'recall': 0.9131944444444444, 'f1-score': 0.8918277382163445, 'support': 1440.0} | {'precision': 0.9559525996693, 'recall': 0.9641450873210728, 'f1-score': 0.9600313660370396, 'support': 21587.0} | {'precision': 0.9251394461297583, 'recall': 0.9027021865750023, 'f1-score': 0.9137831045814807, 'support': 10473.0} | 0.9427 | {'precision': 0.9175100280783696, 'recall': 0.9266805727801732, 'f1-score': 0.9218807362782884, 'support': 33500.0} | {'precision': 0.9426867153351061, 'recall': 0.9427462686567164, 'f1-score': 0.9426411789837302, 'support': 33500.0} |
88
 
89
 
90
  ### Framework versions
meta_data/README_s42_e16.md CHANGED
@@ -17,12 +17,12 @@ model-index:
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
- split: train[40%:60%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
- value: 0.9435675748131765
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +32,13 @@ should probably proofread and complete it, then remove this comment. -->
32
 
33
  This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 0.3010
36
- - B: {'precision': 0.8761061946902655, 'recall': 0.9173745173745174, 'f1-score': 0.8962655601659751, 'support': 1295.0}
37
- - I: {'precision': 0.9587562509283557, 'recall': 0.9650635434836781, 'f1-score': 0.9618995578957825, 'support': 20065.0}
38
- - O: {'precision': 0.9175916988416989, 'recall': 0.8967102935974531, 'f1-score': 0.907030830699505, 'support': 8481.0}
39
- - Accuracy: 0.9436
40
- - Macro avg: {'precision': 0.9174847148201067, 'recall': 0.9263827848185495, 'f1-score': 0.9217319829204209, 'support': 29841.0}
41
- - Weighted avg: {'precision': 0.943470289027774, 'recall': 0.9435675748131765, 'f1-score': 0.9434572234427906, 'support': 29841.0}
42
 
43
  ## Model description
44
 
@@ -67,24 +67,24 @@ The following hyperparameters were used during training:
67
 
68
  ### Training results
69
 
70
- | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
- | No log | 1.0 | 41 | 0.3153 | {'precision': 0.8472222222222222, 'recall': 0.47104247104247104, 'f1-score': 0.6054590570719602, 'support': 1295.0} | {'precision': 0.8894522863277146, 'recall': 0.9703962123099925, 'f1-score': 0.9281628372580799, 'support': 20065.0} | {'precision': 0.8983402489626556, 'recall': 0.7658295012380616, 'f1-score': 0.826809241932404, 'support': 8481.0} | 0.8906 | {'precision': 0.8783382525041974, 'recall': 0.735756061530175, 'f1-score': 0.786810378754148, 'support': 29841.0} | {'precision': 0.8901456571293072, 'recall': 0.8905867765825543, 'f1-score': 0.8853532384745914, 'support': 29841.0} |
73
- | No log | 2.0 | 82 | 0.2253 | {'precision': 0.7966329966329966, 'recall': 0.9135135135135135, 'f1-score': 0.8510791366906474, 'support': 1295.0} | {'precision': 0.9247806497510078, 'recall': 0.9717916770495888, 'f1-score': 0.9477035236938031, 'support': 20065.0} | {'precision': 0.9339843212763032, 'recall': 0.8007310458672326, 'f1-score': 0.8622397155916709, 'support': 8481.0} | 0.9206 | {'precision': 0.8851326558867693, 'recall': 0.895345412143445, 'f1-score': 0.8870074586587071, 'support': 29841.0} | {'precision': 0.9218352098333846, 'recall': 0.9206460909486948, 'f1-score': 0.9192209950358067, 'support': 29841.0} |
74
- | No log | 3.0 | 123 | 0.1809 | {'precision': 0.8226027397260274, 'recall': 0.9274131274131274, 'f1-score': 0.8718693284936478, 'support': 1295.0} | {'precision': 0.9520828198175992, 'recall': 0.9625218041365562, 'f1-score': 0.95727385377943, 'support': 20065.0} | {'precision': 0.91600790513834, 'recall': 0.8744251857092324, 'f1-score': 0.8947336671291549, 'support': 8481.0} | 0.9360 | {'precision': 0.8968978215606556, 'recall': 0.9214533724196388, 'f1-score': 0.9079589498007442, 'support': 29841.0} | {'precision': 0.9362110978540797, 'recall': 0.9359605911330049, 'f1-score': 0.9357932672298482, 'support': 29841.0} |
75
- | No log | 4.0 | 164 | 0.1962 | {'precision': 0.8513513513513513, 'recall': 0.9243243243243243, 'f1-score': 0.8863383931877082, 'support': 1295.0} | {'precision': 0.942660770931462, 'recall': 0.9774732120608024, 'f1-score': 0.9597514129823103, 'support': 20065.0} | {'precision': 0.9454712282081531, 'recall': 0.8504893290885509, 'f1-score': 0.8954686530105526, 'support': 8481.0} | 0.9391 | {'precision': 0.9131611168303221, 'recall': 0.9174289551578925, 'f1-score': 0.913852819726857, 'support': 29841.0} | {'precision': 0.9394969959174669, 'recall': 0.9390771086759827, 'f1-score': 0.9382959675228925, 'support': 29841.0} |
76
- | No log | 5.0 | 205 | 0.1936 | {'precision': 0.8609467455621301, 'recall': 0.8988416988416988, 'f1-score': 0.8794862108046846, 'support': 1295.0} | {'precision': 0.9656717938270347, 'recall': 0.9449289808123599, 'f1-score': 0.955187788105494, 'support': 20065.0} | {'precision': 0.8764539808018069, 'recall': 0.9151043509020163, 'f1-score': 0.8953622519612366, 'support': 8481.0} | 0.9345 | {'precision': 0.9010241733969906, 'recall': 0.9196250101853582, 'f1-score': 0.9100120836238051, 'support': 29841.0} | {'precision': 0.9357708116290517, 'recall': 0.9344525987734995, 'f1-score': 0.9348997979361299, 'support': 29841.0} |
77
- | No log | 6.0 | 246 | 0.1947 | {'precision': 0.8310533515731874, 'recall': 0.9382239382239382, 'f1-score': 0.8813928182807399, 'support': 1295.0} | {'precision': 0.958739197762126, 'recall': 0.9565412409668577, 'f1-score': 0.9576389581878055, 'support': 20065.0} | {'precision': 0.9059808612440191, 'recall': 0.8930550642612899, 'f1-score': 0.8994715278190131, 'support': 8481.0} | 0.9377 | {'precision': 0.8985911368597775, 'recall': 0.9292734144840287, 'f1-score': 0.9128344347625195, 'support': 29841.0} | {'precision': 0.9382038060921168, 'recall': 0.9377031600817667, 'f1-score': 0.9377985799116962, 'support': 29841.0} |
78
- | No log | 7.0 | 287 | 0.2014 | {'precision': 0.8799403430275914, 'recall': 0.9111969111969112, 'f1-score': 0.8952959028831563, 'support': 1295.0} | {'precision': 0.9675979919882359, 'recall': 0.9510092200348866, 'f1-score': 0.9592318906147891, 'support': 20065.0} | {'precision': 0.8888256065611118, 'recall': 0.9200565970993987, 'f1-score': 0.9041714947856316, 'support': 8481.0} | 0.9405 | {'precision': 0.9121213138589797, 'recall': 0.9274209094437321, 'f1-score': 0.919566429427859, 'support': 29841.0} | {'precision': 0.9414063343289258, 'recall': 0.940484568211521, 'f1-score': 0.9408087707079646, 'support': 29841.0} |
79
- | No log | 8.0 | 328 | 0.2169 | {'precision': 0.8607322325915291, 'recall': 0.9258687258687258, 'f1-score': 0.8921130952380952, 'support': 1295.0} | {'precision': 0.9490554125588849, 'recall': 0.9739347121853975, 'f1-score': 0.9613341204250295, 'support': 20065.0} | {'precision': 0.9371261295659921, 'recall': 0.8681759226506308, 'f1-score': 0.9013343126453666, 'support': 8481.0} | 0.9418 | {'precision': 0.9156379249054686, 'recall': 0.9226597869015847, 'f1-score': 0.9182605094361639, 'support': 29841.0} | {'precision': 0.9418321034499257, 'recall': 0.9417914949230924, 'f1-score': 0.9412778355352336, 'support': 29841.0} |
80
- | No log | 9.0 | 369 | 0.2356 | {'precision': 0.8841554559043349, 'recall': 0.9135135135135135, 'f1-score': 0.8985947588302315, 'support': 1295.0} | {'precision': 0.958962427602594, 'recall': 0.9654622476949912, 'f1-score': 0.9622013609496847, 'support': 20065.0} | {'precision': 0.9177306673090821, 'recall': 0.8983610423299139, 'f1-score': 0.9079425609247452, 'support': 8481.0} | 0.9441 | {'precision': 0.9202828502720036, 'recall': 0.9257789345128061, 'f1-score': 0.9229128935682205, 'support': 29841.0} | {'precision': 0.9439977284504704, 'recall': 0.9441372608156563, 'f1-score': 0.944020353853535, 'support': 29841.0} |
81
- | No log | 10.0 | 410 | 0.2491 | {'precision': 0.846045197740113, 'recall': 0.9250965250965251, 'f1-score': 0.883806713389893, 'support': 1295.0} | {'precision': 0.9549009000147544, 'recall': 0.9676551208572141, 'f1-score': 0.9612357047378584, 'support': 20065.0} | {'precision': 0.9259762728620861, 'recall': 0.8835043037377668, 'f1-score': 0.904241839135944, 'support': 8481.0} | 0.9419 | {'precision': 0.9089741235389845, 'recall': 0.9254186498971686, 'f1-score': 0.9164280857545651, 'support': 29841.0} | {'precision': 0.941956364063297, 'recall': 0.9418920277470594, 'f1-score': 0.9416775291416837, 'support': 29841.0} |
82
- | No log | 11.0 | 451 | 0.2823 | {'precision': 0.8699127906976745, 'recall': 0.9243243243243243, 'f1-score': 0.8962935230250841, 'support': 1295.0} | {'precision': 0.9454922579711543, 'recall': 0.9768751557438325, 'f1-score': 0.9609275419158742, 'support': 20065.0} | {'precision': 0.9427204551331781, 'recall': 0.8596863577408325, 'f1-score': 0.8992907801418439, 'support': 8481.0} | 0.9413 | {'precision': 0.9193751679340023, 'recall': 0.9202952792696631, 'f1-score': 0.9188372816942675, 'support': 29841.0} | {'precision': 0.9414245970352596, 'recall': 0.9412888308032573, 'f1-score': 0.9406050851929385, 'support': 29841.0} |
83
- | No log | 12.0 | 492 | 0.2666 | {'precision': 0.8749080206033848, 'recall': 0.9181467181467181, 'f1-score': 0.896006028636021, 'support': 1295.0} | {'precision': 0.9618267212950934, 'recall': 0.9593820084724645, 'f1-score': 0.9606028094513335, 'support': 20065.0} | {'precision': 0.9059990552668871, 'recall': 0.9046103053885155, 'f1-score': 0.9053041477373296, 'support': 8481.0} | 0.9420 | {'precision': 0.9142445990551217, 'recall': 0.9273796773358992, 'f1-score': 0.9206376619415613, 'support': 29841.0} | {'precision': 0.9421881651816595, 'recall': 0.9420260715123487, 'f1-score': 0.9420832966618057, 'support': 29841.0} |
84
- | 0.1288 | 13.0 | 533 | 0.2789 | {'precision': 0.8708971553610503, 'recall': 0.922007722007722, 'f1-score': 0.8957239309827456, 'support': 1295.0} | {'precision': 0.960913024019096, 'recall': 0.9630201844006977, 'f1-score': 0.961965450291233, 'support': 20065.0} | {'precision': 0.9144839134074871, 'recall': 0.9015446291710884, 'f1-score': 0.9079681748010924, 'support': 8481.0} | 0.9438 | {'precision': 0.9154313642625445, 'recall': 0.928857511859836, 'f1-score': 0.9218858520250238, 'support': 29841.0} | {'precision': 0.9438111897303917, 'recall': 0.9437686404611105, 'f1-score': 0.9437444234846122, 'support': 29841.0} |
85
- | 0.1288 | 14.0 | 574 | 0.2878 | {'precision': 0.8693759071117562, 'recall': 0.9250965250965251, 'f1-score': 0.8963711185933408, 'support': 1295.0} | {'precision': 0.9577367433593365, 'recall': 0.9667580363817593, 'f1-score': 0.9622262456906173, 'support': 20065.0} | {'precision': 0.9222804239249605, 'recall': 0.8927013323900483, 'f1-score': 0.9072498502097065, 'support': 8481.0} | 0.9439 | {'precision': 0.9164643581320178, 'recall': 0.9281852979561108, 'f1-score': 0.9219490714978882, 'support': 29841.0} | {'precision': 0.9438252682725914, 'recall': 0.9439026842263999, 'f1-score': 0.943743714955569, 'support': 29841.0} |
86
- | 0.1288 | 15.0 | 615 | 0.3028 | {'precision': 0.8794642857142857, 'recall': 0.9127413127413128, 'f1-score': 0.8957938613111027, 'support': 1295.0} | {'precision': 0.9580749193748449, 'recall': 0.9623722900573137, 'f1-score': 0.9602187966185977, 'support': 20065.0} | {'precision': 0.9105730040757612, 'recall': 0.8956490979837284, 'f1-score': 0.9030493966593355, 'support': 8481.0} | 0.9413 | {'precision': 0.916037403054964, 'recall': 0.9235875669274516, 'f1-score': 0.9196873515296785, 'support': 29841.0} | {'precision': 0.9411631364506148, 'recall': 0.9412553198619349, 'f1-score': 0.941175065769172, 'support': 29841.0} |
87
- | 0.1288 | 16.0 | 656 | 0.3010 | {'precision': 0.8761061946902655, 'recall': 0.9173745173745174, 'f1-score': 0.8962655601659751, 'support': 1295.0} | {'precision': 0.9587562509283557, 'recall': 0.9650635434836781, 'f1-score': 0.9618995578957825, 'support': 20065.0} | {'precision': 0.9175916988416989, 'recall': 0.8967102935974531, 'f1-score': 0.907030830699505, 'support': 8481.0} | 0.9436 | {'precision': 0.9174847148201067, 'recall': 0.9263827848185495, 'f1-score': 0.9217319829204209, 'support': 29841.0} | {'precision': 0.943470289027774, 'recall': 0.9435675748131765, 'f1-score': 0.9434572234427906, 'support': 29841.0} |
88
 
89
 
90
  ### Framework versions
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[60%:80%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9427462686567164
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 0.3134
36
+ - B: {'precision': 0.8714380384360504, 'recall': 0.9131944444444444, 'f1-score': 0.8918277382163445, 'support': 1440.0}
37
+ - I: {'precision': 0.9559525996693, 'recall': 0.9641450873210728, 'f1-score': 0.9600313660370396, 'support': 21587.0}
38
+ - O: {'precision': 0.9251394461297583, 'recall': 0.9027021865750023, 'f1-score': 0.9137831045814807, 'support': 10473.0}
39
+ - Accuracy: 0.9427
40
+ - Macro avg: {'precision': 0.9175100280783696, 'recall': 0.9266805727801732, 'f1-score': 0.9218807362782884, 'support': 33500.0}
41
+ - Weighted avg: {'precision': 0.9426867153351061, 'recall': 0.9427462686567164, 'f1-score': 0.9426411789837302, 'support': 33500.0}
42
 
43
  ## Model description
44
 
 
67
 
68
  ### Training results
69
 
70
+ | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
+ | No log | 1.0 | 41 | 0.3118 | {'precision': 0.6905294556301268, 'recall': 0.6430555555555556, 'f1-score': 0.6659475008989573, 'support': 1440.0} | {'precision': 0.9325347388596071, 'recall': 0.9015611247510076, 'f1-score': 0.9167863956473609, 'support': 21587.0} | {'precision': 0.8138010452653025, 'recall': 0.8772080588179128, 'f1-score': 0.8443157797996509, 'support': 10473.0} | 0.8828 | {'precision': 0.8122884132516788, 'recall': 0.8072749130414919, 'f1-score': 0.8090165587819897, 'support': 33500.0} | {'precision': 0.8850127812218876, 'recall': 0.8828358208955224, 'f1-score': 0.8833478055515172, 'support': 33500.0} |
73
+ | No log | 2.0 | 82 | 0.2266 | {'precision': 0.8113207547169812, 'recall': 0.8659722222222223, 'f1-score': 0.8377561303325496, 'support': 1440.0} | {'precision': 0.9191461555216729, 'recall': 0.9773938018251725, 'f1-score': 0.9473755107538951, 'support': 21587.0} | {'precision': 0.9519316163410302, 'recall': 0.8187720805881791, 'f1-score': 0.8803449514911966, 'support': 10473.0} | 0.9230 | {'precision': 0.8941328421932281, 'recall': 0.8873793682118579, 'f1-score': 0.8884921975258804, 'support': 33500.0} | {'precision': 0.9247608884769675, 'recall': 0.9230149253731343, 'f1-score': 0.9217079598594182, 'support': 33500.0} |
74
+ | No log | 3.0 | 123 | 0.2044 | {'precision': 0.8354591836734694, 'recall': 0.9097222222222222, 'f1-score': 0.8710106382978723, 'support': 1440.0} | {'precision': 0.9392974112791063, 'recall': 0.9698429610413675, 'f1-score': 0.9543258273315707, 'support': 21587.0} | {'precision': 0.9384009125790729, 'recall': 0.8640313186288552, 'f1-score': 0.8996818452972758, 'support': 10473.0} | 0.9342 | {'precision': 0.9043858358438829, 'recall': 0.9145321672974815, 'f1-score': 0.908339436975573, 'support': 33500.0} | {'precision': 0.9345536477376863, 'recall': 0.934179104477612, 'f1-score': 0.9336613408822066, 'support': 33500.0} |
75
+ | No log | 4.0 | 164 | 0.1855 | {'precision': 0.8468002585649644, 'recall': 0.9097222222222222, 'f1-score': 0.8771342484097756, 'support': 1440.0} | {'precision': 0.952900369677331, 'recall': 0.9672024829758651, 'f1-score': 0.959998160834981, 'support': 21587.0} | {'precision': 0.9341764588727345, 'recall': 0.8957318819822401, 'f1-score': 0.9145503290275409, 'support': 10473.0} | 0.9424 | {'precision': 0.9112923623716767, 'recall': 0.9242188623934425, 'f1-score': 0.9172275794240993, 'support': 33500.0} | {'precision': 0.9424860509352908, 'recall': 0.9423880597014925, 'f1-score': 0.9422280361659775, 'support': 33500.0} |
76
+ | No log | 5.0 | 205 | 0.1970 | {'precision': 0.8527131782945736, 'recall': 0.9166666666666666, 'f1-score': 0.8835341365461846, 'support': 1440.0} | {'precision': 0.9453672113485365, 'recall': 0.9755408347616621, 'f1-score': 0.9602170394181884, 'support': 21587.0} | {'precision': 0.9500826787928897, 'recall': 0.877780960565263, 'f1-score': 0.9125018611345476, 'support': 10473.0} | 0.9424 | {'precision': 0.9160543561453333, 'recall': 0.9233294873311971, 'f1-score': 0.9187510123663069, 'support': 33500.0} | {'precision': 0.9428586526305366, 'recall': 0.9424477611940298, 'f1-score': 0.9420037724838524, 'support': 33500.0} |
77
+ | No log | 6.0 | 246 | 0.2258 | {'precision': 0.8543563068920677, 'recall': 0.9125, 'f1-score': 0.882471457353929, 'support': 1440.0} | {'precision': 0.9621173050775939, 'recall': 0.9506184277574466, 'f1-score': 0.9563333022648896, 'support': 21587.0} | {'precision': 0.9025674786043449, 'recall': 0.9163563448868519, 'f1-score': 0.9094096465460059, 'support': 10473.0} | 0.9383 | {'precision': 0.9063470301913354, 'recall': 0.9264915908814327, 'f1-score': 0.9160714687216082, 'support': 33500.0} | {'precision': 0.9388683149271014, 'recall': 0.9382686567164179, 'f1-score': 0.9384887499360641, 'support': 33500.0} |
78
+ | No log | 7.0 | 287 | 0.2221 | {'precision': 0.8678122934567085, 'recall': 0.9118055555555555, 'f1-score': 0.8892651540805959, 'support': 1440.0} | {'precision': 0.9557587173243901, 'recall': 0.963728169731783, 'f1-score': 0.9597268994787103, 'support': 21587.0} | {'precision': 0.925440313111546, 'recall': 0.903084121073236, 'f1-score': 0.914125549702798, 'support': 10473.0} | 0.9425 | {'precision': 0.9163371079642149, 'recall': 0.9262059487868582, 'f1-score': 0.9210392010873681, 'support': 33500.0} | {'precision': 0.9424999860500445, 'recall': 0.9425373134328359, 'f1-score': 0.9424418890435934, 'support': 33500.0} |
79
+ | No log | 8.0 | 328 | 0.2697 | {'precision': 0.8493778650949574, 'recall': 0.9006944444444445, 'f1-score': 0.8742837883383889, 'support': 1440.0} | {'precision': 0.9607962815155641, 'recall': 0.9479779496919443, 'f1-score': 0.954344074989507, 'support': 21587.0} | {'precision': 0.8975079632752483, 'recall': 0.9147331232693593, 'f1-score': 0.9060386816096845, 'support': 10473.0} | 0.9356 | {'precision': 0.9025607032952566, 'recall': 0.9211351724685827, 'f1-score': 0.9115555149791934, 'support': 33500.0} | {'precision': 0.9362213240058178, 'recall': 0.9355522388059702, 'f1-score': 0.9358011138657909, 'support': 33500.0} |
80
+ | No log | 9.0 | 369 | 0.2370 | {'precision': 0.8687541638907396, 'recall': 0.9055555555555556, 'f1-score': 0.8867732063923836, 'support': 1440.0} | {'precision': 0.9576294655220161, 'recall': 0.9611340158428684, 'f1-score': 0.9593785402168635, 'support': 21587.0} | {'precision': 0.9195780509048679, 'recall': 0.907285400553805, 'f1-score': 0.9133903681630298, 'support': 10473.0} | 0.9419 | {'precision': 0.9153205601058745, 'recall': 0.9246583239840763, 'f1-score': 0.9198473715907589, 'support': 33500.0} | {'precision': 0.9419132595627793, 'recall': 0.941910447761194, 'f1-score': 0.9418804564369516, 'support': 33500.0} |
81
+ | No log | 10.0 | 410 | 0.2744 | {'precision': 0.8453214513049013, 'recall': 0.9222222222222223, 'f1-score': 0.8820989704417137, 'support': 1440.0} | {'precision': 0.956655776929094, 'recall': 0.9631259554361421, 'f1-score': 0.9598799630655587, 'support': 21587.0} | {'precision': 0.9273244409572381, 'recall': 0.9027976701995608, 'f1-score': 0.914896705210702, 'support': 10473.0} | 0.9425 | {'precision': 0.9097672230637445, 'recall': 0.9293819492859751, 'f1-score': 0.9189585462393248, 'support': 33500.0} | {'precision': 0.9427002990027631, 'recall': 0.9425074626865672, 'f1-score': 0.9424735663822079, 'support': 33500.0} |
82
+ | No log | 11.0 | 451 | 0.2965 | {'precision': 0.8822724161533196, 'recall': 0.8951388888888889, 'f1-score': 0.8886590830748018, 'support': 1440.0} | {'precision': 0.9591074596209505, 'recall': 0.9517765321721406, 'f1-score': 0.9554279336882978, 'support': 21587.0} | {'precision': 0.9005368748233964, 'recall': 0.91291893440275, 'f1-score': 0.9066856330014225, 'support': 10473.0} | 0.9372 | {'precision': 0.9139722501992221, 'recall': 0.9199447851545933, 'f1-score': 0.916924216588174, 'support': 33500.0} | {'precision': 0.9374939611977214, 'recall': 0.9371940298507463, 'f1-score': 0.9373197169725641, 'support': 33500.0} |
83
+ | No log | 12.0 | 492 | 0.3318 | {'precision': 0.8688963210702341, 'recall': 0.9020833333333333, 'f1-score': 0.8851788756388416, 'support': 1440.0} | {'precision': 0.9624402138235019, 'recall': 0.9508037244637977, 'f1-score': 0.956586582154592, 'support': 21587.0} | {'precision': 0.9008334113681056, 'recall': 0.9185524682516948, 'f1-score': 0.9096066565809379, 'support': 10473.0} | 0.9386 | {'precision': 0.9107233154206137, 'recall': 0.9238131753496086, 'f1-score': 0.9171240381247904, 'support': 33500.0} | {'precision': 0.9391592810569325, 'recall': 0.9386268656716418, 'f1-score': 0.9388299296795006, 'support': 33500.0} |
84
+ | 0.1206 | 13.0 | 533 | 0.2958 | {'precision': 0.8682634730538922, 'recall': 0.90625, 'f1-score': 0.8868501529051986, 'support': 1440.0} | {'precision': 0.9548452097453746, 'recall': 0.9658590818548201, 'f1-score': 0.9603205674412177, 'support': 21587.0} | {'precision': 0.927959846471804, 'recall': 0.9003150959610426, 'f1-score': 0.9139284675777842, 'support': 10473.0} | 0.9428 | {'precision': 0.9170228430903569, 'recall': 0.9241413926052875, 'f1-score': 0.9203663959747336, 'support': 33500.0} | {'precision': 0.9427184004797077, 'recall': 0.9428059701492537, 'f1-score': 0.9426590194172891, 'support': 33500.0} |
85
+ | 0.1206 | 14.0 | 574 | 0.3036 | {'precision': 0.8715046604527297, 'recall': 0.9090277777777778, 'f1-score': 0.8898708361658736, 'support': 1440.0} | {'precision': 0.9562901744719926, 'recall': 0.9648399499698893, 'f1-score': 0.960546037309475, 'support': 21587.0} | {'precision': 0.9261107848894109, 'recall': 0.9035615391960279, 'f1-score': 0.914697211347929, 'support': 10473.0} | 0.9433 | {'precision': 0.9179685399380443, 'recall': 0.9258097556478985, 'f1-score': 0.9217046949410926, 'support': 33500.0} | {'precision': 0.9432107748515115, 'recall': 0.9432835820895522, 'f1-score': 0.9431744837589658, 'support': 33500.0} |
86
+ | 0.1206 | 15.0 | 615 | 0.3104 | {'precision': 0.8767676767676768, 'recall': 0.9041666666666667, 'f1-score': 0.8902564102564102, 'support': 1440.0} | {'precision': 0.9556075838956984, 'recall': 0.9642840598508362, 'f1-score': 0.9599262162785336, 'support': 21587.0} | {'precision': 0.9239640344018765, 'recall': 0.9027021865750023, 'f1-score': 0.9132093697174595, 'support': 10473.0} | 0.9424 | {'precision': 0.9187797650217506, 'recall': 0.9237176376975017, 'f1-score': 0.9211306654174677, 'support': 33500.0} | {'precision': 0.9423260209072463, 'recall': 0.9424477611940298, 'f1-score': 0.9423265131529818, 'support': 33500.0} |
87
+ | 0.1206 | 16.0 | 656 | 0.3134 | {'precision': 0.8714380384360504, 'recall': 0.9131944444444444, 'f1-score': 0.8918277382163445, 'support': 1440.0} | {'precision': 0.9559525996693, 'recall': 0.9641450873210728, 'f1-score': 0.9600313660370396, 'support': 21587.0} | {'precision': 0.9251394461297583, 'recall': 0.9027021865750023, 'f1-score': 0.9137831045814807, 'support': 10473.0} | 0.9427 | {'precision': 0.9175100280783696, 'recall': 0.9266805727801732, 'f1-score': 0.9218807362782884, 'support': 33500.0} | {'precision': 0.9426867153351061, 'recall': 0.9427462686567164, 'f1-score': 0.9426411789837302, 'support': 33500.0} |
88
 
89
 
90
  ### Framework versions
meta_data/meta_s42_e16_cvi3.json CHANGED
@@ -1 +1 @@
1
- {"B": {"precision": 0.8767676767676768, "recall": 0.9041666666666667, "f1-score": 0.8902564102564102, "support": 1440.0}, "I": {"precision": 0.9556075838956984, "recall": 0.9642840598508362, "f1-score": 0.9599262162785336, "support": 21587.0}, "O": {"precision": 0.9239640344018765, "recall": 0.9027021865750023, "f1-score": 0.9132093697174595, "support": 10473.0}, "accuracy": 0.9424477611940298, "macro avg": {"precision": 0.9187797650217506, "recall": 0.9237176376975017, "f1-score": 0.9211306654174677, "support": 33500.0}, "weighted avg": {"precision": 0.9423260209072463, "recall": 0.9424477611940298, "f1-score": 0.9423265131529818, "support": 33500.0}}
 
1
+ {"B": {"precision": 0.8714380384360504, "recall": 0.9131944444444444, "f1-score": 0.8918277382163445, "support": 1440.0}, "I": {"precision": 0.9559525996693, "recall": 0.9641450873210728, "f1-score": 0.9600313660370396, "support": 21587.0}, "O": {"precision": 0.9251394461297583, "recall": 0.9027021865750023, "f1-score": 0.9137831045814807, "support": 10473.0}, "accuracy": 0.9427462686567164, "macro avg": {"precision": 0.9175100280783696, "recall": 0.9266805727801732, "f1-score": 0.9218807362782884, "support": 33500.0}, "weighted avg": {"precision": 0.9426867153351061, "recall": 0.9427462686567164, "f1-score": 0.9426411789837302, "support": 33500.0}}
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:cfe883009ca6eba3d193033ca6483a6c43d4f0310d57d368d86592627af508ee
3
  size 592318676
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3dfef83d80595cc0f7a8470a2848ac6186c883b9615f6596ebfbdb3bac499b84
3
  size 592318676