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
9369722
•
1 Parent(s): ac15db7

trainer: training complete at 2024-03-02 12:46:14.069670.

Browse files
Files changed (3) hide show
  1. README.md +27 -27
  2. meta_data/README_s42_e16.md +27 -27
  3. 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[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
@@ -32,13 +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: 0.3134
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
 
@@ -67,24 +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 | 0.3118 | {'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
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[80%:100%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9436981787899634
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: 0.2879
36
+ - B: {'precision': 0.8652946679139383, 'recall': 0.8868648130393096, 'f1-score': 0.8759469696969697, 'support': 1043.0}
37
+ - I: {'precision': 0.9512374695588152, 'recall': 0.9680691642651297, 'f1-score': 0.9595795126688947, 'support': 17350.0}
38
+ - O: {'precision': 0.9381536039581694, 'recall': 0.9042922176457836, 'f1-score': 0.9209117500965837, 'support': 9226.0}
39
+ - Accuracy: 0.9437
40
+ - Macro avg: {'precision': 0.9182285804769742, 'recall': 0.9197420649834077, 'f1-score': 0.9188127441541494, 'support': 27619.0}
41
+ - Weighted avg: {'precision': 0.9436213326187679, 'recall': 0.9436981787899634, 'f1-score': 0.9435044368221277, 'support': 27619.0}
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 | 0.2947 | {'precision': 0.8076923076923077, 'recall': 0.4429530201342282, 'f1-score': 0.5721362229102167, 'support': 1043.0} | {'precision': 0.8850358282336942, 'recall': 0.9752737752161383, 'f1-score': 0.927966217883682, 'support': 17350.0} | {'precision': 0.9349142280524723, 'recall': 0.8033817472360719, 'f1-score': 0.864171621779177, 'support': 9226.0} | 0.8978 | {'precision': 0.8758807879928246, 'recall': 0.7405361808621462, 'f1-score': 0.7880913541910252, 'support': 27619.0} | {'precision': 0.8987766886849552, 'recall': 0.8977515478474963, 'f1-score': 0.8932184128068332, 'support': 27619.0} |
73
+ | No log | 2.0 | 82 | 0.1954 | {'precision': 0.7979274611398963, 'recall': 0.8859060402684564, 'f1-score': 0.8396183552930486, 'support': 1043.0} | {'precision': 0.9369951534733441, 'recall': 0.9694524495677234, 'f1-score': 0.9529475085691623, 'support': 17350.0} | {'precision': 0.9441833137485312, 'recall': 0.8709083026230219, 'f1-score': 0.9060667568786648, 'support': 9226.0} | 0.9334 | {'precision': 0.8930353094539237, 'recall': 0.9087555974864006, 'f1-score': 0.8995442069136251, 'support': 27619.0} | {'precision': 0.9341445927577168, 'recall': 0.9333791954813715, 'f1-score': 0.933007462877301, 'support': 27619.0} |
74
+ | No log | 3.0 | 123 | 0.1738 | {'precision': 0.856203007518797, 'recall': 0.8734419942473634, 'f1-score': 0.8647365923113433, 'support': 1043.0} | {'precision': 0.9658622719246616, 'recall': 0.945821325648415, 'f1-score': 0.9557367501456028, 'support': 17350.0} | {'precision': 0.9021432305279665, 'recall': 0.9352915673097767, 'f1-score': 0.9184183917833005, 'support': 9226.0} | 0.9396 | {'precision': 0.9080695033238083, 'recall': 0.9181849624018517, 'f1-score': 0.9129639114134155, 'support': 27619.0} | {'precision': 0.940436062116152, 'recall': 0.9395705854665267, 'f1-score': 0.9398342070096555, 'support': 27619.0} |
75
+ | No log | 4.0 | 164 | 0.1750 | {'precision': 0.8874239350912779, 'recall': 0.8389261744966443, 'f1-score': 0.862493839329719, 'support': 1043.0} | {'precision': 0.9537671232876712, 'recall': 0.9631123919308358, 'f1-score': 0.9584169773444221, 'support': 17350.0} | {'precision': 0.926259190167892, 'recall': 0.9149143724257534, 'f1-score': 0.9205518294345384, 'support': 9226.0} | 0.9423 | {'precision': 0.9224834161822804, 'recall': 0.9056509796177444, 'f1-score': 0.9138208820362266, 'support': 27619.0} | {'precision': 0.9420728499160095, 'recall': 0.9423223143488179, 'f1-score': 0.9421458709478862, 'support': 27619.0} |
76
+ | No log | 5.0 | 205 | 0.2035 | {'precision': 0.8457399103139014, 'recall': 0.9041227229146692, 'f1-score': 0.8739573679332716, 'support': 1043.0} | {'precision': 0.9367580161988239, 'recall': 0.9732564841498559, 'f1-score': 0.954658525554048, 'support': 17350.0} | {'precision': 0.948690728945506, 'recall': 0.8717754172989378, 'f1-score': 0.9086082241301401, 'support': 9226.0} | 0.9367 | {'precision': 0.9103962184860771, 'recall': 0.916384874787821, 'f1-score': 0.9124080392058199, 'support': 27619.0} | {'precision': 0.9373068891979518, 'recall': 0.9367464426662805, 'f1-score': 0.9362280469583188, 'support': 27619.0} |
77
+ | No log | 6.0 | 246 | 0.1896 | {'precision': 0.8579335793357934, 'recall': 0.8916586768935763, 'f1-score': 0.8744710860366715, 'support': 1043.0} | {'precision': 0.9394277427631212, 'recall': 0.970778097982709, 'f1-score': 0.9548456588905582, 'support': 17350.0} | {'precision': 0.941900999302812, 'recall': 0.8786039453717754, 'f1-score': 0.9091520861372813, 'support': 9226.0} | 0.9370 | {'precision': 0.9130874404672422, 'recall': 0.9136802400826869, 'f1-score': 0.9128229436881702, 'support': 27619.0} | {'precision': 0.9371763887090455, 'recall': 0.9369998913791231, 'f1-score': 0.9365466769683909, 'support': 27619.0} |
78
+ | No log | 7.0 | 287 | 0.1974 | {'precision': 0.854262144821265, 'recall': 0.8935762224352828, 'f1-score': 0.8734770384254921, 'support': 1043.0} | {'precision': 0.9436012321478577, 'recall': 0.9710662824207493, 'f1-score': 0.9571367703451216, 'support': 17350.0} | {'precision': 0.9436181252161882, 'recall': 0.8870583134619553, 'f1-score': 0.9144644952231968, 'support': 9226.0} | 0.9401 | {'precision': 0.9138271673951035, 'recall': 0.9172336061059957, 'f1-score': 0.9150261013312702, 'support': 27619.0} | {'precision': 0.9402330865729558, 'recall': 0.9400774828922119, 'f1-score': 0.9397229787282255, 'support': 27619.0} |
79
+ | No log | 8.0 | 328 | 0.2392 | {'precision': 0.851952770208901, 'recall': 0.8993288590604027, 'f1-score': 0.875, 'support': 1043.0} | {'precision': 0.9332269074094462, 'recall': 0.9771181556195966, 'f1-score': 0.9546683185043361, 'support': 17350.0} | {'precision': 0.9541427203065134, 'recall': 0.8637546065467158, 'f1-score': 0.90670155876664, 'support': 9226.0} | 0.9363 | {'precision': 0.9131074659749535, 'recall': 0.913400540408905, 'f1-score': 0.9121232924236587, 'support': 27619.0} | {'precision': 0.937144513575063, 'recall': 0.9363119591585503, 'f1-score': 0.9356366598077863, 'support': 27619.0} |
80
+ | No log | 9.0 | 369 | 0.2588 | {'precision': 0.8356890459363958, 'recall': 0.9069990412272292, 'f1-score': 0.8698850574712644, 'support': 1043.0} | {'precision': 0.9281042189033032, 'recall': 0.9813832853025937, 'f1-score': 0.9540004482294935, 'support': 17350.0} | {'precision': 0.9629038201695124, 'recall': 0.8496639930630826, 'f1-score': 0.9027465883572292, 'support': 9226.0} | 0.9346 | {'precision': 0.9088990283364038, 'recall': 0.9126821065309684, 'f1-score': 0.9088773646859957, 'support': 27619.0} | {'precision': 0.9362389122621345, 'recall': 0.9345740251276295, 'f1-score': 0.9337028102359982, 'support': 27619.0} |
81
+ | No log | 10.0 | 410 | 0.2737 | {'precision': 0.8562091503267973, 'recall': 0.8791946308724832, 'f1-score': 0.8675496688741721, 'support': 1043.0} | {'precision': 0.9356232686980609, 'recall': 0.973371757925072, 'f1-score': 0.9541242937853106, 'support': 17350.0} | {'precision': 0.9457519416333255, 'recall': 0.8711250812920008, 'f1-score': 0.9069058903182126, 'support': 9226.0} | 0.9357 | {'precision': 0.9125281202193946, 'recall': 0.9078971566965187, 'f1-score': 0.9095266176592318, 'support': 27619.0} | {'precision': 0.9360077218295835, 'recall': 0.935660233896955, 'f1-score': 0.9350818112852286, 'support': 27619.0} |
82
+ | No log | 11.0 | 451 | 0.2722 | {'precision': 0.8556701030927835, 'recall': 0.87535953978907, 'f1-score': 0.8654028436018957, 'support': 1043.0} | {'precision': 0.9378157792460163, 'recall': 0.9735446685878962, 'f1-score': 0.9553462854557281, 'support': 17350.0} | {'precision': 0.9459079733052336, 'recall': 0.8756774333405593, 'f1-score': 0.9094388473011763, 'support': 9226.0} | 0.9371 | {'precision': 0.9131312852146779, 'recall': 0.9081938805725085, 'f1-score': 0.9100626587862667, 'support': 27619.0} | {'precision': 0.937416801808836, 'recall': 0.9371447192150332, 'f1-score': 0.9366145053671137, 'support': 27619.0} |
83
+ | No log | 12.0 | 492 | 0.2749 | {'precision': 0.8612933458294283, 'recall': 0.8811121764141898, 'f1-score': 0.8710900473933649, 'support': 1043.0} | {'precision': 0.9410812921943871, 'recall': 0.9721613832853025, 'f1-score': 0.9563688940549427, 'support': 17350.0} | {'precision': 0.9440259589755475, 'recall': 0.8829395187513549, 'f1-score': 0.9124614953794455, 'support': 9226.0} | 0.9389 | {'precision': 0.9154668656664544, 'recall': 0.9120710261502823, 'f1-score': 0.9133068122759177, 'support': 27619.0} | {'precision': 0.9390518439038746, 'recall': 0.9389188602049314, 'f1-score': 0.938481371072642, 'support': 27619.0} |
84
+ | 0.1235 | 13.0 | 533 | 0.2709 | {'precision': 0.8675925925925926, 'recall': 0.8983700862895494, 'f1-score': 0.8827131417804992, 'support': 1043.0} | {'precision': 0.9542141230068337, 'recall': 0.9657636887608069, 'f1-score': 0.959954167860212, 'support': 17350.0} | {'precision': 0.9354048335003898, 'recall': 0.9103620203771949, 'f1-score': 0.9227135402361988, 'support': 9226.0} | 0.9447 | {'precision': 0.9190705163666054, 'recall': 0.9248319318091838, 'f1-score': 0.9217936166256367, 'support': 27619.0} | {'precision': 0.9446598031108019, 'recall': 0.9447119736413339, 'f1-score': 0.944597188220823, 'support': 27619.0} |
85
+ | 0.1235 | 14.0 | 574 | 0.2806 | {'precision': 0.8703878902554399, 'recall': 0.8820709491850431, 'f1-score': 0.8761904761904762, 'support': 1043.0} | {'precision': 0.9522888825703725, 'recall': 0.9651873198847263, 'f1-score': 0.9586947187634179, 'support': 17350.0} | {'precision': 0.9327169432995432, 'recall': 0.9075438976804683, 'f1-score': 0.919958248640334, 'support': 9226.0} | 0.9428 | {'precision': 0.9184645720417852, 'recall': 0.918267388916746, 'f1-score': 0.9182811478647427, 'support': 27619.0} | {'precision': 0.942658068757521, 'recall': 0.9427930048155255, 'f1-score': 0.9426393004514172, 'support': 27619.0} |
86
+ | 0.1235 | 15.0 | 615 | 0.2848 | {'precision': 0.865546218487395, 'recall': 0.8887823585810163, 'f1-score': 0.8770104068117313, 'support': 1043.0} | {'precision': 0.9523728525259398, 'recall': 0.9681268011527377, 'f1-score': 0.9601852116500414, 'support': 17350.0} | {'precision': 0.9386151947031759, 'recall': 0.9065683936700628, 'f1-score': 0.9223135027843635, 'support': 9226.0} | 0.9446 | {'precision': 0.9188447552388369, 'recall': 0.921159184467939, 'f1-score': 0.9198363737487121, 'support': 27619.0} | {'precision': 0.9444982614699631, 'recall': 0.9445671458054238, 'f1-score': 0.9443933398429122, 'support': 27619.0} |
87
+ | 0.1235 | 16.0 | 656 | 0.2879 | {'precision': 0.8652946679139383, 'recall': 0.8868648130393096, 'f1-score': 0.8759469696969697, 'support': 1043.0} | {'precision': 0.9512374695588152, 'recall': 0.9680691642651297, 'f1-score': 0.9595795126688947, 'support': 17350.0} | {'precision': 0.9381536039581694, 'recall': 0.9042922176457836, 'f1-score': 0.9209117500965837, 'support': 9226.0} | 0.9437 | {'precision': 0.9182285804769742, 'recall': 0.9197420649834077, 'f1-score': 0.9188127441541494, 'support': 27619.0} | {'precision': 0.9436213326187679, 'recall': 0.9436981787899634, 'f1-score': 0.9435044368221277, 'support': 27619.0} |
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[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
@@ -32,13 +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: 0.3134
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
 
@@ -67,24 +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 | 0.3118 | {'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
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[80%:100%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9436981787899634
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: 0.2879
36
+ - B: {'precision': 0.8652946679139383, 'recall': 0.8868648130393096, 'f1-score': 0.8759469696969697, 'support': 1043.0}
37
+ - I: {'precision': 0.9512374695588152, 'recall': 0.9680691642651297, 'f1-score': 0.9595795126688947, 'support': 17350.0}
38
+ - O: {'precision': 0.9381536039581694, 'recall': 0.9042922176457836, 'f1-score': 0.9209117500965837, 'support': 9226.0}
39
+ - Accuracy: 0.9437
40
+ - Macro avg: {'precision': 0.9182285804769742, 'recall': 0.9197420649834077, 'f1-score': 0.9188127441541494, 'support': 27619.0}
41
+ - Weighted avg: {'precision': 0.9436213326187679, 'recall': 0.9436981787899634, 'f1-score': 0.9435044368221277, 'support': 27619.0}
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 | 0.2947 | {'precision': 0.8076923076923077, 'recall': 0.4429530201342282, 'f1-score': 0.5721362229102167, 'support': 1043.0} | {'precision': 0.8850358282336942, 'recall': 0.9752737752161383, 'f1-score': 0.927966217883682, 'support': 17350.0} | {'precision': 0.9349142280524723, 'recall': 0.8033817472360719, 'f1-score': 0.864171621779177, 'support': 9226.0} | 0.8978 | {'precision': 0.8758807879928246, 'recall': 0.7405361808621462, 'f1-score': 0.7880913541910252, 'support': 27619.0} | {'precision': 0.8987766886849552, 'recall': 0.8977515478474963, 'f1-score': 0.8932184128068332, 'support': 27619.0} |
73
+ | No log | 2.0 | 82 | 0.1954 | {'precision': 0.7979274611398963, 'recall': 0.8859060402684564, 'f1-score': 0.8396183552930486, 'support': 1043.0} | {'precision': 0.9369951534733441, 'recall': 0.9694524495677234, 'f1-score': 0.9529475085691623, 'support': 17350.0} | {'precision': 0.9441833137485312, 'recall': 0.8709083026230219, 'f1-score': 0.9060667568786648, 'support': 9226.0} | 0.9334 | {'precision': 0.8930353094539237, 'recall': 0.9087555974864006, 'f1-score': 0.8995442069136251, 'support': 27619.0} | {'precision': 0.9341445927577168, 'recall': 0.9333791954813715, 'f1-score': 0.933007462877301, 'support': 27619.0} |
74
+ | No log | 3.0 | 123 | 0.1738 | {'precision': 0.856203007518797, 'recall': 0.8734419942473634, 'f1-score': 0.8647365923113433, 'support': 1043.0} | {'precision': 0.9658622719246616, 'recall': 0.945821325648415, 'f1-score': 0.9557367501456028, 'support': 17350.0} | {'precision': 0.9021432305279665, 'recall': 0.9352915673097767, 'f1-score': 0.9184183917833005, 'support': 9226.0} | 0.9396 | {'precision': 0.9080695033238083, 'recall': 0.9181849624018517, 'f1-score': 0.9129639114134155, 'support': 27619.0} | {'precision': 0.940436062116152, 'recall': 0.9395705854665267, 'f1-score': 0.9398342070096555, 'support': 27619.0} |
75
+ | No log | 4.0 | 164 | 0.1750 | {'precision': 0.8874239350912779, 'recall': 0.8389261744966443, 'f1-score': 0.862493839329719, 'support': 1043.0} | {'precision': 0.9537671232876712, 'recall': 0.9631123919308358, 'f1-score': 0.9584169773444221, 'support': 17350.0} | {'precision': 0.926259190167892, 'recall': 0.9149143724257534, 'f1-score': 0.9205518294345384, 'support': 9226.0} | 0.9423 | {'precision': 0.9224834161822804, 'recall': 0.9056509796177444, 'f1-score': 0.9138208820362266, 'support': 27619.0} | {'precision': 0.9420728499160095, 'recall': 0.9423223143488179, 'f1-score': 0.9421458709478862, 'support': 27619.0} |
76
+ | No log | 5.0 | 205 | 0.2035 | {'precision': 0.8457399103139014, 'recall': 0.9041227229146692, 'f1-score': 0.8739573679332716, 'support': 1043.0} | {'precision': 0.9367580161988239, 'recall': 0.9732564841498559, 'f1-score': 0.954658525554048, 'support': 17350.0} | {'precision': 0.948690728945506, 'recall': 0.8717754172989378, 'f1-score': 0.9086082241301401, 'support': 9226.0} | 0.9367 | {'precision': 0.9103962184860771, 'recall': 0.916384874787821, 'f1-score': 0.9124080392058199, 'support': 27619.0} | {'precision': 0.9373068891979518, 'recall': 0.9367464426662805, 'f1-score': 0.9362280469583188, 'support': 27619.0} |
77
+ | No log | 6.0 | 246 | 0.1896 | {'precision': 0.8579335793357934, 'recall': 0.8916586768935763, 'f1-score': 0.8744710860366715, 'support': 1043.0} | {'precision': 0.9394277427631212, 'recall': 0.970778097982709, 'f1-score': 0.9548456588905582, 'support': 17350.0} | {'precision': 0.941900999302812, 'recall': 0.8786039453717754, 'f1-score': 0.9091520861372813, 'support': 9226.0} | 0.9370 | {'precision': 0.9130874404672422, 'recall': 0.9136802400826869, 'f1-score': 0.9128229436881702, 'support': 27619.0} | {'precision': 0.9371763887090455, 'recall': 0.9369998913791231, 'f1-score': 0.9365466769683909, 'support': 27619.0} |
78
+ | No log | 7.0 | 287 | 0.1974 | {'precision': 0.854262144821265, 'recall': 0.8935762224352828, 'f1-score': 0.8734770384254921, 'support': 1043.0} | {'precision': 0.9436012321478577, 'recall': 0.9710662824207493, 'f1-score': 0.9571367703451216, 'support': 17350.0} | {'precision': 0.9436181252161882, 'recall': 0.8870583134619553, 'f1-score': 0.9144644952231968, 'support': 9226.0} | 0.9401 | {'precision': 0.9138271673951035, 'recall': 0.9172336061059957, 'f1-score': 0.9150261013312702, 'support': 27619.0} | {'precision': 0.9402330865729558, 'recall': 0.9400774828922119, 'f1-score': 0.9397229787282255, 'support': 27619.0} |
79
+ | No log | 8.0 | 328 | 0.2392 | {'precision': 0.851952770208901, 'recall': 0.8993288590604027, 'f1-score': 0.875, 'support': 1043.0} | {'precision': 0.9332269074094462, 'recall': 0.9771181556195966, 'f1-score': 0.9546683185043361, 'support': 17350.0} | {'precision': 0.9541427203065134, 'recall': 0.8637546065467158, 'f1-score': 0.90670155876664, 'support': 9226.0} | 0.9363 | {'precision': 0.9131074659749535, 'recall': 0.913400540408905, 'f1-score': 0.9121232924236587, 'support': 27619.0} | {'precision': 0.937144513575063, 'recall': 0.9363119591585503, 'f1-score': 0.9356366598077863, 'support': 27619.0} |
80
+ | No log | 9.0 | 369 | 0.2588 | {'precision': 0.8356890459363958, 'recall': 0.9069990412272292, 'f1-score': 0.8698850574712644, 'support': 1043.0} | {'precision': 0.9281042189033032, 'recall': 0.9813832853025937, 'f1-score': 0.9540004482294935, 'support': 17350.0} | {'precision': 0.9629038201695124, 'recall': 0.8496639930630826, 'f1-score': 0.9027465883572292, 'support': 9226.0} | 0.9346 | {'precision': 0.9088990283364038, 'recall': 0.9126821065309684, 'f1-score': 0.9088773646859957, 'support': 27619.0} | {'precision': 0.9362389122621345, 'recall': 0.9345740251276295, 'f1-score': 0.9337028102359982, 'support': 27619.0} |
81
+ | No log | 10.0 | 410 | 0.2737 | {'precision': 0.8562091503267973, 'recall': 0.8791946308724832, 'f1-score': 0.8675496688741721, 'support': 1043.0} | {'precision': 0.9356232686980609, 'recall': 0.973371757925072, 'f1-score': 0.9541242937853106, 'support': 17350.0} | {'precision': 0.9457519416333255, 'recall': 0.8711250812920008, 'f1-score': 0.9069058903182126, 'support': 9226.0} | 0.9357 | {'precision': 0.9125281202193946, 'recall': 0.9078971566965187, 'f1-score': 0.9095266176592318, 'support': 27619.0} | {'precision': 0.9360077218295835, 'recall': 0.935660233896955, 'f1-score': 0.9350818112852286, 'support': 27619.0} |
82
+ | No log | 11.0 | 451 | 0.2722 | {'precision': 0.8556701030927835, 'recall': 0.87535953978907, 'f1-score': 0.8654028436018957, 'support': 1043.0} | {'precision': 0.9378157792460163, 'recall': 0.9735446685878962, 'f1-score': 0.9553462854557281, 'support': 17350.0} | {'precision': 0.9459079733052336, 'recall': 0.8756774333405593, 'f1-score': 0.9094388473011763, 'support': 9226.0} | 0.9371 | {'precision': 0.9131312852146779, 'recall': 0.9081938805725085, 'f1-score': 0.9100626587862667, 'support': 27619.0} | {'precision': 0.937416801808836, 'recall': 0.9371447192150332, 'f1-score': 0.9366145053671137, 'support': 27619.0} |
83
+ | No log | 12.0 | 492 | 0.2749 | {'precision': 0.8612933458294283, 'recall': 0.8811121764141898, 'f1-score': 0.8710900473933649, 'support': 1043.0} | {'precision': 0.9410812921943871, 'recall': 0.9721613832853025, 'f1-score': 0.9563688940549427, 'support': 17350.0} | {'precision': 0.9440259589755475, 'recall': 0.8829395187513549, 'f1-score': 0.9124614953794455, 'support': 9226.0} | 0.9389 | {'precision': 0.9154668656664544, 'recall': 0.9120710261502823, 'f1-score': 0.9133068122759177, 'support': 27619.0} | {'precision': 0.9390518439038746, 'recall': 0.9389188602049314, 'f1-score': 0.938481371072642, 'support': 27619.0} |
84
+ | 0.1235 | 13.0 | 533 | 0.2709 | {'precision': 0.8675925925925926, 'recall': 0.8983700862895494, 'f1-score': 0.8827131417804992, 'support': 1043.0} | {'precision': 0.9542141230068337, 'recall': 0.9657636887608069, 'f1-score': 0.959954167860212, 'support': 17350.0} | {'precision': 0.9354048335003898, 'recall': 0.9103620203771949, 'f1-score': 0.9227135402361988, 'support': 9226.0} | 0.9447 | {'precision': 0.9190705163666054, 'recall': 0.9248319318091838, 'f1-score': 0.9217936166256367, 'support': 27619.0} | {'precision': 0.9446598031108019, 'recall': 0.9447119736413339, 'f1-score': 0.944597188220823, 'support': 27619.0} |
85
+ | 0.1235 | 14.0 | 574 | 0.2806 | {'precision': 0.8703878902554399, 'recall': 0.8820709491850431, 'f1-score': 0.8761904761904762, 'support': 1043.0} | {'precision': 0.9522888825703725, 'recall': 0.9651873198847263, 'f1-score': 0.9586947187634179, 'support': 17350.0} | {'precision': 0.9327169432995432, 'recall': 0.9075438976804683, 'f1-score': 0.919958248640334, 'support': 9226.0} | 0.9428 | {'precision': 0.9184645720417852, 'recall': 0.918267388916746, 'f1-score': 0.9182811478647427, 'support': 27619.0} | {'precision': 0.942658068757521, 'recall': 0.9427930048155255, 'f1-score': 0.9426393004514172, 'support': 27619.0} |
86
+ | 0.1235 | 15.0 | 615 | 0.2848 | {'precision': 0.865546218487395, 'recall': 0.8887823585810163, 'f1-score': 0.8770104068117313, 'support': 1043.0} | {'precision': 0.9523728525259398, 'recall': 0.9681268011527377, 'f1-score': 0.9601852116500414, 'support': 17350.0} | {'precision': 0.9386151947031759, 'recall': 0.9065683936700628, 'f1-score': 0.9223135027843635, 'support': 9226.0} | 0.9446 | {'precision': 0.9188447552388369, 'recall': 0.921159184467939, 'f1-score': 0.9198363737487121, 'support': 27619.0} | {'precision': 0.9444982614699631, 'recall': 0.9445671458054238, 'f1-score': 0.9443933398429122, 'support': 27619.0} |
87
+ | 0.1235 | 16.0 | 656 | 0.2879 | {'precision': 0.8652946679139383, 'recall': 0.8868648130393096, 'f1-score': 0.8759469696969697, 'support': 1043.0} | {'precision': 0.9512374695588152, 'recall': 0.9680691642651297, 'f1-score': 0.9595795126688947, 'support': 17350.0} | {'precision': 0.9381536039581694, 'recall': 0.9042922176457836, 'f1-score': 0.9209117500965837, 'support': 9226.0} | 0.9437 | {'precision': 0.9182285804769742, 'recall': 0.9197420649834077, 'f1-score': 0.9188127441541494, 'support': 27619.0} | {'precision': 0.9436213326187679, 'recall': 0.9436981787899634, 'f1-score': 0.9435044368221277, 'support': 27619.0} |
88
 
89
 
90
  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:c53b5b5b30909241deca704a6ddd1b062bdb49e3a46df24a34685691cfa036c6
3
  size 592318676
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4819b21ef3412b7c2a48fa9d192013978d195ecdbc38c1d0056acea87027a489
3
  size 592318676