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Theoreticallyhugo
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longformer-spans

Token Classification
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longformer
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trainer: training complete at 2024-03-02 12:08:08.393559.

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  1. README.md +27 -27
  2. meta_data/README_s42_e16.md +27 -27
  3. meta_data/meta_s42_e16_cvi1.json +1 -1
  4. model.safetensors +1 -1
README.md CHANGED
@@ -17,12 +17,12 @@ model-index:
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  name: essays_su_g
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  type: essays_su_g
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  config: spans
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- split: train[0%:20%]
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  args: spans
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9266721210881571
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  ---
27
 
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  <!-- 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. -->
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  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: 0.4247
36
- - B: {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}
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- - I: {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}
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- - O: {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0}
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- - Accuracy: 0.9267
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- - Macro avg: {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0}
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- - Weighted avg: {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0}
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  ## Model description
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@@ -67,24 +67,24 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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- | No log | 1.0 | 41 | 0.3607 | {'precision': 0.8202247191011236, 'recall': 0.19329214474845544, 'f1-score': 0.3128571428571429, 'support': 1133.0} | {'precision': 0.8412101850981866, 'recall': 0.9767086674303169, 'f1-score': 0.9039097402761301, 'support': 18333.0} | {'precision': 0.9249453797712376, 'recall': 0.7293271179570329, 'f1-score': 0.8155702872684006, 'support': 9868.0} | 0.8632 | {'precision': 0.8621267613235158, 'recall': 0.6331093100452684, 'f1-score': 0.6774457234672245, 'support': 29334.0} | {'precision': 0.8685682804162133, 'recall': 0.8632303811277017, 'f1-score': 0.8513633328596172, 'support': 29334.0} |
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- | No log | 2.0 | 82 | 0.2532 | {'precision': 0.7996755879967559, 'recall': 0.8702559576345984, 'f1-score': 0.8334742180896026, 'support': 1133.0} | {'precision': 0.9331675137882557, 'recall': 0.9413625702285496, 'f1-score': 0.937247128465528, 'support': 18333.0} | {'precision': 0.8902883314250026, 'recall': 0.8667409809485205, 'f1-score': 0.878356867779204, 'support': 9868.0} | 0.9135 | {'precision': 0.874377144403338, 'recall': 0.892786502937223, 'f1-score': 0.8830260714447782, 'support': 29334.0} | {'precision': 0.9135868864110707, 'recall': 0.9135133292425173, 'f1-score': 0.9134282220801536, 'support': 29334.0} |
74
- | No log | 3.0 | 123 | 0.2280 | {'precision': 0.8041074249605056, 'recall': 0.8984995586937334, 'f1-score': 0.8486869528970404, 'support': 1133.0} | {'precision': 0.9231209660628774, 'recall': 0.9673812251131839, 'f1-score': 0.9447329870821681, 'support': 18333.0} | {'precision': 0.9356368563685636, 'recall': 0.8396838265099311, 'f1-score': 0.8850672933133945, 'support': 9868.0} | 0.9218 | {'precision': 0.8876217491306488, 'recall': 0.9018548701056162, 'f1-score': 0.8928290777642011, 'support': 29334.0} | {'precision': 0.9227345360999514, 'recall': 0.9217631417467785, 'f1-score': 0.9209516676970857, 'support': 29334.0} |
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- | No log | 4.0 | 164 | 0.2643 | {'precision': 0.8111111111111111, 'recall': 0.9020300088261254, 'f1-score': 0.854157960718763, 'support': 1133.0} | {'precision': 0.9180539091893006, 'recall': 0.9716358479245077, 'f1-score': 0.9440852236591055, 'support': 18333.0} | {'precision': 0.9426825049013955, 'recall': 0.8283340089177138, 'f1-score': 0.8818167107179459, 'support': 9868.0} | 0.9207 | {'precision': 0.8906158417339357, 'recall': 0.9006666218894489, 'f1-score': 0.8933532983652714, 'support': 29334.0} | {'precision': 0.9222084326864153, 'recall': 0.9207404377173246, 'f1-score': 0.9196646443104053, 'support': 29334.0} |
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- | No log | 5.0 | 205 | 0.2475 | {'precision': 0.8158526821457166, 'recall': 0.8993821712268314, 'f1-score': 0.8555835432409741, 'support': 1133.0} | {'precision': 0.9278074866310161, 'recall': 0.965308460153821, 'f1-score': 0.9461865426257119, 'support': 18333.0} | {'precision': 0.9316391077571856, 'recall': 0.8507296311309283, 'f1-score': 0.8893479527517347, 'support': 9868.0} | 0.9242 | {'precision': 0.8917664255113061, 'recall': 0.9051400875038601, 'f1-score': 0.8970393462061402, 'support': 29334.0} | {'precision': 0.924772293469197, 'recall': 0.9242176314174678, 'f1-score': 0.9235664975183513, 'support': 29334.0} |
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- | No log | 6.0 | 246 | 0.2554 | {'precision': 0.8058176100628931, 'recall': 0.9046778464254193, 'f1-score': 0.8523908523908524, 'support': 1133.0} | {'precision': 0.9404408990459764, 'recall': 0.951726395025364, 'f1-score': 0.946049991866833, 'support': 18333.0} | {'precision': 0.9114523083394679, 'recall': 0.8782934738548844, 'f1-score': 0.894565722248026, 'support': 9868.0} | 0.9252 | {'precision': 0.8859036058161124, 'recall': 0.9115659051018893, 'f1-score': 0.8976688555019038, 'support': 29334.0} | {'precision': 0.9254893888697421, 'recall': 0.9252062453126065, 'f1-score': 0.9251131071042821, 'support': 29334.0} |
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- | No log | 7.0 | 287 | 0.2822 | {'precision': 0.8323746918652424, 'recall': 0.8940864960282436, 'f1-score': 0.8621276595744681, 'support': 1133.0} | {'precision': 0.9353455123113582, 'recall': 0.9635084274259532, 'f1-score': 0.9492181202643882, 'support': 18333.0} | {'precision': 0.9287261698440208, 'recall': 0.8688690717470612, 'f1-score': 0.8978010471204189, 'support': 9868.0} | 0.9290 | {'precision': 0.8988154580068738, 'recall': 0.9088213317337527, 'f1-score': 0.9030489423197584, 'support': 29334.0} | {'precision': 0.9291415983878176, 'recall': 0.9289902502215859, 'f1-score': 0.9285575499450874, 'support': 29334.0} |
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- | No log | 8.0 | 328 | 0.3068 | {'precision': 0.8181089743589743, 'recall': 0.9011473962930273, 'f1-score': 0.8576228475430492, 'support': 1133.0} | {'precision': 0.933372111469515, 'recall': 0.9627993236240658, 'f1-score': 0.9478573729996776, 'support': 18333.0} | {'precision': 0.9279564032697548, 'recall': 0.8627888123226591, 'f1-score': 0.8941868403087749, 'support': 9868.0} | 0.9268 | {'precision': 0.8931458296994147, 'recall': 0.9089118440799174, 'f1-score': 0.899889020283834, 'support': 29334.0} | {'precision': 0.9270983219126366, 'recall': 0.9267743914911025, 'f1-score': 0.926317298889901, 'support': 29334.0} |
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- | No log | 9.0 | 369 | 0.3574 | {'precision': 0.8315441783649876, 'recall': 0.8887908208296558, 'f1-score': 0.8592150170648465, 'support': 1133.0} | {'precision': 0.9180683108038387, 'recall': 0.9705994654448262, 'f1-score': 0.9436033408458174, 'support': 18333.0} | {'precision': 0.9387941883079739, 'recall': 0.8315768139440616, 'f1-score': 0.8819388467945618, 'support': 9868.0} | 0.9207 | {'precision': 0.8961355591589334, 'recall': 0.8969890334061811, 'f1-score': 0.8949190682350753, 'support': 29334.0} | {'precision': 0.9216986072911089, 'recall': 0.9206722574486943, 'f1-score': 0.9195998909875767, 'support': 29334.0} |
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- | No log | 10.0 | 410 | 0.3228 | {'precision': 0.8491048593350383, 'recall': 0.8790820829655781, 'f1-score': 0.8638334778837814, 'support': 1133.0} | {'precision': 0.9479900314226893, 'recall': 0.9544537173403153, 'f1-score': 0.9512108939686336, 'support': 18333.0} | {'precision': 0.9123982273523652, 'recall': 0.897142278070531, 'f1-score': 0.9047059424658934, 'support': 9868.0} | 0.9323 | {'precision': 0.9031643727033641, 'recall': 0.9102260261254749, 'f1-score': 0.9065834381061029, 'support': 29334.0} | {'precision': 0.9321975441198576, 'recall': 0.9322629031158383, 'f1-score': 0.9321916850692957, 'support': 29334.0} |
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- | No log | 11.0 | 451 | 0.3397 | {'precision': 0.8524871355060034, 'recall': 0.8773168578993822, 'f1-score': 0.8647237929534581, 'support': 1133.0} | {'precision': 0.941372096765542, 'recall': 0.9572901325478645, 'f1-score': 0.9492643877109477, 'support': 18333.0} | {'precision': 0.9164304461942258, 'recall': 0.8845764085934333, 'f1-score': 0.9002217294900223, 'support': 9868.0} | 0.9297 | {'precision': 0.9034298928219237, 'recall': 0.9063944663468934, 'f1-score': 0.9047366367181428, 'support': 29334.0} | {'precision': 0.9295485858585807, 'recall': 0.9297402331765187, 'f1-score': 0.9295010603371041, 'support': 29334.0} |
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- | No log | 12.0 | 492 | 0.3769 | {'precision': 0.8406040268456376, 'recall': 0.884377758164166, 'f1-score': 0.8619354838709679, 'support': 1133.0} | {'precision': 0.9364758459246648, 'recall': 0.9601265477554137, 'f1-score': 0.9481537342777884, 'support': 18333.0} | {'precision': 0.9215707254440403, 'recall': 0.8728212403729225, 'f1-score': 0.8965337774539398, 'support': 9868.0} | 0.9278 | {'precision': 0.8995501994047809, 'recall': 0.9057751820975007, 'f1-score': 0.9022076652008987, 'support': 29334.0} | {'precision': 0.9277587769971628, 'recall': 0.9278311856548714, 'f1-score': 0.927458601951864, 'support': 29334.0} |
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- | 0.1199 | 13.0 | 533 | 0.4395 | {'precision': 0.8141945773524721, 'recall': 0.9011473962930273, 'f1-score': 0.8554671135316297, 'support': 1133.0} | {'precision': 0.9200891931134619, 'recall': 0.967817596683576, 'f1-score': 0.9433500810803624, 'support': 18333.0} | {'precision': 0.9357662573897226, 'recall': 0.8341102553708958, 'f1-score': 0.8820188598371196, 'support': 9868.0} | 0.9203 | {'precision': 0.8900166759518856, 'recall': 0.9010250827824997, 'f1-score': 0.8936120181497039, 'support': 29334.0} | {'precision': 0.9212728936187097, 'recall': 0.9202631758369128, 'f1-score': 0.9193237671285989, 'support': 29334.0} |
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- | 0.1199 | 14.0 | 574 | 0.4362 | {'precision': 0.8338842975206612, 'recall': 0.8905560458958517, 'f1-score': 0.861288945795988, 'support': 1133.0} | {'precision': 0.9288525106249016, 'recall': 0.9656357388316151, 'f1-score': 0.9468870346598203, 'support': 18333.0} | {'precision': 0.9307225592939878, 'recall': 0.8549858127280098, 'f1-score': 0.8912480853536153, 'support': 9868.0} | 0.9255 | {'precision': 0.8978197891465168, 'recall': 0.9037258658184922, 'f1-score': 0.8998080219364746, 'support': 29334.0} | {'precision': 0.9258135338341278, 'recall': 0.9255130565214427, 'f1-score': 0.9248638606488995, 'support': 29334.0} |
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- | 0.1199 | 15.0 | 615 | 0.4385 | {'precision': 0.8272208638956805, 'recall': 0.8958517210944396, 'f1-score': 0.8601694915254238, 'support': 1133.0} | {'precision': 0.9282784730255548, 'recall': 0.9629629629629629, 'f1-score': 0.9453026692725763, 'support': 18333.0} | {'precision': 0.9263945428539994, 'recall': 0.8532630725577625, 'f1-score': 0.8883262119533682, 'support': 9868.0} | 0.9235 | {'precision': 0.8939646265917448, 'recall': 0.9040259188717217, 'f1-score': 0.8979327909171229, 'support': 29334.0} | {'precision': 0.923741454750616, 'recall': 0.9234676484625349, 'f1-score': 0.9228475124165911, 'support': 29334.0} |
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- | 0.1199 | 16.0 | 656 | 0.4247 | {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0} | {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0} | {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0} | 0.9267 | {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0} | {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0} |
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  ### Framework versions
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[20%:40%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9337012922629474
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:
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+ - Loss: 0.2971
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+ - B: {'precision': 0.8351477449455676, 'recall': 0.9117147707979627, 'f1-score': 0.8717532467532468, 'support': 1178.0}
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+ - I: {'precision': 0.9428615911567804, 'recall': 0.9613207047991957, 'f1-score': 0.9520016767973171, 'support': 18899.0}
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+ - O: {'precision': 0.9285714285714286, 'recall': 0.8849705304518664, 'f1-score': 0.9062468564530731, 'support': 10180.0}
39
+ - Accuracy: 0.9337
40
+ - Macro avg: {'precision': 0.9021935882245922, 'recall': 0.9193353353496749, 'f1-score': 0.9100005933345456, 'support': 30257.0}
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+ - Weighted avg: {'precision': 0.9338600124822359, 'recall': 0.9337012922629474, 'f1-score': 0.9334830952559773, 'support': 30257.0}
42
 
43
  ## Model description
44
 
 
67
 
68
  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
71
+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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+ | No log | 1.0 | 41 | 0.2806 | {'precision': 0.7871116225546605, 'recall': 0.5806451612903226, 'f1-score': 0.668295065950171, 'support': 1178.0} | {'precision': 0.9208257120459891, 'recall': 0.9323244616117254, 'f1-score': 0.9265394121049587, 'support': 18899.0} | {'precision': 0.8648200526675119, 'recall': 0.8710216110019646, 'f1-score': 0.8679097538295895, 'support': 10180.0} | 0.8980 | {'precision': 0.8575857957560539, 'recall': 0.7946637446346708, 'f1-score': 0.820914743961573, 'support': 30257.0} | {'precision': 0.8967766387772022, 'recall': 0.8980070727434973, 'f1-score': 0.896759137754772, 'support': 30257.0} |
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+ | No log | 2.0 | 82 | 0.1942 | {'precision': 0.8446771378708552, 'recall': 0.8217317487266553, 'f1-score': 0.8330464716006883, 'support': 1178.0} | {'precision': 0.950406156477127, 'recall': 0.9410021694269538, 'f1-score': 0.9456807848767648, 'support': 18899.0} | {'precision': 0.8897009327819982, 'recall': 0.9088408644400786, 'f1-score': 0.8991690558336167, 'support': 10180.0} | 0.9255 | {'precision': 0.8949280757099934, 'recall': 0.8905249275312292, 'f1-score': 0.89263210410369, 'support': 30257.0} | {'precision': 0.9258654564363231, 'recall': 0.9255378920580362, 'f1-score': 0.925646656486691, 'support': 30257.0} |
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+ | No log | 3.0 | 123 | 0.1832 | {'precision': 0.8074866310160428, 'recall': 0.8972835314091681, 'f1-score': 0.8500201045436268, 'support': 1178.0} | {'precision': 0.942701581540057, 'recall': 0.9619556590295782, 'f1-score': 0.9522313010685104, 'support': 18899.0} | {'precision': 0.9321121804822519, 'recall': 0.8847740667976425, 'f1-score': 0.907826437534647, 'support': 10180.0} | 0.9335 | {'precision': 0.894100131012784, 'recall': 0.9146710857454629, 'f1-score': 0.9033592810489282, 'support': 30257.0} | {'precision': 0.9338744237092824, 'recall': 0.9334699408401361, 'f1-score': 0.9333118344895024, 'support': 30257.0} |
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+ | No log | 4.0 | 164 | 0.1747 | {'precision': 0.8522167487684729, 'recall': 0.8811544991511036, 'f1-score': 0.8664440734557596, 'support': 1178.0} | {'precision': 0.9485603194619588, 'recall': 0.9552357267580295, 'f1-score': 0.9518863198966544, 'support': 18899.0} | {'precision': 0.9159588288198262, 'recall': 0.9003929273084479, 'f1-score': 0.9081091791747165, 'support': 10180.0} | 0.9339 | {'precision': 0.905578632350086, 'recall': 0.912261051072527, 'f1-score': 0.9088131908423769, 'support': 30257.0} | {'precision': 0.9338405554069026, 'recall': 0.9338995934825, 'f1-score': 0.9338309192007261, 'support': 30257.0} |
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+ | No log | 5.0 | 205 | 0.1861 | {'precision': 0.8224085365853658, 'recall': 0.9159592529711376, 'f1-score': 0.8666666666666667, 'support': 1178.0} | {'precision': 0.9393582120155833, 'recall': 0.9696280226467009, 'f1-score': 0.9542531309396725, 'support': 18899.0} | {'precision': 0.9446858111688037, 'recall': 0.8757367387033399, 'f1-score': 0.9089055411123006, 'support': 10180.0} | 0.9359 | {'precision': 0.9021508532565843, 'recall': 0.9204413381070594, 'f1-score': 0.9099417795728799, 'support': 30257.0} | {'precision': 0.9365974704259672, 'recall': 0.9359487060845424, 'f1-score': 0.9355858698312928, 'support': 30257.0} |
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+ | No log | 6.0 | 246 | 0.1963 | {'precision': 0.8229740361919748, 'recall': 0.8879456706281834, 'f1-score': 0.8542262147815436, 'support': 1178.0} | {'precision': 0.962094547029837, 'recall': 0.9401026509339119, 'f1-score': 0.9509714713911042, 'support': 18899.0} | {'precision': 0.897328643407168, 'recall': 0.9272102161100196, 'f1-score': 0.9120247354944683, 'support': 10180.0} | 0.9337 | {'precision': 0.8941324088763266, 'recall': 0.9184195125573716, 'f1-score': 0.9057408072223719, 'support': 30257.0} | {'precision': 0.9348875912627161, 'recall': 0.9337343424662061, 'f1-score': 0.9341012038922174, 'support': 30257.0} |
78
+ | No log | 7.0 | 287 | 0.2315 | {'precision': 0.8133535660091047, 'recall': 0.9100169779286927, 'f1-score': 0.8589743589743589, 'support': 1178.0} | {'precision': 0.9424149252175725, 'recall': 0.9568760251865178, 'f1-score': 0.9495904221802143, 'support': 18899.0} | {'precision': 0.9218461538461539, 'recall': 0.8829076620825147, 'f1-score': 0.9019568489713999, 'support': 10180.0} | 0.9302 | {'precision': 0.8925382150242771, 'recall': 0.9166002217325749, 'f1-score': 0.9035072100419911, 'support': 30257.0} | {'precision': 0.9304697762038363, 'recall': 0.9301649205142611, 'f1-score': 0.9300360877213377, 'support': 30257.0} |
79
+ | No log | 8.0 | 328 | 0.2543 | {'precision': 0.833076923076923, 'recall': 0.9193548387096774, 'f1-score': 0.87409200968523, 'support': 1178.0} | {'precision': 0.9300999293428889, 'recall': 0.9751309593100164, 'f1-score': 0.952083279518508, 'support': 18899.0} | {'precision': 0.9527507382697146, 'recall': 0.8556974459724951, 'f1-score': 0.9016198312891373, 'support': 10180.0} | 0.9328 | {'precision': 0.9053091968965088, 'recall': 0.9167277479973963, 'f1-score': 0.9092650401642918, 'support': 30257.0} | {'precision': 0.9339434079922521, 'recall': 0.9327758865717024, 'f1-score': 0.932068353424097, 'support': 30257.0} |
80
+ | No log | 9.0 | 369 | 0.2367 | {'precision': 0.8409976617303195, 'recall': 0.9159592529711376, 'f1-score': 0.8768793173506705, 'support': 1178.0} | {'precision': 0.9428438661710037, 'recall': 0.9662416000846605, 'f1-score': 0.9543993519220215, 'support': 18899.0} | {'precision': 0.9379554445138455, 'recall': 0.8850687622789783, 'f1-score': 0.9107449711917517, 'support': 10180.0} | 0.9370 | {'precision': 0.9072656574717229, 'recall': 0.9224232051115923, 'f1-score': 0.9140078801548146, 'support': 30257.0} | {'precision': 0.9372339589990767, 'recall': 0.9369732623855637, 'f1-score': 0.9366936905359226, 'support': 30257.0} |
81
+ | No log | 10.0 | 410 | 0.2730 | {'precision': 0.8094170403587444, 'recall': 0.9193548387096774, 'f1-score': 0.8608903020667728, 'support': 1178.0} | {'precision': 0.9393060590367686, 'recall': 0.9597333192232393, 'f1-score': 0.9494098249103614, 'support': 18899.0} | {'precision': 0.9288167343115828, 'recall': 0.8767190569744597, 'f1-score': 0.9020162716660771, 'support': 10180.0} | 0.9302 | {'precision': 0.8925132779023652, 'recall': 0.9186024049691256, 'f1-score': 0.9041054662144038, 'support': 30257.0} | {'precision': 0.9307199272423042, 'recall': 0.9302310209207787, 'f1-score': 0.9300178703234373, 'support': 30257.0} |
82
+ | No log | 11.0 | 451 | 0.2785 | {'precision': 0.8337218337218337, 'recall': 0.9108658743633277, 'f1-score': 0.8705882352941178, 'support': 1178.0} | {'precision': 0.9392393320964749, 'recall': 0.9643367373935129, 'f1-score': 0.9516225883090098, 'support': 18899.0} | {'precision': 0.9336190675308383, 'recall': 0.8773084479371316, 'f1-score': 0.9045882710422363, 'support': 10180.0} | 0.9330 | {'precision': 0.9021934111163823, 'recall': 0.9175036865646574, 'f1-score': 0.9089330315484546, 'support': 30257.0} | {'precision': 0.9332402605968713, 'recall': 0.932974187791255, 'f1-score': 0.9326429202114688, 'support': 30257.0} |
83
+ | No log | 12.0 | 492 | 0.2703 | {'precision': 0.8390894819466248, 'recall': 0.9074702886247877, 'f1-score': 0.871941272430669, 'support': 1178.0} | {'precision': 0.9483742604324834, 'recall': 0.9584104979099424, 'f1-score': 0.9533659666298226, 'support': 18899.0} | {'precision': 0.924524484014569, 'recall': 0.8976424361493124, 'f1-score': 0.9108851674641149, 'support': 10180.0} | 0.9360 | {'precision': 0.903996075464559, 'recall': 0.9211744075613475, 'f1-score': 0.9120641355082021, 'support': 30257.0} | {'precision': 0.9360951781377842, 'recall': 0.9359817562878012, 'f1-score': 0.9359031373581331, 'support': 30257.0} |
84
+ | 0.1317 | 13.0 | 533 | 0.2982 | {'precision': 0.8402832415420929, 'recall': 0.9066213921901528, 'f1-score': 0.8721927317272357, 'support': 1178.0} | {'precision': 0.9399876263147041, 'recall': 0.9647071273612361, 'f1-score': 0.952186969578274, 'support': 18899.0} | {'precision': 0.9331595411887382, 'recall': 0.8790766208251474, 'f1-score': 0.9053110773899848, 'support': 10180.0} | 0.9336 | {'precision': 0.9044768030151783, 'recall': 0.9168017134588454, 'f1-score': 0.9098969262318315, 'support': 30257.0} | {'precision': 0.9338085050586488, 'recall': 0.93363519185643, 'f1-score': 0.9333010987164798, 'support': 30257.0} |
85
+ | 0.1317 | 14.0 | 574 | 0.3190 | {'precision': 0.827559661277906, 'recall': 0.9125636672325976, 'f1-score': 0.8679854662898667, 'support': 1178.0} | {'precision': 0.938201668554949, 'recall': 0.9639663474257897, 'f1-score': 0.9509095179685257, 'support': 18899.0} | {'precision': 0.9335429769392034, 'recall': 0.8748526522593321, 'f1-score': 0.9032454361054767, 'support': 10180.0} | 0.9320 | {'precision': 0.8997681022573528, 'recall': 0.9171275556392398, 'f1-score': 0.9073801401212896, 'support': 30257.0} | {'precision': 0.9323266060827724, 'recall': 0.9319826816934924, 'f1-score': 0.9316443929976661, 'support': 30257.0} |
86
+ | 0.1317 | 15.0 | 615 | 0.3058 | {'precision': 0.8361934477379095, 'recall': 0.9100169779286927, 'f1-score': 0.8715447154471545, 'support': 1178.0} | {'precision': 0.9371953409615681, 'recall': 0.9664532514947881, 'f1-score': 0.9515994581640096, 'support': 18899.0} | {'precision': 0.9362218005481763, 'recall': 0.8723968565815324, 'f1-score': 0.9031831587511441, 'support': 10180.0} | 0.9326 | {'precision': 0.9032035297492179, 'recall': 0.9162890286683378, 'f1-score': 0.9087757774541028, 'support': 30257.0} | {'precision': 0.932935471456138, 'recall': 0.9326106355554087, 'f1-score': 0.9321929600001657, 'support': 30257.0} |
87
+ | 0.1317 | 16.0 | 656 | 0.2971 | {'precision': 0.8351477449455676, 'recall': 0.9117147707979627, 'f1-score': 0.8717532467532468, 'support': 1178.0} | {'precision': 0.9428615911567804, 'recall': 0.9613207047991957, 'f1-score': 0.9520016767973171, 'support': 18899.0} | {'precision': 0.9285714285714286, 'recall': 0.8849705304518664, 'f1-score': 0.9062468564530731, 'support': 10180.0} | 0.9337 | {'precision': 0.9021935882245922, 'recall': 0.9193353353496749, 'f1-score': 0.9100005933345456, 'support': 30257.0} | {'precision': 0.9338600124822359, 'recall': 0.9337012922629474, 'f1-score': 0.9334830952559773, 'support': 30257.0} |
88
 
89
 
90
  ### Framework versions
meta_data/README_s42_e16.md CHANGED
@@ -17,12 +17,12 @@ model-index:
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  name: essays_su_g
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  type: essays_su_g
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  config: spans
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- split: train[0%:20%]
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  args: spans
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9266721210881571
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  ---
27
 
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  <!-- 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: 0.4247
36
- - B: {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0}
37
- - I: {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0}
38
- - O: {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0}
39
- - Accuracy: 0.9267
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- - Macro avg: {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0}
41
- - Weighted avg: {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0}
42
 
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  ## Model description
44
 
@@ -67,24 +67,24 @@ The following hyperparameters were used during training:
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  ### Training results
69
 
70
- | Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
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- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
72
- | No log | 1.0 | 41 | 0.3607 | {'precision': 0.8202247191011236, 'recall': 0.19329214474845544, 'f1-score': 0.3128571428571429, 'support': 1133.0} | {'precision': 0.8412101850981866, 'recall': 0.9767086674303169, 'f1-score': 0.9039097402761301, 'support': 18333.0} | {'precision': 0.9249453797712376, 'recall': 0.7293271179570329, 'f1-score': 0.8155702872684006, 'support': 9868.0} | 0.8632 | {'precision': 0.8621267613235158, 'recall': 0.6331093100452684, 'f1-score': 0.6774457234672245, 'support': 29334.0} | {'precision': 0.8685682804162133, 'recall': 0.8632303811277017, 'f1-score': 0.8513633328596172, 'support': 29334.0} |
73
- | No log | 2.0 | 82 | 0.2532 | {'precision': 0.7996755879967559, 'recall': 0.8702559576345984, 'f1-score': 0.8334742180896026, 'support': 1133.0} | {'precision': 0.9331675137882557, 'recall': 0.9413625702285496, 'f1-score': 0.937247128465528, 'support': 18333.0} | {'precision': 0.8902883314250026, 'recall': 0.8667409809485205, 'f1-score': 0.878356867779204, 'support': 9868.0} | 0.9135 | {'precision': 0.874377144403338, 'recall': 0.892786502937223, 'f1-score': 0.8830260714447782, 'support': 29334.0} | {'precision': 0.9135868864110707, 'recall': 0.9135133292425173, 'f1-score': 0.9134282220801536, 'support': 29334.0} |
74
- | No log | 3.0 | 123 | 0.2280 | {'precision': 0.8041074249605056, 'recall': 0.8984995586937334, 'f1-score': 0.8486869528970404, 'support': 1133.0} | {'precision': 0.9231209660628774, 'recall': 0.9673812251131839, 'f1-score': 0.9447329870821681, 'support': 18333.0} | {'precision': 0.9356368563685636, 'recall': 0.8396838265099311, 'f1-score': 0.8850672933133945, 'support': 9868.0} | 0.9218 | {'precision': 0.8876217491306488, 'recall': 0.9018548701056162, 'f1-score': 0.8928290777642011, 'support': 29334.0} | {'precision': 0.9227345360999514, 'recall': 0.9217631417467785, 'f1-score': 0.9209516676970857, 'support': 29334.0} |
75
- | No log | 4.0 | 164 | 0.2643 | {'precision': 0.8111111111111111, 'recall': 0.9020300088261254, 'f1-score': 0.854157960718763, 'support': 1133.0} | {'precision': 0.9180539091893006, 'recall': 0.9716358479245077, 'f1-score': 0.9440852236591055, 'support': 18333.0} | {'precision': 0.9426825049013955, 'recall': 0.8283340089177138, 'f1-score': 0.8818167107179459, 'support': 9868.0} | 0.9207 | {'precision': 0.8906158417339357, 'recall': 0.9006666218894489, 'f1-score': 0.8933532983652714, 'support': 29334.0} | {'precision': 0.9222084326864153, 'recall': 0.9207404377173246, 'f1-score': 0.9196646443104053, 'support': 29334.0} |
76
- | No log | 5.0 | 205 | 0.2475 | {'precision': 0.8158526821457166, 'recall': 0.8993821712268314, 'f1-score': 0.8555835432409741, 'support': 1133.0} | {'precision': 0.9278074866310161, 'recall': 0.965308460153821, 'f1-score': 0.9461865426257119, 'support': 18333.0} | {'precision': 0.9316391077571856, 'recall': 0.8507296311309283, 'f1-score': 0.8893479527517347, 'support': 9868.0} | 0.9242 | {'precision': 0.8917664255113061, 'recall': 0.9051400875038601, 'f1-score': 0.8970393462061402, 'support': 29334.0} | {'precision': 0.924772293469197, 'recall': 0.9242176314174678, 'f1-score': 0.9235664975183513, 'support': 29334.0} |
77
- | No log | 6.0 | 246 | 0.2554 | {'precision': 0.8058176100628931, 'recall': 0.9046778464254193, 'f1-score': 0.8523908523908524, 'support': 1133.0} | {'precision': 0.9404408990459764, 'recall': 0.951726395025364, 'f1-score': 0.946049991866833, 'support': 18333.0} | {'precision': 0.9114523083394679, 'recall': 0.8782934738548844, 'f1-score': 0.894565722248026, 'support': 9868.0} | 0.9252 | {'precision': 0.8859036058161124, 'recall': 0.9115659051018893, 'f1-score': 0.8976688555019038, 'support': 29334.0} | {'precision': 0.9254893888697421, 'recall': 0.9252062453126065, 'f1-score': 0.9251131071042821, 'support': 29334.0} |
78
- | No log | 7.0 | 287 | 0.2822 | {'precision': 0.8323746918652424, 'recall': 0.8940864960282436, 'f1-score': 0.8621276595744681, 'support': 1133.0} | {'precision': 0.9353455123113582, 'recall': 0.9635084274259532, 'f1-score': 0.9492181202643882, 'support': 18333.0} | {'precision': 0.9287261698440208, 'recall': 0.8688690717470612, 'f1-score': 0.8978010471204189, 'support': 9868.0} | 0.9290 | {'precision': 0.8988154580068738, 'recall': 0.9088213317337527, 'f1-score': 0.9030489423197584, 'support': 29334.0} | {'precision': 0.9291415983878176, 'recall': 0.9289902502215859, 'f1-score': 0.9285575499450874, 'support': 29334.0} |
79
- | No log | 8.0 | 328 | 0.3068 | {'precision': 0.8181089743589743, 'recall': 0.9011473962930273, 'f1-score': 0.8576228475430492, 'support': 1133.0} | {'precision': 0.933372111469515, 'recall': 0.9627993236240658, 'f1-score': 0.9478573729996776, 'support': 18333.0} | {'precision': 0.9279564032697548, 'recall': 0.8627888123226591, 'f1-score': 0.8941868403087749, 'support': 9868.0} | 0.9268 | {'precision': 0.8931458296994147, 'recall': 0.9089118440799174, 'f1-score': 0.899889020283834, 'support': 29334.0} | {'precision': 0.9270983219126366, 'recall': 0.9267743914911025, 'f1-score': 0.926317298889901, 'support': 29334.0} |
80
- | No log | 9.0 | 369 | 0.3574 | {'precision': 0.8315441783649876, 'recall': 0.8887908208296558, 'f1-score': 0.8592150170648465, 'support': 1133.0} | {'precision': 0.9180683108038387, 'recall': 0.9705994654448262, 'f1-score': 0.9436033408458174, 'support': 18333.0} | {'precision': 0.9387941883079739, 'recall': 0.8315768139440616, 'f1-score': 0.8819388467945618, 'support': 9868.0} | 0.9207 | {'precision': 0.8961355591589334, 'recall': 0.8969890334061811, 'f1-score': 0.8949190682350753, 'support': 29334.0} | {'precision': 0.9216986072911089, 'recall': 0.9206722574486943, 'f1-score': 0.9195998909875767, 'support': 29334.0} |
81
- | No log | 10.0 | 410 | 0.3228 | {'precision': 0.8491048593350383, 'recall': 0.8790820829655781, 'f1-score': 0.8638334778837814, 'support': 1133.0} | {'precision': 0.9479900314226893, 'recall': 0.9544537173403153, 'f1-score': 0.9512108939686336, 'support': 18333.0} | {'precision': 0.9123982273523652, 'recall': 0.897142278070531, 'f1-score': 0.9047059424658934, 'support': 9868.0} | 0.9323 | {'precision': 0.9031643727033641, 'recall': 0.9102260261254749, 'f1-score': 0.9065834381061029, 'support': 29334.0} | {'precision': 0.9321975441198576, 'recall': 0.9322629031158383, 'f1-score': 0.9321916850692957, 'support': 29334.0} |
82
- | No log | 11.0 | 451 | 0.3397 | {'precision': 0.8524871355060034, 'recall': 0.8773168578993822, 'f1-score': 0.8647237929534581, 'support': 1133.0} | {'precision': 0.941372096765542, 'recall': 0.9572901325478645, 'f1-score': 0.9492643877109477, 'support': 18333.0} | {'precision': 0.9164304461942258, 'recall': 0.8845764085934333, 'f1-score': 0.9002217294900223, 'support': 9868.0} | 0.9297 | {'precision': 0.9034298928219237, 'recall': 0.9063944663468934, 'f1-score': 0.9047366367181428, 'support': 29334.0} | {'precision': 0.9295485858585807, 'recall': 0.9297402331765187, 'f1-score': 0.9295010603371041, 'support': 29334.0} |
83
- | No log | 12.0 | 492 | 0.3769 | {'precision': 0.8406040268456376, 'recall': 0.884377758164166, 'f1-score': 0.8619354838709679, 'support': 1133.0} | {'precision': 0.9364758459246648, 'recall': 0.9601265477554137, 'f1-score': 0.9481537342777884, 'support': 18333.0} | {'precision': 0.9215707254440403, 'recall': 0.8728212403729225, 'f1-score': 0.8965337774539398, 'support': 9868.0} | 0.9278 | {'precision': 0.8995501994047809, 'recall': 0.9057751820975007, 'f1-score': 0.9022076652008987, 'support': 29334.0} | {'precision': 0.9277587769971628, 'recall': 0.9278311856548714, 'f1-score': 0.927458601951864, 'support': 29334.0} |
84
- | 0.1199 | 13.0 | 533 | 0.4395 | {'precision': 0.8141945773524721, 'recall': 0.9011473962930273, 'f1-score': 0.8554671135316297, 'support': 1133.0} | {'precision': 0.9200891931134619, 'recall': 0.967817596683576, 'f1-score': 0.9433500810803624, 'support': 18333.0} | {'precision': 0.9357662573897226, 'recall': 0.8341102553708958, 'f1-score': 0.8820188598371196, 'support': 9868.0} | 0.9203 | {'precision': 0.8900166759518856, 'recall': 0.9010250827824997, 'f1-score': 0.8936120181497039, 'support': 29334.0} | {'precision': 0.9212728936187097, 'recall': 0.9202631758369128, 'f1-score': 0.9193237671285989, 'support': 29334.0} |
85
- | 0.1199 | 14.0 | 574 | 0.4362 | {'precision': 0.8338842975206612, 'recall': 0.8905560458958517, 'f1-score': 0.861288945795988, 'support': 1133.0} | {'precision': 0.9288525106249016, 'recall': 0.9656357388316151, 'f1-score': 0.9468870346598203, 'support': 18333.0} | {'precision': 0.9307225592939878, 'recall': 0.8549858127280098, 'f1-score': 0.8912480853536153, 'support': 9868.0} | 0.9255 | {'precision': 0.8978197891465168, 'recall': 0.9037258658184922, 'f1-score': 0.8998080219364746, 'support': 29334.0} | {'precision': 0.9258135338341278, 'recall': 0.9255130565214427, 'f1-score': 0.9248638606488995, 'support': 29334.0} |
86
- | 0.1199 | 15.0 | 615 | 0.4385 | {'precision': 0.8272208638956805, 'recall': 0.8958517210944396, 'f1-score': 0.8601694915254238, 'support': 1133.0} | {'precision': 0.9282784730255548, 'recall': 0.9629629629629629, 'f1-score': 0.9453026692725763, 'support': 18333.0} | {'precision': 0.9263945428539994, 'recall': 0.8532630725577625, 'f1-score': 0.8883262119533682, 'support': 9868.0} | 0.9235 | {'precision': 0.8939646265917448, 'recall': 0.9040259188717217, 'f1-score': 0.8979327909171229, 'support': 29334.0} | {'precision': 0.923741454750616, 'recall': 0.9234676484625349, 'f1-score': 0.9228475124165911, 'support': 29334.0} |
87
- | 0.1199 | 16.0 | 656 | 0.4247 | {'precision': 0.8405063291139241, 'recall': 0.8790820829655781, 'f1-score': 0.8593615185504745, 'support': 1133.0} | {'precision': 0.9346315063405316, 'recall': 0.960835651557301, 'f1-score': 0.9475524475524475, 'support': 18333.0} | {'precision': 0.9215222532788647, 'recall': 0.8686663964329144, 'f1-score': 0.8943140323422013, 'support': 9868.0} | 0.9267 | {'precision': 0.8988866962444403, 'recall': 0.9028613769852646, 'f1-score': 0.9004093328150411, 'support': 29334.0} | {'precision': 0.9265860323168638, 'recall': 0.9266721210881571, 'f1-score': 0.926236670506905, 'support': 29334.0} |
88
 
89
 
90
  ### Framework versions
 
17
  name: essays_su_g
18
  type: essays_su_g
19
  config: spans
20
+ split: train[20%:40%]
21
  args: spans
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.9337012922629474
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: 0.2971
36
+ - B: {'precision': 0.8351477449455676, 'recall': 0.9117147707979627, 'f1-score': 0.8717532467532468, 'support': 1178.0}
37
+ - I: {'precision': 0.9428615911567804, 'recall': 0.9613207047991957, 'f1-score': 0.9520016767973171, 'support': 18899.0}
38
+ - O: {'precision': 0.9285714285714286, 'recall': 0.8849705304518664, 'f1-score': 0.9062468564530731, 'support': 10180.0}
39
+ - Accuracy: 0.9337
40
+ - Macro avg: {'precision': 0.9021935882245922, 'recall': 0.9193353353496749, 'f1-score': 0.9100005933345456, 'support': 30257.0}
41
+ - Weighted avg: {'precision': 0.9338600124822359, 'recall': 0.9337012922629474, 'f1-score': 0.9334830952559773, 'support': 30257.0}
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 | 0.2806 | {'precision': 0.7871116225546605, 'recall': 0.5806451612903226, 'f1-score': 0.668295065950171, 'support': 1178.0} | {'precision': 0.9208257120459891, 'recall': 0.9323244616117254, 'f1-score': 0.9265394121049587, 'support': 18899.0} | {'precision': 0.8648200526675119, 'recall': 0.8710216110019646, 'f1-score': 0.8679097538295895, 'support': 10180.0} | 0.8980 | {'precision': 0.8575857957560539, 'recall': 0.7946637446346708, 'f1-score': 0.820914743961573, 'support': 30257.0} | {'precision': 0.8967766387772022, 'recall': 0.8980070727434973, 'f1-score': 0.896759137754772, 'support': 30257.0} |
73
+ | No log | 2.0 | 82 | 0.1942 | {'precision': 0.8446771378708552, 'recall': 0.8217317487266553, 'f1-score': 0.8330464716006883, 'support': 1178.0} | {'precision': 0.950406156477127, 'recall': 0.9410021694269538, 'f1-score': 0.9456807848767648, 'support': 18899.0} | {'precision': 0.8897009327819982, 'recall': 0.9088408644400786, 'f1-score': 0.8991690558336167, 'support': 10180.0} | 0.9255 | {'precision': 0.8949280757099934, 'recall': 0.8905249275312292, 'f1-score': 0.89263210410369, 'support': 30257.0} | {'precision': 0.9258654564363231, 'recall': 0.9255378920580362, 'f1-score': 0.925646656486691, 'support': 30257.0} |
74
+ | No log | 3.0 | 123 | 0.1832 | {'precision': 0.8074866310160428, 'recall': 0.8972835314091681, 'f1-score': 0.8500201045436268, 'support': 1178.0} | {'precision': 0.942701581540057, 'recall': 0.9619556590295782, 'f1-score': 0.9522313010685104, 'support': 18899.0} | {'precision': 0.9321121804822519, 'recall': 0.8847740667976425, 'f1-score': 0.907826437534647, 'support': 10180.0} | 0.9335 | {'precision': 0.894100131012784, 'recall': 0.9146710857454629, 'f1-score': 0.9033592810489282, 'support': 30257.0} | {'precision': 0.9338744237092824, 'recall': 0.9334699408401361, 'f1-score': 0.9333118344895024, 'support': 30257.0} |
75
+ | No log | 4.0 | 164 | 0.1747 | {'precision': 0.8522167487684729, 'recall': 0.8811544991511036, 'f1-score': 0.8664440734557596, 'support': 1178.0} | {'precision': 0.9485603194619588, 'recall': 0.9552357267580295, 'f1-score': 0.9518863198966544, 'support': 18899.0} | {'precision': 0.9159588288198262, 'recall': 0.9003929273084479, 'f1-score': 0.9081091791747165, 'support': 10180.0} | 0.9339 | {'precision': 0.905578632350086, 'recall': 0.912261051072527, 'f1-score': 0.9088131908423769, 'support': 30257.0} | {'precision': 0.9338405554069026, 'recall': 0.9338995934825, 'f1-score': 0.9338309192007261, 'support': 30257.0} |
76
+ | No log | 5.0 | 205 | 0.1861 | {'precision': 0.8224085365853658, 'recall': 0.9159592529711376, 'f1-score': 0.8666666666666667, 'support': 1178.0} | {'precision': 0.9393582120155833, 'recall': 0.9696280226467009, 'f1-score': 0.9542531309396725, 'support': 18899.0} | {'precision': 0.9446858111688037, 'recall': 0.8757367387033399, 'f1-score': 0.9089055411123006, 'support': 10180.0} | 0.9359 | {'precision': 0.9021508532565843, 'recall': 0.9204413381070594, 'f1-score': 0.9099417795728799, 'support': 30257.0} | {'precision': 0.9365974704259672, 'recall': 0.9359487060845424, 'f1-score': 0.9355858698312928, 'support': 30257.0} |
77
+ | No log | 6.0 | 246 | 0.1963 | {'precision': 0.8229740361919748, 'recall': 0.8879456706281834, 'f1-score': 0.8542262147815436, 'support': 1178.0} | {'precision': 0.962094547029837, 'recall': 0.9401026509339119, 'f1-score': 0.9509714713911042, 'support': 18899.0} | {'precision': 0.897328643407168, 'recall': 0.9272102161100196, 'f1-score': 0.9120247354944683, 'support': 10180.0} | 0.9337 | {'precision': 0.8941324088763266, 'recall': 0.9184195125573716, 'f1-score': 0.9057408072223719, 'support': 30257.0} | {'precision': 0.9348875912627161, 'recall': 0.9337343424662061, 'f1-score': 0.9341012038922174, 'support': 30257.0} |
78
+ | No log | 7.0 | 287 | 0.2315 | {'precision': 0.8133535660091047, 'recall': 0.9100169779286927, 'f1-score': 0.8589743589743589, 'support': 1178.0} | {'precision': 0.9424149252175725, 'recall': 0.9568760251865178, 'f1-score': 0.9495904221802143, 'support': 18899.0} | {'precision': 0.9218461538461539, 'recall': 0.8829076620825147, 'f1-score': 0.9019568489713999, 'support': 10180.0} | 0.9302 | {'precision': 0.8925382150242771, 'recall': 0.9166002217325749, 'f1-score': 0.9035072100419911, 'support': 30257.0} | {'precision': 0.9304697762038363, 'recall': 0.9301649205142611, 'f1-score': 0.9300360877213377, 'support': 30257.0} |
79
+ | No log | 8.0 | 328 | 0.2543 | {'precision': 0.833076923076923, 'recall': 0.9193548387096774, 'f1-score': 0.87409200968523, 'support': 1178.0} | {'precision': 0.9300999293428889, 'recall': 0.9751309593100164, 'f1-score': 0.952083279518508, 'support': 18899.0} | {'precision': 0.9527507382697146, 'recall': 0.8556974459724951, 'f1-score': 0.9016198312891373, 'support': 10180.0} | 0.9328 | {'precision': 0.9053091968965088, 'recall': 0.9167277479973963, 'f1-score': 0.9092650401642918, 'support': 30257.0} | {'precision': 0.9339434079922521, 'recall': 0.9327758865717024, 'f1-score': 0.932068353424097, 'support': 30257.0} |
80
+ | No log | 9.0 | 369 | 0.2367 | {'precision': 0.8409976617303195, 'recall': 0.9159592529711376, 'f1-score': 0.8768793173506705, 'support': 1178.0} | {'precision': 0.9428438661710037, 'recall': 0.9662416000846605, 'f1-score': 0.9543993519220215, 'support': 18899.0} | {'precision': 0.9379554445138455, 'recall': 0.8850687622789783, 'f1-score': 0.9107449711917517, 'support': 10180.0} | 0.9370 | {'precision': 0.9072656574717229, 'recall': 0.9224232051115923, 'f1-score': 0.9140078801548146, 'support': 30257.0} | {'precision': 0.9372339589990767, 'recall': 0.9369732623855637, 'f1-score': 0.9366936905359226, 'support': 30257.0} |
81
+ | No log | 10.0 | 410 | 0.2730 | {'precision': 0.8094170403587444, 'recall': 0.9193548387096774, 'f1-score': 0.8608903020667728, 'support': 1178.0} | {'precision': 0.9393060590367686, 'recall': 0.9597333192232393, 'f1-score': 0.9494098249103614, 'support': 18899.0} | {'precision': 0.9288167343115828, 'recall': 0.8767190569744597, 'f1-score': 0.9020162716660771, 'support': 10180.0} | 0.9302 | {'precision': 0.8925132779023652, 'recall': 0.9186024049691256, 'f1-score': 0.9041054662144038, 'support': 30257.0} | {'precision': 0.9307199272423042, 'recall': 0.9302310209207787, 'f1-score': 0.9300178703234373, 'support': 30257.0} |
82
+ | No log | 11.0 | 451 | 0.2785 | {'precision': 0.8337218337218337, 'recall': 0.9108658743633277, 'f1-score': 0.8705882352941178, 'support': 1178.0} | {'precision': 0.9392393320964749, 'recall': 0.9643367373935129, 'f1-score': 0.9516225883090098, 'support': 18899.0} | {'precision': 0.9336190675308383, 'recall': 0.8773084479371316, 'f1-score': 0.9045882710422363, 'support': 10180.0} | 0.9330 | {'precision': 0.9021934111163823, 'recall': 0.9175036865646574, 'f1-score': 0.9089330315484546, 'support': 30257.0} | {'precision': 0.9332402605968713, 'recall': 0.932974187791255, 'f1-score': 0.9326429202114688, 'support': 30257.0} |
83
+ | No log | 12.0 | 492 | 0.2703 | {'precision': 0.8390894819466248, 'recall': 0.9074702886247877, 'f1-score': 0.871941272430669, 'support': 1178.0} | {'precision': 0.9483742604324834, 'recall': 0.9584104979099424, 'f1-score': 0.9533659666298226, 'support': 18899.0} | {'precision': 0.924524484014569, 'recall': 0.8976424361493124, 'f1-score': 0.9108851674641149, 'support': 10180.0} | 0.9360 | {'precision': 0.903996075464559, 'recall': 0.9211744075613475, 'f1-score': 0.9120641355082021, 'support': 30257.0} | {'precision': 0.9360951781377842, 'recall': 0.9359817562878012, 'f1-score': 0.9359031373581331, 'support': 30257.0} |
84
+ | 0.1317 | 13.0 | 533 | 0.2982 | {'precision': 0.8402832415420929, 'recall': 0.9066213921901528, 'f1-score': 0.8721927317272357, 'support': 1178.0} | {'precision': 0.9399876263147041, 'recall': 0.9647071273612361, 'f1-score': 0.952186969578274, 'support': 18899.0} | {'precision': 0.9331595411887382, 'recall': 0.8790766208251474, 'f1-score': 0.9053110773899848, 'support': 10180.0} | 0.9336 | {'precision': 0.9044768030151783, 'recall': 0.9168017134588454, 'f1-score': 0.9098969262318315, 'support': 30257.0} | {'precision': 0.9338085050586488, 'recall': 0.93363519185643, 'f1-score': 0.9333010987164798, 'support': 30257.0} |
85
+ | 0.1317 | 14.0 | 574 | 0.3190 | {'precision': 0.827559661277906, 'recall': 0.9125636672325976, 'f1-score': 0.8679854662898667, 'support': 1178.0} | {'precision': 0.938201668554949, 'recall': 0.9639663474257897, 'f1-score': 0.9509095179685257, 'support': 18899.0} | {'precision': 0.9335429769392034, 'recall': 0.8748526522593321, 'f1-score': 0.9032454361054767, 'support': 10180.0} | 0.9320 | {'precision': 0.8997681022573528, 'recall': 0.9171275556392398, 'f1-score': 0.9073801401212896, 'support': 30257.0} | {'precision': 0.9323266060827724, 'recall': 0.9319826816934924, 'f1-score': 0.9316443929976661, 'support': 30257.0} |
86
+ | 0.1317 | 15.0 | 615 | 0.3058 | {'precision': 0.8361934477379095, 'recall': 0.9100169779286927, 'f1-score': 0.8715447154471545, 'support': 1178.0} | {'precision': 0.9371953409615681, 'recall': 0.9664532514947881, 'f1-score': 0.9515994581640096, 'support': 18899.0} | {'precision': 0.9362218005481763, 'recall': 0.8723968565815324, 'f1-score': 0.9031831587511441, 'support': 10180.0} | 0.9326 | {'precision': 0.9032035297492179, 'recall': 0.9162890286683378, 'f1-score': 0.9087757774541028, 'support': 30257.0} | {'precision': 0.932935471456138, 'recall': 0.9326106355554087, 'f1-score': 0.9321929600001657, 'support': 30257.0} |
87
+ | 0.1317 | 16.0 | 656 | 0.2971 | {'precision': 0.8351477449455676, 'recall': 0.9117147707979627, 'f1-score': 0.8717532467532468, 'support': 1178.0} | {'precision': 0.9428615911567804, 'recall': 0.9613207047991957, 'f1-score': 0.9520016767973171, 'support': 18899.0} | {'precision': 0.9285714285714286, 'recall': 0.8849705304518664, 'f1-score': 0.9062468564530731, 'support': 10180.0} | 0.9337 | {'precision': 0.9021935882245922, 'recall': 0.9193353353496749, 'f1-score': 0.9100005933345456, 'support': 30257.0} | {'precision': 0.9338600124822359, 'recall': 0.9337012922629474, 'f1-score': 0.9334830952559773, 'support': 30257.0} |
88
 
89
 
90
  ### Framework versions
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