trainer: training complete at 2024-03-02 12:59:22.037773.

README.md

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: simple
-      split: train[80%:100%]
+      split: train[0%:20%]
       args: simple
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8436583511350881
+      value: 0.8390263857639599
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,14 +32,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 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.
 It achieves the following results on the evaluation set:
-- Loss: 0.6438
-- Claim: {'precision': 0.6039084842707341, 'recall': 0.6079654510556622, 'f1-score': 0.6059301769488283, 'support': 4168.0}
-- Majorclaim: {'precision': 0.7791218637992832, 'recall': 0.8080855018587361, 'f1-score': 0.7933394160583942, 'support': 2152.0}
-- O: {'precision': 0.936604624929498, 'recall': 0.8999566442662043, 'f1-score': 0.9179149853518324, 'support': 9226.0}
-- Premise: {'precision': 0.8701119584617881, 'recall': 0.8883458958005467, 'f1-score': 0.8791343907537195, 'support': 12073.0}
-- Accuracy: 0.8437
-- Macro avg: {'precision': 0.7974367328653259, 'recall': 0.8010883732452874, 'f1-score': 0.7990797422781937, 'support': 27619.0}
-- Weighted avg: {'precision': 0.8450608913228282, 'recall': 0.8436583511350881, 'f1-score': 0.8441745376482147, 'support': 27619.0}
+- Loss: 0.7323
+- Claim: {'precision': 0.5949216896060749, 'recall': 0.5882214922571563, 'f1-score': 0.5915526191599811, 'support': 4262.0}
+- Majorclaim: {'precision': 0.8048654244306418, 'recall': 0.7182448036951501, 'f1-score': 0.7590920185501586, 'support': 2165.0}
+- O: {'precision': 0.9059811340313051, 'recall': 0.8856911228212404, 'f1-score': 0.8957212400717396, 'support': 9868.0}
+- Premise: {'precision': 0.8721660143268591, 'recall': 0.9057443055449037, 'f1-score': 0.8886380737396539, 'support': 13039.0}
+- Accuracy: 0.8390
+- Macro avg: {'precision': 0.7944835655987202, 'recall': 0.7744754310796127, 'f1-score': 0.7837509878803832, 'support': 29334.0}
+- Weighted avg: {'precision': 0.8382929152663212, 'recall': 0.8390263857639599, 'f1-score': 0.8382955111317995, 'support': 29334.0}
 
 ## Model description
 
@@ -64,27 +64,28 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 15
+- num_epochs: 16
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                              | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.5691          | {'precision': 0.4949392712550607, 'recall': 0.23464491362763915, 'f1-score': 0.318359375, 'support': 4168.0}       | {'precision': 0.5329815303430079, 'recall': 0.6570631970260223, 'f1-score': 0.5885535900104059, 'support': 2152.0} | {'precision': 0.919937015503876, 'recall': 0.8232169954476479, 'f1-score': 0.86889371925409, 'support': 9226.0}    | {'precision': 0.7775213791231166, 'recall': 0.9488942267870455, 'f1-score': 0.8547021300406611, 'support': 12073.0} | 0.7764   | {'precision': 0.6813447990562653, 'recall': 0.6659548332220888, 'f1-score': 0.6576272035762892, 'support': 27619.0} | {'precision': 0.7633961277048913, 'recall': 0.7763858213548644, 'f1-score': 0.7577653597350205, 'support': 27619.0} |
-| No log        | 2.0   | 82   | 0.4424          | {'precision': 0.6061801446416831, 'recall': 0.44241842610364684, 'f1-score': 0.511511789181692, 'support': 4168.0} | {'precision': 0.6986357999173212, 'recall': 0.7853159851301115, 'f1-score': 0.7394443229052724, 'support': 2152.0} | {'precision': 0.9271515569343904, 'recall': 0.8745935399956645, 'f1-score': 0.900105973562385, 'support': 9226.0}  | {'precision': 0.8290598290598291, 'recall': 0.9239625610867225, 'f1-score': 0.8739423378251332, 'support': 12073.0} | 0.8240   | {'precision': 0.765256832638306, 'recall': 0.7565726280790362, 'f1-score': 0.7562511058686207, 'support': 27619.0}  | {'precision': 0.818029713776915, 'recall': 0.8239979724102973, 'f1-score': 0.8175078343477619, 'support': 27619.0}  |
-| No log        | 3.0   | 123  | 0.4282          | {'precision': 0.5578358208955224, 'recall': 0.6456333973128598, 'f1-score': 0.598532028469751, 'support': 4168.0}  | {'precision': 0.7531854648419065, 'recall': 0.741635687732342, 'f1-score': 0.74736595645048, 'support': 2152.0}    | {'precision': 0.9484389782403028, 'recall': 0.8692824626056797, 'f1-score': 0.9071372016740188, 'support': 9226.0} | {'precision': 0.872013093289689, 'recall': 0.8826306634639278, 'f1-score': 0.8772897542501955, 'support': 12073.0}  | 0.8314   | {'precision': 0.7828683393168552, 'recall': 0.7847955527787023, 'f1-score': 0.7825812352111113, 'support': 27619.0} | {'precision': 0.8408713896362565, 'recall': 0.831420399000688, 'f1-score': 0.835069338449997, 'support': 27619.0}   |
-| No log        | 4.0   | 164  | 0.4201          | {'precision': 0.6498756218905473, 'recall': 0.5014395393474088, 'f1-score': 0.566088840736728, 'support': 4168.0}  | {'precision': 0.781447963800905, 'recall': 0.8025092936802974, 'f1-score': 0.7918386061439706, 'support': 2152.0}  | {'precision': 0.9166757197175448, 'recall': 0.9145892044222849, 'f1-score': 0.9156312733980794, 'support': 9226.0} | {'precision': 0.8523252232830305, 'recall': 0.9169220574836412, 'f1-score': 0.8834443956745541, 'support': 12073.0} | 0.8445   | {'precision': 0.8000811321730068, 'recall': 0.7838650237334082, 'f1-score': 0.7892507789883332, 'support': 27619.0} | {'precision': 0.8377468489427367, 'recall': 0.8445273181505485, 'f1-score': 0.839166272709442, 'support': 27619.0}  |
-| No log        | 5.0   | 205  | 0.4511          | {'precision': 0.5708701913186587, 'recall': 0.6657869481765835, 'f1-score': 0.614686011739949, 'support': 4168.0}  | {'precision': 0.7117988394584139, 'recall': 0.8550185873605948, 'f1-score': 0.7768629934557737, 'support': 2152.0} | {'precision': 0.9312506998096518, 'recall': 0.9014740949490571, 'f1-score': 0.916120504488627, 'support': 9226.0}  | {'precision': 0.9057107276285359, 'recall': 0.8433695021949805, 'f1-score': 0.8734291228822647, 'support': 12073.0} | 0.8369   | {'precision': 0.7799076145538151, 'recall': 0.816412283170304, 'f1-score': 0.7952746581416535, 'support': 27619.0}  | {'precision': 0.8486021445756123, 'recall': 0.8368876498062928, 'f1-score': 0.8411187238429554, 'support': 27619.0} |
-| No log        | 6.0   | 246  | 0.4568          | {'precision': 0.5821080969144751, 'recall': 0.6744241842610365, 'f1-score': 0.6248749583194397, 'support': 4168.0} | {'precision': 0.7789237668161435, 'recall': 0.8071561338289963, 'f1-score': 0.7927886809675947, 'support': 2152.0} | {'precision': 0.9134450171821306, 'recall': 0.9219596791675699, 'f1-score': 0.9176825979070017, 'support': 9226.0} | {'precision': 0.9038051209103841, 'recall': 0.8420442309285182, 'f1-score': 0.8718322541915012, 'support': 12073.0} | 0.8407   | {'precision': 0.7945705004557834, 'recall': 0.8113960570465302, 'f1-score': 0.8017946228463844, 'support': 27619.0} | {'precision': 0.8487473640392945, 'recall': 0.8407255874579094, 'f1-score': 0.8437210080329368, 'support': 27619.0} |
-| No log        | 7.0   | 287  | 0.5084          | {'precision': 0.6148536720044174, 'recall': 0.5343090211132437, 'f1-score': 0.5717586649550707, 'support': 4168.0} | {'precision': 0.8070429329474192, 'recall': 0.7774163568773235, 'f1-score': 0.7919526627218936, 'support': 2152.0} | {'precision': 0.9237677984665936, 'recall': 0.914155647084327, 'f1-score': 0.9189365874918283, 'support': 9226.0}  | {'precision': 0.8554009692043145, 'recall': 0.9064855462602501, 'f1-score': 0.8802026782482808, 'support': 12073.0} | 0.8428   | {'precision': 0.8002663431556862, 'recall': 0.7830916428337862, 'f1-score': 0.7907126483542682, 'support': 27619.0} | {'precision': 0.838169524837023, 'recall': 0.8428255910786053, 'f1-score': 0.8397178803143253, 'support': 27619.0}  |
-| No log        | 8.0   | 328  | 0.5501          | {'precision': 0.5789353438428148, 'recall': 0.6079654510556622, 'f1-score': 0.5930953774136923, 'support': 4168.0} | {'precision': 0.7649107531562909, 'recall': 0.8164498141263941, 'f1-score': 0.7898404135760846, 'support': 2152.0} | {'precision': 0.9487657196087564, 'recall': 0.8831562974203339, 'f1-score': 0.9147861232738297, 'support': 9226.0} | {'precision': 0.8670389253054949, 'recall': 0.8874347718048539, 'f1-score': 0.8771182971756039, 'support': 12073.0} | 0.8383   | {'precision': 0.7899126854783393, 'recall': 0.7987515836018111, 'f1-score': 0.7937100528598027, 'support': 27619.0} | {'precision': 0.8429039403400853, 'recall': 0.8382997212064158, 'f1-score': 0.8400385270357877, 'support': 27619.0} |
-| No log        | 9.0   | 369  | 0.5615          | {'precision': 0.5539168741620379, 'recall': 0.6938579654510557, 'f1-score': 0.6160400468633508, 'support': 4168.0} | {'precision': 0.7715914072775099, 'recall': 0.8178438661710037, 'f1-score': 0.7940446650124069, 'support': 2152.0} | {'precision': 0.9404937990670156, 'recall': 0.8959462388900932, 'f1-score': 0.9176797113516515, 'support': 9226.0} | {'precision': 0.8944209039548022, 'recall': 0.8392280294872857, 'f1-score': 0.865945899747874, 'support': 12073.0}  | 0.8346   | {'precision': 0.7901057461153415, 'recall': 0.8117190249998596, 'f1-score': 0.7984275807438208, 'support': 27619.0} | {'precision': 0.8488551216049527, 'recall': 0.8345704044317318, 'f1-score': 0.8399115427430235, 'support': 27619.0} |
-| No log        | 10.0  | 410  | 0.5889          | {'precision': 0.5963951631302761, 'recall': 0.6271593090211133, 'f1-score': 0.6113904806455386, 'support': 4168.0} | {'precision': 0.7538461538461538, 'recall': 0.8424721189591078, 'f1-score': 0.7956989247311828, 'support': 2152.0} | {'precision': 0.9409945004582951, 'recall': 0.8902016041621504, 'f1-score': 0.9148936170212766, 'support': 9226.0} | {'precision': 0.8769726514087416, 'recall': 0.8791518263894641, 'f1-score': 0.8780608868299139, 'support': 12073.0} | 0.8420   | {'precision': 0.7920521172108667, 'recall': 0.8097462146329589, 'f1-score': 0.800010977306978, 'support': 27619.0}  | {'precision': 0.8464230437267781, 'recall': 0.841956624063145, 'f1-score': 0.8437038707660653, 'support': 27619.0}  |
-| No log        | 11.0  | 451  | 0.5894          | {'precision': 0.5867732872271451, 'recall': 0.6513915547024952, 'f1-score': 0.6173962478681069, 'support': 4168.0} | {'precision': 0.7666963490650045, 'recall': 0.800185873605948, 'f1-score': 0.7830832196452934, 'support': 2152.0}  | {'precision': 0.9389980688401681, 'recall': 0.8959462388900932, 'f1-score': 0.9169671085473403, 'support': 9226.0} | {'precision': 0.8817717491417567, 'recall': 0.8722769816946906, 'f1-score': 0.8769986675549633, 'support': 12073.0} | 0.8412   | {'precision': 0.7935598635685186, 'recall': 0.8049501622233067, 'f1-score': 0.798611310903926, 'support': 27619.0}  | {'precision': 0.8474031686468898, 'recall': 0.8412324848835946, 'f1-score': 0.8438555380947815, 'support': 27619.0} |
-| No log        | 12.0  | 492  | 0.6198          | {'precision': 0.5958633511503603, 'recall': 0.6151631477927063, 'f1-score': 0.6053594616928344, 'support': 4168.0} | {'precision': 0.7789770061004223, 'recall': 0.7713754646840149, 'f1-score': 0.7751575998132151, 'support': 2152.0} | {'precision': 0.9328919313208394, 'recall': 0.901040537611099, 'f1-score': 0.9166896399625074, 'support': 9226.0}  | {'precision': 0.8742056379338439, 'recall': 0.8887600430713162, 'f1-score': 0.8814227625580152, 'support': 12073.0} | 0.8424   | {'precision': 0.7954844816263664, 'recall': 0.7940847982897842, 'f1-score': 0.7946573660066429, 'support': 27619.0} | {'precision': 0.8443847565032829, 'recall': 0.8424273145298526, 'f1-score': 0.8432627184833189, 'support': 27619.0} |
-| 0.271         | 13.0  | 533  | 0.6308          | {'precision': 0.5984138428262437, 'recall': 0.5974088291746641, 'f1-score': 0.597910913675111, 'support': 4168.0}  | {'precision': 0.7893231649189705, 'recall': 0.7695167286245354, 'f1-score': 0.779294117647059, 'support': 2152.0}  | {'precision': 0.9218476357267951, 'recall': 0.9128549750704531, 'f1-score': 0.917329266964383, 'support': 9226.0}  | {'precision': 0.8714005235602095, 'recall': 0.8822993456473122, 'f1-score': 0.8768160678273037, 'support': 12073.0} | 0.8407   | {'precision': 0.7952462917580546, 'recall': 0.7905199696292412, 'f1-score': 0.7928375915284641, 'support': 27619.0} | {'precision': 0.8406603119578272, 'recall': 0.8407255874579094, 'f1-score': 0.8406609157922722, 'support': 27619.0} |
-| 0.271         | 14.0  | 574  | 0.6361          | {'precision': 0.6123370110330993, 'recall': 0.5858925143953935, 'f1-score': 0.5988229524276607, 'support': 4168.0} | {'precision': 0.7828622700762674, 'recall': 0.8108736059479554, 'f1-score': 0.7966217758502625, 'support': 2152.0} | {'precision': 0.9273249392533687, 'recall': 0.9100368523737264, 'f1-score': 0.9185995623632386, 'support': 9226.0} | {'precision': 0.8696145124716553, 'recall': 0.8894226787045474, 'f1-score': 0.879407067687646, 'support': 12073.0}  | 0.8444   | {'precision': 0.7980346832085977, 'recall': 0.7990564128554057, 'f1-score': 0.798362839582202, 'support': 27619.0}  | {'precision': 0.8433070048087172, 'recall': 0.8443824903146385, 'f1-score': 0.8437056091062111, 'support': 27619.0} |
-| 0.271         | 15.0  | 615  | 0.6438          | {'precision': 0.6039084842707341, 'recall': 0.6079654510556622, 'f1-score': 0.6059301769488283, 'support': 4168.0} | {'precision': 0.7791218637992832, 'recall': 0.8080855018587361, 'f1-score': 0.7933394160583942, 'support': 2152.0} | {'precision': 0.936604624929498, 'recall': 0.8999566442662043, 'f1-score': 0.9179149853518324, 'support': 9226.0}  | {'precision': 0.8701119584617881, 'recall': 0.8883458958005467, 'f1-score': 0.8791343907537195, 'support': 12073.0} | 0.8437   | {'precision': 0.7974367328653259, 'recall': 0.8010883732452874, 'f1-score': 0.7990797422781937, 'support': 27619.0} | {'precision': 0.8450608913228282, 'recall': 0.8436583511350881, 'f1-score': 0.8441745376482147, 'support': 27619.0} |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.6210          | {'precision': 0.3978787878787879, 'recall': 0.3080713280150164, 'f1-score': 0.3472626289341444, 'support': 4262.0}  | {'precision': 0.5235765124555161, 'recall': 0.54364896073903, 'f1-score': 0.5334239746204397, 'support': 2165.0}   | {'precision': 0.9157377442167086, 'recall': 0.7742197000405351, 'f1-score': 0.8390533194223273, 'support': 9868.0} | {'precision': 0.7896134170821731, 'recall': 0.9351944167497508, 'f1-score': 0.8562600940945159, 'support': 13039.0} | 0.7610   | {'precision': 0.6567016154082965, 'recall': 0.6402836013860831, 'f1-score': 0.6440000042678569, 'support': 29334.0} | {'precision': 0.7554909643645776, 'recall': 0.7610281584509443, 'f1-score': 0.7526914076678427, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.5057          | {'precision': 0.5328757225433526, 'recall': 0.34608165180666356, 'f1-score': 0.4196301564722618, 'support': 4262.0} | {'precision': 0.6262842465753424, 'recall': 0.6757505773672056, 'f1-score': 0.6500777604976672, 'support': 2165.0} | {'precision': 0.9066595059076262, 'recall': 0.8553911633563032, 'f1-score': 0.8802794869120867, 'support': 9868.0} | {'precision': 0.821313672922252, 'recall': 0.9397959966255081, 'f1-score': 0.8765692621338388, 'support': 13039.0}  | 0.8057   | {'precision': 0.7217832869871433, 'recall': 0.7042548472889201, 'f1-score': 0.7066391665039636, 'support': 29334.0} | {'precision': 0.7937221895699558, 'recall': 0.8056521442694484, 'f1-score': 0.7947114837449316, 'support': 29334.0} |
+| No log        | 3.0   | 123  | 0.4707          | {'precision': 0.542234931808183, 'recall': 0.5783669638667293, 'f1-score': 0.5597184377838329, 'support': 4262.0}   | {'precision': 0.669374492282697, 'recall': 0.7612009237875289, 'f1-score': 0.7123406094661768, 'support': 2165.0}  | {'precision': 0.9139037996000421, 'recall': 0.8799148763680583, 'f1-score': 0.896587330270019, 'support': 9868.0}  | {'precision': 0.8872514619883041, 'recall': 0.8726896234373802, 'f1-score': 0.879910300030931, 'support': 13039.0}  | 0.8241   | {'precision': 0.7531911714198065, 'recall': 0.7730430968649242, 'f1-score': 0.76213916938774, 'support': 29334.0}   | {'precision': 0.8300087121591747, 'recall': 0.8241289970682485, 'f1-score': 0.8266316076408545, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.4995          | {'precision': 0.5606635071090047, 'recall': 0.5551384326607227, 'f1-score': 0.5578872907333177, 'support': 4262.0}  | {'precision': 0.748995983935743, 'recall': 0.6891454965357968, 'f1-score': 0.7178253548231899, 'support': 2165.0}  | {'precision': 0.9082793070464449, 'recall': 0.8660316173490069, 'f1-score': 0.8866524874202418, 'support': 9868.0} | {'precision': 0.8605702617953767, 'recall': 0.9050540685635402, 'f1-score': 0.8822517942583731, 'support': 13039.0} | 0.8252   | {'precision': 0.7696272649716422, 'recall': 0.7538424037772666, 'f1-score': 0.7611542318087806, 'support': 29334.0} | {'precision': 0.8248108003682995, 'recall': 0.8251517010977023, 'f1-score': 0.8244690603905188, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.5356          | {'precision': 0.5562700964630225, 'recall': 0.5682778038479587, 'f1-score': 0.5622098421541318, 'support': 4262.0}  | {'precision': 0.7994186046511628, 'recall': 0.6351039260969977, 'f1-score': 0.7078507078507079, 'support': 2165.0} | {'precision': 0.9167929019692708, 'recall': 0.858633968382651, 'f1-score': 0.8867608581894296, 'support': 9868.0}  | {'precision': 0.8533314310172635, 'recall': 0.9174016412301557, 'f1-score': 0.8842074139778985, 'support': 13039.0} | 0.8261   | {'precision': 0.7814532585251799, 'recall': 0.7448543348894408, 'f1-score': 0.760257205543042, 'support': 29334.0}  | {'precision': 0.827540237126271, 'recall': 0.8260721347242108, 'f1-score': 0.8252666444817892, 'support': 29334.0}  |
+| No log        | 6.0   | 246  | 0.5402          | {'precision': 0.5901198337001712, 'recall': 0.5661661191928672, 'f1-score': 0.5778948628906718, 'support': 4262.0}  | {'precision': 0.7593778591033852, 'recall': 0.766743648960739, 'f1-score': 0.7630429786256032, 'support': 2165.0}  | {'precision': 0.909998948585848, 'recall': 0.877077421970004, 'f1-score': 0.8932349450436038, 'support': 9868.0}   | {'precision': 0.8704605845881311, 'recall': 0.9044405245801058, 'f1-score': 0.8871252867942979, 'support': 13039.0} | 0.8359   | {'precision': 0.7824893064943839, 'recall': 0.778606928675929, 'f1-score': 0.7803245183385442, 'support': 29334.0}  | {'precision': 0.8348315600763192, 'recall': 0.8359241835412832, 'f1-score': 0.8350939185438606, 'support': 29334.0} |
+| No log        | 7.0   | 287  | 0.5522          | {'precision': 0.5645161290322581, 'recall': 0.6241201313937119, 'f1-score': 0.5928237129485181, 'support': 4262.0}  | {'precision': 0.776257938446507, 'recall': 0.7339491916859122, 'f1-score': 0.7545109211775878, 'support': 2165.0}  | {'precision': 0.9063338147307612, 'recall': 0.8903526550466153, 'f1-score': 0.8982721603108067, 'support': 9868.0} | {'precision': 0.8897601117925626, 'recall': 0.8789784492675818, 'f1-score': 0.8843364197530864, 'support': 13039.0} | 0.8351   | {'precision': 0.7842169985005223, 'recall': 0.7818501068484554, 'f1-score': 0.7824858035474997, 'support': 29334.0} | {'precision': 0.8397030872059231, 'recall': 0.835071930183405, 'f1-score': 0.8370881251804593, 'support': 29334.0}  |
+| No log        | 8.0   | 328  | 0.5864          | {'precision': 0.5921815889029004, 'recall': 0.5509150633505396, 'f1-score': 0.5708034520481342, 'support': 4262.0}  | {'precision': 0.7887952404561229, 'recall': 0.7348729792147806, 'f1-score': 0.7608799617407939, 'support': 2165.0} | {'precision': 0.909240754094983, 'recall': 0.8944061613295501, 'f1-score': 0.9017624521072796, 'support': 9868.0}  | {'precision': 0.868889703187981, 'recall': 0.909272183449651, 'f1-score': 0.888622395442962, 'support': 13039.0}    | 0.8393   | {'precision': 0.7897768216604968, 'recall': 0.7723665968361304, 'f1-score': 0.7805170653347924, 'support': 29334.0} | {'precision': 0.8363489544136171, 'recall': 0.8393331969727961, 'f1-score': 0.8374380828176649, 'support': 29334.0} |
+| No log        | 9.0   | 369  | 0.6258          | {'precision': 0.5400439384861194, 'recall': 0.6344439230408259, 'f1-score': 0.5834502103786816, 'support': 4262.0}  | {'precision': 0.7136109918419923, 'recall': 0.7676674364896073, 'f1-score': 0.739652870493992, 'support': 2165.0}  | {'precision': 0.9208710651142734, 'recall': 0.8656262667207134, 'f1-score': 0.8923944839114083, 'support': 9868.0} | {'precision': 0.8900330136770948, 'recall': 0.86839481555334, 'f1-score': 0.8790807810255813, 'support': 13039.0}   | 0.8260   | {'precision': 0.76613975227987, 'recall': 0.7840331104511216, 'f1-score': 0.7736445864524157, 'support': 29334.0}   | {'precision': 0.8365354605252965, 'recall': 0.8260380445898957, 'f1-score': 0.8303162314135053, 'support': 29334.0} |
+| No log        | 10.0  | 410  | 0.6433          | {'precision': 0.5887546468401487, 'recall': 0.5945565462224308, 'f1-score': 0.5916413728694839, 'support': 4262.0}  | {'precision': 0.765103914934751, 'recall': 0.7311778290993072, 'f1-score': 0.747756258856873, 'support': 2165.0}   | {'precision': 0.9102390147166266, 'recall': 0.8837657073368463, 'f1-score': 0.8968070337806571, 'support': 9868.0} | {'precision': 0.878101644245142, 'recall': 0.9010660326712171, 'f1-score': 0.8894356334456263, 'support': 13039.0}  | 0.8382   | {'precision': 0.7855498051841671, 'recall': 0.7776415288324503, 'f1-score': 0.78141007473816, 'support': 29334.0}   | {'precision': 0.8385330407446147, 'recall': 0.8381741324060816, 'f1-score': 0.8381915478775454, 'support': 29334.0} |
+| No log        | 11.0  | 451  | 0.6916          | {'precision': 0.5963211533681332, 'recall': 0.5628812763960582, 'f1-score': 0.5791188895594448, 'support': 4262.0}  | {'precision': 0.7905679513184585, 'recall': 0.7200923787528868, 'f1-score': 0.7536862460720328, 'support': 2165.0} | {'precision': 0.9027949034114262, 'recall': 0.8903526550466153, 'f1-score': 0.896530612244898, 'support': 9868.0}  | {'precision': 0.8699933857573308, 'recall': 0.9078917094869239, 'f1-score': 0.888538617428507, 'support': 13039.0}  | 0.8380   | {'precision': 0.7899193484638372, 'recall': 0.770304504920621, 'f1-score': 0.7794685913262207, 'support': 29334.0}  | {'precision': 0.8354034306270279, 'recall': 0.838003681734506, 'f1-score': 0.836318079509486, 'support': 29334.0}   |
+| No log        | 12.0  | 492  | 0.6997          | {'precision': 0.5914396887159533, 'recall': 0.5706241201313937, 'f1-score': 0.5808454740864581, 'support': 4262.0}  | {'precision': 0.797138477261114, 'recall': 0.7205542725173211, 'f1-score': 0.7569141193595342, 'support': 2165.0}  | {'precision': 0.9003264639869415, 'recall': 0.8943048236724767, 'f1-score': 0.8973055414336554, 'support': 9868.0} | {'precision': 0.870236945703038, 'recall': 0.8985351637395506, 'f1-score': 0.8841596860614294, 'support': 13039.0}  | 0.8363   | {'precision': 0.7897853939167616, 'recall': 0.7710045950151856, 'f1-score': 0.7798062052352693, 'support': 29334.0} | {'precision': 0.8344570068256206, 'recall': 0.8363332651530647, 'f1-score': 0.8351214191174802, 'support': 29334.0} |
+| 0.2673        | 13.0  | 533  | 0.7149          | {'precision': 0.5794110827730118, 'recall': 0.5863444392304082, 'f1-score': 0.582857142857143, 'support': 4262.0}   | {'precision': 0.7928753180661577, 'recall': 0.7196304849884526, 'f1-score': 0.7544794188861985, 'support': 2165.0} | {'precision': 0.9063998332291016, 'recall': 0.8812322659100121, 'f1-score': 0.8936388860343233, 'support': 9868.0} | {'precision': 0.8717872530084683, 'recall': 0.9000690236981364, 'f1-score': 0.8857024263235349, 'support': 13039.0} | 0.8348   | {'precision': 0.7876183717691848, 'recall': 0.7718190534567524, 'f1-score': 0.7791694685252999, 'support': 29334.0} | {'precision': 0.8351269054569442, 'recall': 0.834833299243199, 'f1-score': 0.8346862872081897, 'support': 29334.0}  |
+| 0.2673        | 14.0  | 574  | 0.7156          | {'precision': 0.5767102058888642, 'recall': 0.6112153918348193, 'f1-score': 0.5934616698940653, 'support': 4262.0}  | {'precision': 0.7826520438683948, 'recall': 0.7251732101616628, 'f1-score': 0.7528170702469431, 'support': 2165.0} | {'precision': 0.9055462885738115, 'recall': 0.8802188893392785, 'f1-score': 0.8927029804727646, 'support': 9868.0} | {'precision': 0.8822149935698615, 'recall': 0.894393741851369, 'f1-score': 0.8882626247238937, 'support': 13039.0}  | 0.8360   | {'precision': 0.786780882975233, 'recall': 0.7777503082967825, 'f1-score': 0.7818110863344166, 'support': 29334.0}  | {'precision': 0.8383279692260589, 'recall': 0.8359923638099134, 'f1-score': 0.8369275233262845, 'support': 29334.0} |
+| 0.2673        | 15.0  | 615  | 0.7311          | {'precision': 0.5837887067395264, 'recall': 0.6015954950727358, 'f1-score': 0.5925583545181419, 'support': 4262.0}  | {'precision': 0.7695631301008161, 'recall': 0.7404157043879908, 'f1-score': 0.754708097928437, 'support': 2165.0}  | {'precision': 0.9121858097359211, 'recall': 0.8716051884880421, 'f1-score': 0.8914339016427424, 'support': 9868.0} | {'precision': 0.8736411020104244, 'recall': 0.8998389447043484, 'f1-score': 0.8865465261249008, 'support': 13039.0} | 0.8352   | {'precision': 0.784794687146672, 'recall': 0.7783638331632793, 'f1-score': 0.7813117200535555, 'support': 29334.0}  | {'precision': 0.8368128296304671, 'recall': 0.8352423808549806, 'f1-score': 0.8357461183106479, 'support': 29334.0} |
+| 0.2673        | 16.0  | 656  | 0.7323          | {'precision': 0.5949216896060749, 'recall': 0.5882214922571563, 'f1-score': 0.5915526191599811, 'support': 4262.0}  | {'precision': 0.8048654244306418, 'recall': 0.7182448036951501, 'f1-score': 0.7590920185501586, 'support': 2165.0} | {'precision': 0.9059811340313051, 'recall': 0.8856911228212404, 'f1-score': 0.8957212400717396, 'support': 9868.0} | {'precision': 0.8721660143268591, 'recall': 0.9057443055449037, 'f1-score': 0.8886380737396539, 'support': 13039.0} | 0.8390   | {'precision': 0.7944835655987202, 'recall': 0.7744754310796127, 'f1-score': 0.7837509878803832, 'support': 29334.0} | {'precision': 0.8382929152663212, 'recall': 0.8390263857639599, 'f1-score': 0.8382955111317995, 'support': 29334.0} |
 
 
 ### Framework versions

meta_data/README_s42_e16.md

@@ -0,0 +1,96 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-simple
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: simple
+      split: train[0%:20%]
+      args: simple
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8390263857639599
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-simple
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.7323
+- Claim: {'precision': 0.5949216896060749, 'recall': 0.5882214922571563, 'f1-score': 0.5915526191599811, 'support': 4262.0}
+- Majorclaim: {'precision': 0.8048654244306418, 'recall': 0.7182448036951501, 'f1-score': 0.7590920185501586, 'support': 2165.0}
+- O: {'precision': 0.9059811340313051, 'recall': 0.8856911228212404, 'f1-score': 0.8957212400717396, 'support': 9868.0}
+- Premise: {'precision': 0.8721660143268591, 'recall': 0.9057443055449037, 'f1-score': 0.8886380737396539, 'support': 13039.0}
+- Accuracy: 0.8390
+- Macro avg: {'precision': 0.7944835655987202, 'recall': 0.7744754310796127, 'f1-score': 0.7837509878803832, 'support': 29334.0}
+- Weighted avg: {'precision': 0.8382929152663212, 'recall': 0.8390263857639599, 'f1-score': 0.8382955111317995, 'support': 29334.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 16
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.6210          | {'precision': 0.3978787878787879, 'recall': 0.3080713280150164, 'f1-score': 0.3472626289341444, 'support': 4262.0}  | {'precision': 0.5235765124555161, 'recall': 0.54364896073903, 'f1-score': 0.5334239746204397, 'support': 2165.0}   | {'precision': 0.9157377442167086, 'recall': 0.7742197000405351, 'f1-score': 0.8390533194223273, 'support': 9868.0} | {'precision': 0.7896134170821731, 'recall': 0.9351944167497508, 'f1-score': 0.8562600940945159, 'support': 13039.0} | 0.7610   | {'precision': 0.6567016154082965, 'recall': 0.6402836013860831, 'f1-score': 0.6440000042678569, 'support': 29334.0} | {'precision': 0.7554909643645776, 'recall': 0.7610281584509443, 'f1-score': 0.7526914076678427, 'support': 29334.0} |
+| No log        | 2.0   | 82   | 0.5057          | {'precision': 0.5328757225433526, 'recall': 0.34608165180666356, 'f1-score': 0.4196301564722618, 'support': 4262.0} | {'precision': 0.6262842465753424, 'recall': 0.6757505773672056, 'f1-score': 0.6500777604976672, 'support': 2165.0} | {'precision': 0.9066595059076262, 'recall': 0.8553911633563032, 'f1-score': 0.8802794869120867, 'support': 9868.0} | {'precision': 0.821313672922252, 'recall': 0.9397959966255081, 'f1-score': 0.8765692621338388, 'support': 13039.0}  | 0.8057   | {'precision': 0.7217832869871433, 'recall': 0.7042548472889201, 'f1-score': 0.7066391665039636, 'support': 29334.0} | {'precision': 0.7937221895699558, 'recall': 0.8056521442694484, 'f1-score': 0.7947114837449316, 'support': 29334.0} |
+| No log        | 3.0   | 123  | 0.4707          | {'precision': 0.542234931808183, 'recall': 0.5783669638667293, 'f1-score': 0.5597184377838329, 'support': 4262.0}   | {'precision': 0.669374492282697, 'recall': 0.7612009237875289, 'f1-score': 0.7123406094661768, 'support': 2165.0}  | {'precision': 0.9139037996000421, 'recall': 0.8799148763680583, 'f1-score': 0.896587330270019, 'support': 9868.0}  | {'precision': 0.8872514619883041, 'recall': 0.8726896234373802, 'f1-score': 0.879910300030931, 'support': 13039.0}  | 0.8241   | {'precision': 0.7531911714198065, 'recall': 0.7730430968649242, 'f1-score': 0.76213916938774, 'support': 29334.0}   | {'precision': 0.8300087121591747, 'recall': 0.8241289970682485, 'f1-score': 0.8266316076408545, 'support': 29334.0} |
+| No log        | 4.0   | 164  | 0.4995          | {'precision': 0.5606635071090047, 'recall': 0.5551384326607227, 'f1-score': 0.5578872907333177, 'support': 4262.0}  | {'precision': 0.748995983935743, 'recall': 0.6891454965357968, 'f1-score': 0.7178253548231899, 'support': 2165.0}  | {'precision': 0.9082793070464449, 'recall': 0.8660316173490069, 'f1-score': 0.8866524874202418, 'support': 9868.0} | {'precision': 0.8605702617953767, 'recall': 0.9050540685635402, 'f1-score': 0.8822517942583731, 'support': 13039.0} | 0.8252   | {'precision': 0.7696272649716422, 'recall': 0.7538424037772666, 'f1-score': 0.7611542318087806, 'support': 29334.0} | {'precision': 0.8248108003682995, 'recall': 0.8251517010977023, 'f1-score': 0.8244690603905188, 'support': 29334.0} |
+| No log        | 5.0   | 205  | 0.5356          | {'precision': 0.5562700964630225, 'recall': 0.5682778038479587, 'f1-score': 0.5622098421541318, 'support': 4262.0}  | {'precision': 0.7994186046511628, 'recall': 0.6351039260969977, 'f1-score': 0.7078507078507079, 'support': 2165.0} | {'precision': 0.9167929019692708, 'recall': 0.858633968382651, 'f1-score': 0.8867608581894296, 'support': 9868.0}  | {'precision': 0.8533314310172635, 'recall': 0.9174016412301557, 'f1-score': 0.8842074139778985, 'support': 13039.0} | 0.8261   | {'precision': 0.7814532585251799, 'recall': 0.7448543348894408, 'f1-score': 0.760257205543042, 'support': 29334.0}  | {'precision': 0.827540237126271, 'recall': 0.8260721347242108, 'f1-score': 0.8252666444817892, 'support': 29334.0}  |
+| No log        | 6.0   | 246  | 0.5402          | {'precision': 0.5901198337001712, 'recall': 0.5661661191928672, 'f1-score': 0.5778948628906718, 'support': 4262.0}  | {'precision': 0.7593778591033852, 'recall': 0.766743648960739, 'f1-score': 0.7630429786256032, 'support': 2165.0}  | {'precision': 0.909998948585848, 'recall': 0.877077421970004, 'f1-score': 0.8932349450436038, 'support': 9868.0}   | {'precision': 0.8704605845881311, 'recall': 0.9044405245801058, 'f1-score': 0.8871252867942979, 'support': 13039.0} | 0.8359   | {'precision': 0.7824893064943839, 'recall': 0.778606928675929, 'f1-score': 0.7803245183385442, 'support': 29334.0}  | {'precision': 0.8348315600763192, 'recall': 0.8359241835412832, 'f1-score': 0.8350939185438606, 'support': 29334.0} |
+| No log        | 7.0   | 287  | 0.5522          | {'precision': 0.5645161290322581, 'recall': 0.6241201313937119, 'f1-score': 0.5928237129485181, 'support': 4262.0}  | {'precision': 0.776257938446507, 'recall': 0.7339491916859122, 'f1-score': 0.7545109211775878, 'support': 2165.0}  | {'precision': 0.9063338147307612, 'recall': 0.8903526550466153, 'f1-score': 0.8982721603108067, 'support': 9868.0} | {'precision': 0.8897601117925626, 'recall': 0.8789784492675818, 'f1-score': 0.8843364197530864, 'support': 13039.0} | 0.8351   | {'precision': 0.7842169985005223, 'recall': 0.7818501068484554, 'f1-score': 0.7824858035474997, 'support': 29334.0} | {'precision': 0.8397030872059231, 'recall': 0.835071930183405, 'f1-score': 0.8370881251804593, 'support': 29334.0}  |
+| No log        | 8.0   | 328  | 0.5864          | {'precision': 0.5921815889029004, 'recall': 0.5509150633505396, 'f1-score': 0.5708034520481342, 'support': 4262.0}  | {'precision': 0.7887952404561229, 'recall': 0.7348729792147806, 'f1-score': 0.7608799617407939, 'support': 2165.0} | {'precision': 0.909240754094983, 'recall': 0.8944061613295501, 'f1-score': 0.9017624521072796, 'support': 9868.0}  | {'precision': 0.868889703187981, 'recall': 0.909272183449651, 'f1-score': 0.888622395442962, 'support': 13039.0}    | 0.8393   | {'precision': 0.7897768216604968, 'recall': 0.7723665968361304, 'f1-score': 0.7805170653347924, 'support': 29334.0} | {'precision': 0.8363489544136171, 'recall': 0.8393331969727961, 'f1-score': 0.8374380828176649, 'support': 29334.0} |
+| No log        | 9.0   | 369  | 0.6258          | {'precision': 0.5400439384861194, 'recall': 0.6344439230408259, 'f1-score': 0.5834502103786816, 'support': 4262.0}  | {'precision': 0.7136109918419923, 'recall': 0.7676674364896073, 'f1-score': 0.739652870493992, 'support': 2165.0}  | {'precision': 0.9208710651142734, 'recall': 0.8656262667207134, 'f1-score': 0.8923944839114083, 'support': 9868.0} | {'precision': 0.8900330136770948, 'recall': 0.86839481555334, 'f1-score': 0.8790807810255813, 'support': 13039.0}   | 0.8260   | {'precision': 0.76613975227987, 'recall': 0.7840331104511216, 'f1-score': 0.7736445864524157, 'support': 29334.0}   | {'precision': 0.8365354605252965, 'recall': 0.8260380445898957, 'f1-score': 0.8303162314135053, 'support': 29334.0} |
+| No log        | 10.0  | 410  | 0.6433          | {'precision': 0.5887546468401487, 'recall': 0.5945565462224308, 'f1-score': 0.5916413728694839, 'support': 4262.0}  | {'precision': 0.765103914934751, 'recall': 0.7311778290993072, 'f1-score': 0.747756258856873, 'support': 2165.0}   | {'precision': 0.9102390147166266, 'recall': 0.8837657073368463, 'f1-score': 0.8968070337806571, 'support': 9868.0} | {'precision': 0.878101644245142, 'recall': 0.9010660326712171, 'f1-score': 0.8894356334456263, 'support': 13039.0}  | 0.8382   | {'precision': 0.7855498051841671, 'recall': 0.7776415288324503, 'f1-score': 0.78141007473816, 'support': 29334.0}   | {'precision': 0.8385330407446147, 'recall': 0.8381741324060816, 'f1-score': 0.8381915478775454, 'support': 29334.0} |
+| No log        | 11.0  | 451  | 0.6916          | {'precision': 0.5963211533681332, 'recall': 0.5628812763960582, 'f1-score': 0.5791188895594448, 'support': 4262.0}  | {'precision': 0.7905679513184585, 'recall': 0.7200923787528868, 'f1-score': 0.7536862460720328, 'support': 2165.0} | {'precision': 0.9027949034114262, 'recall': 0.8903526550466153, 'f1-score': 0.896530612244898, 'support': 9868.0}  | {'precision': 0.8699933857573308, 'recall': 0.9078917094869239, 'f1-score': 0.888538617428507, 'support': 13039.0}  | 0.8380   | {'precision': 0.7899193484638372, 'recall': 0.770304504920621, 'f1-score': 0.7794685913262207, 'support': 29334.0}  | {'precision': 0.8354034306270279, 'recall': 0.838003681734506, 'f1-score': 0.836318079509486, 'support': 29334.0}   |
+| No log        | 12.0  | 492  | 0.6997          | {'precision': 0.5914396887159533, 'recall': 0.5706241201313937, 'f1-score': 0.5808454740864581, 'support': 4262.0}  | {'precision': 0.797138477261114, 'recall': 0.7205542725173211, 'f1-score': 0.7569141193595342, 'support': 2165.0}  | {'precision': 0.9003264639869415, 'recall': 0.8943048236724767, 'f1-score': 0.8973055414336554, 'support': 9868.0} | {'precision': 0.870236945703038, 'recall': 0.8985351637395506, 'f1-score': 0.8841596860614294, 'support': 13039.0}  | 0.8363   | {'precision': 0.7897853939167616, 'recall': 0.7710045950151856, 'f1-score': 0.7798062052352693, 'support': 29334.0} | {'precision': 0.8344570068256206, 'recall': 0.8363332651530647, 'f1-score': 0.8351214191174802, 'support': 29334.0} |
+| 0.2673        | 13.0  | 533  | 0.7149          | {'precision': 0.5794110827730118, 'recall': 0.5863444392304082, 'f1-score': 0.582857142857143, 'support': 4262.0}   | {'precision': 0.7928753180661577, 'recall': 0.7196304849884526, 'f1-score': 0.7544794188861985, 'support': 2165.0} | {'precision': 0.9063998332291016, 'recall': 0.8812322659100121, 'f1-score': 0.8936388860343233, 'support': 9868.0} | {'precision': 0.8717872530084683, 'recall': 0.9000690236981364, 'f1-score': 0.8857024263235349, 'support': 13039.0} | 0.8348   | {'precision': 0.7876183717691848, 'recall': 0.7718190534567524, 'f1-score': 0.7791694685252999, 'support': 29334.0} | {'precision': 0.8351269054569442, 'recall': 0.834833299243199, 'f1-score': 0.8346862872081897, 'support': 29334.0}  |
+| 0.2673        | 14.0  | 574  | 0.7156          | {'precision': 0.5767102058888642, 'recall': 0.6112153918348193, 'f1-score': 0.5934616698940653, 'support': 4262.0}  | {'precision': 0.7826520438683948, 'recall': 0.7251732101616628, 'f1-score': 0.7528170702469431, 'support': 2165.0} | {'precision': 0.9055462885738115, 'recall': 0.8802188893392785, 'f1-score': 0.8927029804727646, 'support': 9868.0} | {'precision': 0.8822149935698615, 'recall': 0.894393741851369, 'f1-score': 0.8882626247238937, 'support': 13039.0}  | 0.8360   | {'precision': 0.786780882975233, 'recall': 0.7777503082967825, 'f1-score': 0.7818110863344166, 'support': 29334.0}  | {'precision': 0.8383279692260589, 'recall': 0.8359923638099134, 'f1-score': 0.8369275233262845, 'support': 29334.0} |
+| 0.2673        | 15.0  | 615  | 0.7311          | {'precision': 0.5837887067395264, 'recall': 0.6015954950727358, 'f1-score': 0.5925583545181419, 'support': 4262.0}  | {'precision': 0.7695631301008161, 'recall': 0.7404157043879908, 'f1-score': 0.754708097928437, 'support': 2165.0}  | {'precision': 0.9121858097359211, 'recall': 0.8716051884880421, 'f1-score': 0.8914339016427424, 'support': 9868.0} | {'precision': 0.8736411020104244, 'recall': 0.8998389447043484, 'f1-score': 0.8865465261249008, 'support': 13039.0} | 0.8352   | {'precision': 0.784794687146672, 'recall': 0.7783638331632793, 'f1-score': 0.7813117200535555, 'support': 29334.0}  | {'precision': 0.8368128296304671, 'recall': 0.8352423808549806, 'f1-score': 0.8357461183106479, 'support': 29334.0} |
+| 0.2673        | 16.0  | 656  | 0.7323          | {'precision': 0.5949216896060749, 'recall': 0.5882214922571563, 'f1-score': 0.5915526191599811, 'support': 4262.0}  | {'precision': 0.8048654244306418, 'recall': 0.7182448036951501, 'f1-score': 0.7590920185501586, 'support': 2165.0} | {'precision': 0.9059811340313051, 'recall': 0.8856911228212404, 'f1-score': 0.8957212400717396, 'support': 9868.0} | {'precision': 0.8721660143268591, 'recall': 0.9057443055449037, 'f1-score': 0.8886380737396539, 'support': 13039.0} | 0.8390   | {'precision': 0.7944835655987202, 'recall': 0.7744754310796127, 'f1-score': 0.7837509878803832, 'support': 29334.0} | {'precision': 0.8382929152663212, 'recall': 0.8390263857639599, 'f1-score': 0.8382955111317995, 'support': 29334.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/meta_s42_e16_cvi0.json

@@ -1 +1 @@
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+{"Claim": {"precision": 0.5949216896060749, "recall": 0.5882214922571563, "f1-score": 0.5915526191599811, "support": 4262.0}, "MajorClaim": {"precision": 0.8048654244306418, "recall": 0.7182448036951501, "f1-score": 0.7590920185501586, "support": 2165.0}, "O": {"precision": 0.9059811340313051, "recall": 0.8856911228212404, "f1-score": 0.8957212400717396, "support": 9868.0}, "Premise": {"precision": 0.8721660143268591, "recall": 0.9057443055449037, "f1-score": 0.8886380737396539, "support": 13039.0}, "accuracy": 0.8390263857639599, "macro avg": {"precision": 0.7944835655987202, "recall": 0.7744754310796127, "f1-score": 0.7837509878803832, "support": 29334.0}, "weighted avg": {"precision": 0.8382929152663212, "recall": 0.8390263857639599, "f1-score": 0.8382955111317995, "support": 29334.0}}

model.safetensors

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