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longformer-sep_tok_full_labels

Token Classification
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Safetensors
longformer
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longformer-sep_tok_full_labels / README.md
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metadata
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
  - generated_from_trainer
datasets:
  - essays_su_g
metrics:
  - accuracy
model-index:
  - name: longformer-sep_tok_full_labels
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: essays_su_g
          type: essays_su_g
          config: sep_tok_full_labels
          split: train[0%:20%]
          args: sep_tok_full_labels
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9089556962025317

longformer-sep_tok_full_labels

This model is a fine-tuned version of allenai/longformer-base-4096 on the essays_su_g dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8144
  • B-claim: {'precision': 0.6953405017921147, 'recall': 0.6830985915492958, 'f1-score': 0.6891651865008882, 'support': 284.0}
  • B-majorclaim: {'precision': 0.9323308270676691, 'recall': 0.8794326241134752, 'f1-score': 0.9051094890510948, 'support': 141.0}
  • B-premise: {'precision': 0.8848821081830791, 'recall': 0.9011299435028248, 'f1-score': 0.8929321203638908, 'support': 708.0}
  • I-claim: {'precision': 0.6836601307189543, 'recall': 0.6414029923963699, 'f1-score': 0.6618577575297392, 'support': 4077.0}
  • I-majorclaim: {'precision': 0.9350785340314136, 'recall': 0.8824110671936759, 'f1-score': 0.9079816980172852, 'support': 2024.0}
  • I-premise: {'precision': 0.8918532634587899, 'recall': 0.9182472204054938, 'f1-score': 0.9048578103601064, 'support': 12232.0}
  • O: {'precision': 0.9996704564178612, 'recall': 1.0, 'f1-score': 0.9998352010547131, 'support': 12134.0}
  • Accuracy: 0.9090
  • Macro avg: {'precision': 0.8604022602385546, 'recall': 0.8436746341658764, 'f1-score': 0.8516770375539596, 'support': 31600.0}
  • Weighted avg: {'precision': 0.9074197349475357, 'recall': 0.9089556962025317, 'f1-score': 0.907971816211979, 'support': 31600.0}

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: 50

Training results

Training Loss Epoch Step Validation Loss B-claim B-majorclaim B-premise I-claim I-majorclaim I-premise O Accuracy Macro avg Weighted avg
No log 1.0 81 0.3326 {'precision': 0.23275862068965517, 'recall': 0.09507042253521127, 'f1-score': 0.135, 'support': 284.0} {'precision': 1.0, 'recall': 0.0070921985815602835, 'f1-score': 0.014084507042253521, 'support': 141.0} {'precision': 0.7025948103792415, 'recall': 0.9943502824858758, 'f1-score': 0.8233918128654971, 'support': 708.0} {'precision': 0.5555555555555556, 'recall': 0.3102771645818003, 'f1-score': 0.39817437834435004, 'support': 4077.0} {'precision': 0.8446601941747572, 'recall': 0.6017786561264822, 'f1-score': 0.7028274668205424, 'support': 2024.0} {'precision': 0.8097785092230679, 'recall': 0.9654185742315239, 'f1-score': 0.8807756852507925, 'support': 12232.0} {'precision': 0.9958945726250102, 'recall': 0.9995879347288611, 'f1-score': 0.9977378357257435, 'support': 12134.0} 0.8593 {'precision': 0.734463180378184, 'recall': 0.5676536047530449, 'f1-score': 0.5645702408641685, 'support': 31600.0} {'precision': 0.8439407682304593, 'recall': 0.8592721518987342, 'f1-score': 0.840169814336718, 'support': 31600.0}
No log 2.0 162 0.3249 {'precision': 0.5647668393782384, 'recall': 0.38380281690140844, 'f1-score': 0.4570230607966457, 'support': 284.0} {'precision': 0.8888888888888888, 'recall': 0.5673758865248227, 'f1-score': 0.6926406926406928, 'support': 141.0} {'precision': 0.7981220657276995, 'recall': 0.96045197740113, 'f1-score': 0.8717948717948718, 'support': 708.0} {'precision': 0.6064968623108158, 'recall': 0.4029923963698798, 'f1-score': 0.48423224285293254, 'support': 4077.0} {'precision': 0.9049773755656109, 'recall': 0.5928853754940712, 'f1-score': 0.7164179104477612, 'support': 2024.0} {'precision': 0.832260321903429, 'recall': 0.9722858077174624, 'f1-score': 0.896840358947289, 'support': 12232.0} {'precision': 0.9988467874794069, 'recall': 0.9993406955661777, 'f1-score': 0.9990936804811733, 'support': 12134.0} 0.8776 {'precision': 0.7991941630362983, 'recall': 0.6970192794249932, 'f1-score': 0.7311489739944809, 'support': 31600.0} {'precision': 0.8688409693302476, 'recall': 0.8775632911392405, 'f1-score': 0.8658888085638868, 'support': 31600.0}
No log 3.0 243 0.2603 {'precision': 0.6473214285714286, 'recall': 0.5105633802816901, 'f1-score': 0.5708661417322833, 'support': 284.0} {'precision': 0.8345864661654135, 'recall': 0.7872340425531915, 'f1-score': 0.8102189781021898, 'support': 141.0} {'precision': 0.847741935483871, 'recall': 0.9279661016949152, 'f1-score': 0.8860418071476737, 'support': 708.0} {'precision': 0.6502840909090909, 'recall': 0.5614422369389257, 'f1-score': 0.6026062919573516, 'support': 4077.0} {'precision': 0.8301886792452831, 'recall': 0.8043478260869565, 'f1-score': 0.8170639899623588, 'support': 2024.0} {'precision': 0.8837009899446566, 'recall': 0.9268312622629169, 'f1-score': 0.9047524041339132, 'support': 12232.0} {'precision': 0.9972034874156934, 'recall': 0.9991758694577221, 'f1-score': 0.9981887041001153, 'support': 12134.0} 0.8953 {'precision': 0.8130038682479196, 'recall': 0.7882229598966168, 'f1-score': 0.7985340453051265, 'support': 31600.0} {'precision': 0.8905925638765844, 'recall': 0.8952848101265822, 'f1-score': 0.8921899041308763, 'support': 31600.0}
No log 4.0 324 0.3208 {'precision': 0.5478723404255319, 'recall': 0.7253521126760564, 'f1-score': 0.6242424242424244, 'support': 284.0} {'precision': 0.7926829268292683, 'recall': 0.9219858156028369, 'f1-score': 0.8524590163934427, 'support': 141.0} {'precision': 0.9159663865546218, 'recall': 0.769774011299435, 'f1-score': 0.8365310821181888, 'support': 708.0} {'precision': 0.5391455972101133, 'recall': 0.7584007848908512, 'f1-score': 0.6302486750917244, 'support': 4077.0} {'precision': 0.7994825355756792, 'recall': 0.91600790513834, 'f1-score': 0.8537877043518306, 'support': 2024.0} {'precision': 0.9427625815243843, 'recall': 0.7917756703727927, 'f1-score': 0.8606976227504999, 'support': 12232.0} {'precision': 0.9985170538803757, 'recall': 0.9988462172408109, 'f1-score': 0.998681608437706, 'support': 12134.0} 0.8744 {'precision': 0.7909184888571392, 'recall': 0.8403060738887318, 'f1-score': 0.8080925904836882, 'support': 31600.0} {'precision': 0.8981011177172786, 'recall': 0.8744303797468355, 'f1-score': 0.8808035346886173, 'support': 31600.0}
No log 5.0 405 0.3258 {'precision': 0.6464285714285715, 'recall': 0.6373239436619719, 'f1-score': 0.6418439716312057, 'support': 284.0} {'precision': 0.8923076923076924, 'recall': 0.8226950354609929, 'f1-score': 0.8560885608856089, 'support': 141.0} {'precision': 0.8741355463347165, 'recall': 0.8926553672316384, 'f1-score': 0.883298392732355, 'support': 708.0} {'precision': 0.6225490196078431, 'recall': 0.6230071130733382, 'f1-score': 0.6227779821012628, 'support': 4077.0} {'precision': 0.9174364896073903, 'recall': 0.7850790513833992, 'f1-score': 0.8461128860489883, 'support': 2024.0} {'precision': 0.8866928062735057, 'recall': 0.9059025506867233, 'f1-score': 0.8961947511019451, 'support': 12232.0} {'precision': 0.9978614903767067, 'recall': 0.9998351738915444, 'f1-score': 0.9988473571546188, 'support': 12134.0} 0.8947 {'precision': 0.8339159451337751, 'recall': 0.8094997479128011, 'f1-score': 0.8207377002365693, 'support': 31600.0} {'precision': 0.8948541330437054, 'recall': 0.8946518987341772, 'f1-score': 0.8943744866666387, 'support': 31600.0}
No log 6.0 486 0.3819 {'precision': 0.668, 'recall': 0.5880281690140845, 'f1-score': 0.6254681647940075, 'support': 284.0} {'precision': 0.8823529411764706, 'recall': 0.851063829787234, 'f1-score': 0.8664259927797834, 'support': 141.0} {'precision': 0.8607764390896921, 'recall': 0.9081920903954802, 'f1-score': 0.8838487972508592, 'support': 708.0} {'precision': 0.6385115180153573, 'recall': 0.5302918812852587, 'f1-score': 0.5793916655500468, 'support': 4077.0} {'precision': 0.8816371681415929, 'recall': 0.7875494071146245, 'f1-score': 0.8319415448851774, 'support': 2024.0} {'precision': 0.8668801463860933, 'recall': 0.9295291039895357, 'f1-score': 0.8971121982010416, 'support': 12232.0} {'precision': 0.9979435716048367, 'recall': 0.9998351738915444, 'f1-score': 0.9988884772137829, 'support': 12134.0} 0.8920 {'precision': 0.8280145406305776, 'recall': 0.7992128079253946, 'f1-score': 0.8118681200963855, 'support': 31600.0} {'precision': 0.886833032256214, 'recall': 0.8920253164556962, 'f1-score': 0.88815135033036, 'support': 31600.0}
0.2533 7.0 567 0.3827 {'precision': 0.6228373702422145, 'recall': 0.6338028169014085, 'f1-score': 0.6282722513089006, 'support': 284.0} {'precision': 0.9611650485436893, 'recall': 0.7021276595744681, 'f1-score': 0.8114754098360656, 'support': 141.0} {'precision': 0.8638814016172507, 'recall': 0.905367231638418, 'f1-score': 0.8841379310344828, 'support': 708.0} {'precision': 0.6089494163424124, 'recall': 0.6141770909982831, 'f1-score': 0.6115520820613017, 'support': 4077.0} {'precision': 0.9721611721611721, 'recall': 0.6556324110671937, 'f1-score': 0.78312186485689, 'support': 2024.0} {'precision': 0.8794795886569025, 'recall': 0.9229071288423807, 'f1-score': 0.9006701771182384, 'support': 12232.0} {'precision': 0.9981897473874763, 'recall': 0.9997527608373167, 'f1-score': 0.9989706427306789, 'support': 12134.0} 0.8915 {'precision': 0.8438091064215882, 'recall': 0.776252442837067, 'f1-score': 0.8026000512780798, 'support': 31600.0} {'precision': 0.8938040450465683, 'recall': 0.8914873417721519, 'f1-score': 0.8903690153724573, 'support': 31600.0}
0.2533 8.0 648 0.4193 {'precision': 0.6566666666666666, 'recall': 0.6936619718309859, 'f1-score': 0.6746575342465753, 'support': 284.0} {'precision': 0.8767123287671232, 'recall': 0.9078014184397163, 'f1-score': 0.89198606271777, 'support': 141.0} {'precision': 0.8981077147016011, 'recall': 0.8714689265536724, 'f1-score': 0.8845878136200716, 'support': 708.0} {'precision': 0.6460472892285646, 'recall': 0.6634780475840079, 'f1-score': 0.654646660212972, 'support': 4077.0} {'precision': 0.8898765432098765, 'recall': 0.8903162055335968, 'f1-score': 0.8900963200790318, 'support': 2024.0} {'precision': 0.9043643577450818, 'recall': 0.8944571615434925, 'f1-score': 0.899383477188656, 'support': 12232.0} {'precision': 0.9979435716048367, 'recall': 0.9998351738915444, 'f1-score': 0.9988884772137829, 'support': 12134.0} 0.9026 {'precision': 0.8385312102748215, 'recall': 0.8458598436252881, 'f1-score': 0.8420351921826942, 'support': 31600.0} {'precision': 0.9035521226940129, 'recall': 0.9025949367088607, 'f1-score': 0.9030373989754484, 'support': 31600.0}
0.2533 9.0 729 0.4639 {'precision': 0.6351351351351351, 'recall': 0.6619718309859155, 'f1-score': 0.6482758620689655, 'support': 284.0} {'precision': 0.9140625, 'recall': 0.8297872340425532, 'f1-score': 0.8698884758364313, 'support': 141.0} {'precision': 0.8772919605077574, 'recall': 0.8785310734463276, 'f1-score': 0.8779110797459421, 'support': 708.0} {'precision': 0.6366223908918406, 'recall': 0.6583272013735589, 'f1-score': 0.6472928976245026, 'support': 4077.0} {'precision': 0.9625890736342043, 'recall': 0.8008893280632411, 'f1-score': 0.8743257820927725, 'support': 2024.0} {'precision': 0.891213389121339, 'recall': 0.9054937867887508, 'f1-score': 0.8982968369829685, 'support': 12232.0} {'precision': 0.9993409671307356, 'recall': 0.9997527608373167, 'f1-score': 0.9995468215712932, 'support': 12134.0} 0.9000 {'precision': 0.8451793452030018, 'recall': 0.8192504593625234, 'f1-score': 0.8307911079889821, 'support': 31600.0} {'precision': 0.9019461753960791, 'recall': 0.8999683544303797, 'f1-score': 0.9004253871478879, 'support': 31600.0}
0.2533 10.0 810 0.6042 {'precision': 0.7027027027027027, 'recall': 0.5492957746478874, 'f1-score': 0.6166007905138341, 'support': 284.0} {'precision': 0.9224806201550387, 'recall': 0.8439716312056738, 'f1-score': 0.8814814814814815, 'support': 141.0} {'precision': 0.8452685421994884, 'recall': 0.9336158192090396, 'f1-score': 0.8872483221476509, 'support': 708.0} {'precision': 0.6888888888888889, 'recall': 0.5094432180524896, 'f1-score': 0.5857304004512126, 'support': 4077.0} {'precision': 0.8985122210414452, 'recall': 0.8354743083003953, 'f1-score': 0.8658474142345111, 'support': 2024.0} {'precision': 0.8613160444146359, 'recall': 0.9448986265533028, 'f1-score': 0.9011734435304667, 'support': 12232.0} {'precision': 0.9986009381943873, 'recall': 1.0, 'f1-score': 0.9992999794111592, 'support': 12134.0} 0.8986 {'precision': 0.845395708228084, 'recall': 0.8023856254241126, 'f1-score': 0.819625975967188, 'support': 31600.0} {'precision': 0.8926556277723124, 'recall': 0.8986075949367088, 'f1-score': 0.8929345837330782, 'support': 31600.0}
0.2533 11.0 891 0.5668 {'precision': 0.7142857142857143, 'recall': 0.545774647887324, 'f1-score': 0.6187624750499001, 'support': 284.0} {'precision': 0.9166666666666666, 'recall': 0.8581560283687943, 'f1-score': 0.8864468864468864, 'support': 141.0} {'precision': 0.8431122448979592, 'recall': 0.9336158192090396, 'f1-score': 0.8860589812332441, 'support': 708.0} {'precision': 0.6964671953857245, 'recall': 0.47387785136129507, 'f1-score': 0.5640052547073421, 'support': 4077.0} {'precision': 0.8942917547568711, 'recall': 0.8359683794466403, 'f1-score': 0.86414708886619, 'support': 2024.0} {'precision': 0.8518274371932909, 'recall': 0.9507848266841072, 'f1-score': 0.898589916940313, 'support': 12232.0} {'precision': 0.998847546921304, 'recall': 1.0, 'f1-score': 0.9994234412321885, 'support': 12134.0} 0.8964 {'precision': 0.8450712228725044, 'recall': 0.7997396504224572, 'f1-score': 0.8167762920680092, 'support': 31600.0} {'precision': 0.8898146590364506, 'recall': 0.8963607594936709, 'f1-score': 0.889085010881285, 'support': 31600.0}
0.2533 12.0 972 0.5729 {'precision': 0.631578947368421, 'recall': 0.7605633802816901, 'f1-score': 0.6900958466453674, 'support': 284.0} {'precision': 0.9130434782608695, 'recall': 0.8936170212765957, 'f1-score': 0.9032258064516129, 'support': 141.0} {'precision': 0.9096477794793262, 'recall': 0.8389830508474576, 'f1-score': 0.872887582659809, 'support': 708.0} {'precision': 0.6274635818337618, 'recall': 0.7184204071621291, 'f1-score': 0.6698684962835907, 'support': 4077.0} {'precision': 0.9063136456211812, 'recall': 0.8794466403162056, 'f1-score': 0.8926780341023068, 'support': 2024.0} {'precision': 0.9143712574850299, 'recall': 0.8738554610856769, 'f1-score': 0.8936543767243541, 'support': 12232.0} {'precision': 0.9990942774804447, 'recall': 1.0, 'f1-score': 0.9995469335639853, 'support': 12134.0} 0.9009 {'precision': 0.8430732810755763, 'recall': 0.8521265658528222, 'f1-score': 0.8459938680615754, 'support': 31600.0} {'precision': 0.9067178862574966, 'recall': 0.9008860759493671, 'f1-score': 0.9031285916458567, 'support': 31600.0}
0.0325 13.0 1053 0.6332 {'precision': 0.6585365853658537, 'recall': 0.6654929577464789, 'f1-score': 0.6619964973730298, 'support': 284.0} {'precision': 0.8260869565217391, 'recall': 0.9432624113475178, 'f1-score': 0.8807947019867549, 'support': 141.0} {'precision': 0.8948905109489051, 'recall': 0.865819209039548, 'f1-score': 0.8801148600143575, 'support': 708.0} {'precision': 0.6492939666238767, 'recall': 0.6203090507726269, 'f1-score': 0.634470647265429, 'support': 4077.0} {'precision': 0.8399822695035462, 'recall': 0.9362648221343873, 'f1-score': 0.8855140186915889, 'support': 2024.0} {'precision': 0.8975770020533881, 'recall': 0.8933943754087639, 'f1-score': 0.8954808046871798, 'support': 12232.0} {'precision': 0.9994234412321884, 'recall': 1.0, 'f1-score': 0.9997116374871267, 'support': 12134.0} 0.8994 {'precision': 0.8236843903213568, 'recall': 0.8463632609213317, 'f1-score': 0.8340118810722095, 'support': 31600.0} {'precision': 0.8984350213067859, 'recall': 0.8993987341772152, 'f1-score': 0.8986822719639097, 'support': 31600.0}
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0.0008 46.0 3726 0.8219 {'precision': 0.6779661016949152, 'recall': 0.704225352112676, 'f1-score': 0.690846286701209, 'support': 284.0} {'precision': 0.9538461538461539, 'recall': 0.8794326241134752, 'f1-score': 0.915129151291513, 'support': 141.0} {'precision': 0.8884180790960452, 'recall': 0.8884180790960452, 'f1-score': 0.8884180790960452, 'support': 708.0} {'precision': 0.670194437607679, 'recall': 0.6678930586215355, 'f1-score': 0.6690417690417692, 'support': 4077.0} {'precision': 0.9608908202064096, 'recall': 0.8740118577075099, 'f1-score': 0.9153945666235447, 'support': 2024.0} {'precision': 0.8958383643242372, 'recall': 0.9098266841072596, 'f1-score': 0.9027783411072804, 'support': 12232.0} {'precision': 0.999505766062603, 'recall': 1.0, 'f1-score': 0.999752821949411, 'support': 12134.0} 0.9085 {'precision': 0.8638085318340061, 'recall': 0.8462582365369287, 'f1-score': 0.8544801451158246, 'support': 31600.0} {'precision': 0.9088341221713809, 'recall': 0.9084810126582279, 'f1-score': 0.9084955968652848, 'support': 31600.0}
0.0008 47.0 3807 0.8161 {'precision': 0.6928571428571428, 'recall': 0.6830985915492958, 'f1-score': 0.6879432624113475, 'support': 284.0} {'precision': 0.9461538461538461, 'recall': 0.8723404255319149, 'f1-score': 0.9077490774907749, 'support': 141.0} {'precision': 0.8824343015214384, 'recall': 0.9011299435028248, 'f1-score': 0.8916841369671559, 'support': 708.0} {'precision': 0.6951885565669701, 'recall': 0.6556291390728477, 'f1-score': 0.6748295884877556, 'support': 4077.0} {'precision': 0.974669603524229, 'recall': 0.8745059288537549, 'f1-score': 0.921875, 'support': 2024.0} {'precision': 0.8908976079576854, 'recall': 0.9225801177240026, 'f1-score': 0.9064621069119243, 'support': 12232.0} {'precision': 0.9995881044567098, 'recall': 1.0, 'f1-score': 0.9997940098051333, 'support': 12134.0} 0.9119 {'precision': 0.868827023291146, 'recall': 0.8441834494620916, 'f1-score': 0.8557624545820132, 'support': 31600.0} {'precision': 0.9110259249692721, 'recall': 0.9119303797468354, 'f1-score': 0.9111133264053953, 'support': 31600.0}
0.0008 48.0 3888 0.8136 {'precision': 0.6866197183098591, 'recall': 0.6866197183098591, 'f1-score': 0.6866197183098591, 'support': 284.0} {'precision': 0.9253731343283582, 'recall': 0.8794326241134752, 'f1-score': 0.9018181818181819, 'support': 141.0} {'precision': 0.8867132867132868, 'recall': 0.8954802259887006, 'f1-score': 0.8910751932536893, 'support': 708.0} {'precision': 0.6768637532133676, 'recall': 0.6458180034338975, 'f1-score': 0.6609765281787372, 'support': 4077.0} {'precision': 0.9365495542737283, 'recall': 0.8824110671936759, 'f1-score': 0.9086746374968201, 'support': 2024.0} {'precision': 0.8925151611873604, 'recall': 0.914404839764552, 'f1-score': 0.9033274107575513, 'support': 12232.0} {'precision': 0.9996704564178612, 'recall': 1.0, 'f1-score': 0.9998352010547131, 'support': 12134.0} 0.9079 {'precision': 0.8577578663491173, 'recall': 0.84345235411488, 'f1-score': 0.8503324101242218, 'support': 31600.0} {'precision': 0.9068249116598845, 'recall': 0.9079430379746836, 'f1-score': 0.9072309363962819, 'support': 31600.0}
0.0008 49.0 3969 0.8144 {'precision': 0.6942446043165468, 'recall': 0.6795774647887324, 'f1-score': 0.6868327402135231, 'support': 284.0} {'precision': 0.9253731343283582, 'recall': 0.8794326241134752, 'f1-score': 0.9018181818181819, 'support': 141.0} {'precision': 0.8848821081830791, 'recall': 0.9011299435028248, 'f1-score': 0.8929321203638908, 'support': 708.0} {'precision': 0.6846799580272823, 'recall': 0.6401766004415012, 'f1-score': 0.6616808213968818, 'support': 4077.0} {'precision': 0.9341004184100419, 'recall': 0.8824110671936759, 'f1-score': 0.907520325203252, 'support': 2024.0} {'precision': 0.8917096390321301, 'recall': 0.9189012426422498, 'f1-score': 0.9051012602166123, 'support': 12232.0} {'precision': 0.9996704564178612, 'recall': 1.0, 'f1-score': 0.9998352010547131, 'support': 12134.0} 0.9090 {'precision': 0.8592371883879, 'recall': 0.843089848954637, 'f1-score': 0.8508172357524364, 'support': 31600.0} {'precision': 0.9073921731238708, 'recall': 0.9090189873417721, 'f1-score': 0.9079780251483734, 'support': 31600.0}
0.0004 50.0 4050 0.8144 {'precision': 0.6953405017921147, 'recall': 0.6830985915492958, 'f1-score': 0.6891651865008882, 'support': 284.0} {'precision': 0.9323308270676691, 'recall': 0.8794326241134752, 'f1-score': 0.9051094890510948, 'support': 141.0} {'precision': 0.8848821081830791, 'recall': 0.9011299435028248, 'f1-score': 0.8929321203638908, 'support': 708.0} {'precision': 0.6836601307189543, 'recall': 0.6414029923963699, 'f1-score': 0.6618577575297392, 'support': 4077.0} {'precision': 0.9350785340314136, 'recall': 0.8824110671936759, 'f1-score': 0.9079816980172852, 'support': 2024.0} {'precision': 0.8918532634587899, 'recall': 0.9182472204054938, 'f1-score': 0.9048578103601064, 'support': 12232.0} {'precision': 0.9996704564178612, 'recall': 1.0, 'f1-score': 0.9998352010547131, 'support': 12134.0} 0.9090 {'precision': 0.8604022602385546, 'recall': 0.8436746341658764, 'f1-score': 0.8516770375539596, 'support': 31600.0} {'precision': 0.9074197349475357, 'recall': 0.9089556962025317, 'f1-score': 0.907971816211979, 'support': 31600.0}

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2