layoutlm-funsd / README.md
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metadata
tags:
  - generated_from_trainer
datasets:
  - funsd
model-index:
  - name: layoutlm-funsd
    results: []

layoutlm-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7243
  • Answer: {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809}
  • Header: {'precision': 0.3706896551724138, 'recall': 0.36134453781512604, 'f1': 0.36595744680851067, 'number': 119}
  • Question: {'precision': 0.8041704442429737, 'recall': 0.8328638497652582, 'f1': 0.8182656826568265, 'number': 1065}
  • Overall Precision: 0.7380
  • Overall Recall: 0.7943
  • Overall F1: 0.7651
  • Overall Accuracy: 0.8003

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: 3e-05
  • train_batch_size: 128
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
1.1725 1.0 2 1.0951 {'precision': 0.33885350318471336, 'recall': 0.3288009888751545, 'f1': 0.33375156838143033, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.559967585089141, 'recall': 0.6488262910798122, 'f1': 0.6011309264897782, 'number': 1065} 0.4738 0.4802 0.4769 0.6364
1.0154 2.0 4 0.9732 {'precision': 0.4147521160822249, 'recall': 0.42398022249690975, 'f1': 0.4193154034229829, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.5925020374898126, 'recall': 0.6826291079812207, 'f1': 0.6343804537521816, 'number': 1065} 0.5184 0.5369 0.5275 0.6891
0.9362 3.0 6 0.8900 {'precision': 0.5035714285714286, 'recall': 0.522867737948084, 'f1': 0.5130382049727107, 'number': 809} {'precision': 0.06896551724137931, 'recall': 0.01680672268907563, 'f1': 0.027027027027027025, 'number': 119} {'precision': 0.6452159187129551, 'recall': 0.7154929577464789, 'f1': 0.678539626001781, 'number': 1065} 0.5790 0.5956 0.5872 0.7197
0.8256 4.0 8 0.8352 {'precision': 0.5638179800221975, 'recall': 0.6279357231149567, 'f1': 0.5941520467836258, 'number': 809} {'precision': 0.06976744186046512, 'recall': 0.025210084033613446, 'f1': 0.037037037037037035, 'number': 119} {'precision': 0.6519607843137255, 'recall': 0.7492957746478873, 'f1': 0.6972477064220184, 'number': 1065} 0.6038 0.6568 0.6292 0.7434
0.7591 5.0 10 0.7736 {'precision': 0.6031914893617021, 'recall': 0.7008652657601978, 'f1': 0.6483704974271013, 'number': 809} {'precision': 0.10204081632653061, 'recall': 0.04201680672268908, 'f1': 0.05952380952380952, 'number': 119} {'precision': 0.6701612903225806, 'recall': 0.780281690140845, 'f1': 0.7210412147505423, 'number': 1065} 0.6294 0.7040 0.6646 0.7644
0.7242 6.0 12 0.7291 {'precision': 0.5970619097586569, 'recall': 0.7033374536464772, 'f1': 0.6458569807037458, 'number': 809} {'precision': 0.12727272727272726, 'recall': 0.058823529411764705, 'f1': 0.08045977011494251, 'number': 119} {'precision': 0.7053264604810997, 'recall': 0.7708920187793428, 'f1': 0.7366532077164648, 'number': 1065} 0.6432 0.7010 0.6708 0.7638
0.6542 7.0 14 0.6921 {'precision': 0.6148148148148148, 'recall': 0.7181705809641533, 'f1': 0.6624857468643102, 'number': 809} {'precision': 0.1506849315068493, 'recall': 0.09243697478991597, 'f1': 0.11458333333333334, 'number': 119} {'precision': 0.7185929648241206, 'recall': 0.8056338028169014, 'f1': 0.7596281540504649, 'number': 1065} 0.6555 0.7275 0.6897 0.7776
0.6076 8.0 16 0.6709 {'precision': 0.6371220020855057, 'recall': 0.7552533992583437, 'f1': 0.6911764705882354, 'number': 809} {'precision': 0.20253164556962025, 'recall': 0.13445378151260504, 'f1': 0.1616161616161616, 'number': 119} {'precision': 0.7142857142857143, 'recall': 0.8215962441314554, 'f1': 0.7641921397379913, 'number': 1065} 0.6637 0.7536 0.7058 0.7878
0.5743 9.0 18 0.6503 {'precision': 0.6582278481012658, 'recall': 0.7713226205191595, 'f1': 0.7103016505406944, 'number': 809} {'precision': 0.24691358024691357, 'recall': 0.16806722689075632, 'f1': 0.2, 'number': 119} {'precision': 0.7366638441998307, 'recall': 0.8169014084507042, 'f1': 0.7747105966162066, 'number': 1065} 0.6851 0.7597 0.7204 0.7929
0.5316 10.0 20 0.6438 {'precision': 0.6608695652173913, 'recall': 0.7515451174289246, 'f1': 0.7032967032967032, 'number': 809} {'precision': 0.23863636363636365, 'recall': 0.17647058823529413, 'f1': 0.20289855072463767, 'number': 119} {'precision': 0.7390202702702703, 'recall': 0.8215962441314554, 'f1': 0.7781236104935527, 'number': 1065} 0.6861 0.7546 0.7188 0.7944
0.5033 11.0 22 0.6252 {'precision': 0.6775599128540305, 'recall': 0.7688504326328801, 'f1': 0.7203242617255357, 'number': 809} {'precision': 0.3218390804597701, 'recall': 0.23529411764705882, 'f1': 0.27184466019417475, 'number': 119} {'precision': 0.7491467576791809, 'recall': 0.8244131455399061, 'f1': 0.7849798837729102, 'number': 1065} 0.7019 0.7667 0.7329 0.8021
0.5073 12.0 24 0.6364 {'precision': 0.6750261233019854, 'recall': 0.7985166872682324, 'f1': 0.7315968289920726, 'number': 809} {'precision': 0.30612244897959184, 'recall': 0.25210084033613445, 'f1': 0.2764976958525346, 'number': 119} {'precision': 0.7528089887640449, 'recall': 0.8178403755868544, 'f1': 0.7839783978397841, 'number': 1065} 0.6994 0.7762 0.7358 0.7987
0.4453 13.0 26 0.6300 {'precision': 0.6767782426778243, 'recall': 0.799752781211372, 'f1': 0.7331444759206799, 'number': 809} {'precision': 0.26785714285714285, 'recall': 0.25210084033613445, 'f1': 0.2597402597402597, 'number': 119} {'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065} 0.6947 0.7842 0.7367 0.8006
0.4563 14.0 28 0.6225 {'precision': 0.6713819368879217, 'recall': 0.7626699629171817, 'f1': 0.7141203703703703, 'number': 809} {'precision': 0.2743362831858407, 'recall': 0.2605042016806723, 'f1': 0.26724137931034486, 'number': 119} {'precision': 0.7542662116040956, 'recall': 0.8300469483568075, 'f1': 0.7903442109968708, 'number': 1065} 0.6951 0.7687 0.7300 0.7987
0.414 15.0 30 0.6206 {'precision': 0.6900328587075575, 'recall': 0.7787391841779975, 'f1': 0.7317073170731707, 'number': 809} {'precision': 0.27884615384615385, 'recall': 0.24369747899159663, 'f1': 0.2600896860986547, 'number': 119} {'precision': 0.7674624226348364, 'recall': 0.8150234741784037, 'f1': 0.7905282331511838, 'number': 1065} 0.7109 0.7662 0.7375 0.8033
0.4023 16.0 32 0.6221 {'precision': 0.6893203883495146, 'recall': 0.7898640296662547, 'f1': 0.7361751152073734, 'number': 809} {'precision': 0.2621359223300971, 'recall': 0.226890756302521, 'f1': 0.24324324324324326, 'number': 119} {'precision': 0.7527993109388458, 'recall': 0.8206572769953052, 'f1': 0.7852650494159928, 'number': 1065} 0.7029 0.7727 0.7361 0.8027
0.3783 17.0 34 0.6263 {'precision': 0.693304535637149, 'recall': 0.7935723114956736, 'f1': 0.740057636887608, 'number': 809} {'precision': 0.25892857142857145, 'recall': 0.24369747899159663, 'f1': 0.2510822510822511, 'number': 119} {'precision': 0.7519116397621071, 'recall': 0.8309859154929577, 'f1': 0.7894736842105263, 'number': 1065} 0.7025 0.7807 0.7395 0.8018
0.3868 18.0 36 0.6347 {'precision': 0.6956989247311828, 'recall': 0.799752781211372, 'f1': 0.7441058079355952, 'number': 809} {'precision': 0.2845528455284553, 'recall': 0.29411764705882354, 'f1': 0.2892561983471075, 'number': 119} {'precision': 0.771729587357331, 'recall': 0.8253521126760563, 'f1': 0.7976406533575316, 'number': 1065} 0.7121 0.7832 0.7460 0.8027
0.324 19.0 38 0.6480 {'precision': 0.6813880126182965, 'recall': 0.8009888751545118, 'f1': 0.7363636363636363, 'number': 809} {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119} {'precision': 0.775692582663092, 'recall': 0.8150234741784037, 'f1': 0.7948717948717948, 'number': 1065} 0.7066 0.7782 0.7407 0.7999
0.3436 20.0 40 0.6438 {'precision': 0.6919786096256685, 'recall': 0.799752781211372, 'f1': 0.7419724770642202, 'number': 809} {'precision': 0.2682926829268293, 'recall': 0.2773109243697479, 'f1': 0.27272727272727276, 'number': 119} {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065} 0.7125 0.7832 0.7462 0.8015
0.3081 21.0 42 0.6469 {'precision': 0.7048458149779736, 'recall': 0.7911001236093943, 'f1': 0.7454863133372162, 'number': 809} {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119} {'precision': 0.7796167247386759, 'recall': 0.8403755868544601, 'f1': 0.8088567555354722, 'number': 1065} 0.7222 0.7878 0.7535 0.8075
0.3109 22.0 44 0.6553 {'precision': 0.6980306345733042, 'recall': 0.788627935723115, 'f1': 0.7405687753917585, 'number': 809} {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119} {'precision': 0.7737991266375546, 'recall': 0.831924882629108, 'f1': 0.8018099547511313, 'number': 1065} 0.7163 0.7817 0.7476 0.8024
0.3021 23.0 46 0.6704 {'precision': 0.7031763417305587, 'recall': 0.7935723114956736, 'f1': 0.7456445993031359, 'number': 809} {'precision': 0.275, 'recall': 0.2773109243697479, 'f1': 0.27615062761506276, 'number': 119} {'precision': 0.78584229390681, 'recall': 0.8234741784037559, 'f1': 0.8042182485098579, 'number': 1065} 0.7222 0.7787 0.7494 0.7991
0.2921 24.0 48 0.6767 {'precision': 0.7011995637949836, 'recall': 0.7948084054388134, 'f1': 0.7450753186558517, 'number': 809} {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119} {'precision': 0.7777777777777778, 'recall': 0.8215962441314554, 'f1': 0.7990867579908676, 'number': 1065} 0.7192 0.7787 0.7478 0.7980
0.2837 25.0 50 0.6758 {'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809} {'precision': 0.2982456140350877, 'recall': 0.2857142857142857, 'f1': 0.2918454935622318, 'number': 119} {'precision': 0.7662901824500434, 'recall': 0.828169014084507, 'f1': 0.796028880866426, 'number': 1065} 0.7135 0.7822 0.7463 0.7983
0.2565 26.0 52 0.6793 {'precision': 0.6942949407965554, 'recall': 0.7972805933250927, 'f1': 0.7422324510932106, 'number': 809} {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119} {'precision': 0.7700348432055749, 'recall': 0.8300469483568075, 'f1': 0.7989154993221871, 'number': 1065} 0.7148 0.7847 0.7481 0.7970
0.2487 27.0 54 0.6859 {'precision': 0.6886993603411514, 'recall': 0.7985166872682324, 'f1': 0.7395535203205496, 'number': 809} {'precision': 0.3275862068965517, 'recall': 0.31932773109243695, 'f1': 0.3234042553191489, 'number': 119} {'precision': 0.7835420393559929, 'recall': 0.8225352112676056, 'f1': 0.8025652771415483, 'number': 1065} 0.7182 0.7827 0.7491 0.7959
0.2663 28.0 56 0.6907 {'precision': 0.692390139335477, 'recall': 0.7985166872682324, 'f1': 0.7416762342135478, 'number': 809} {'precision': 0.36065573770491804, 'recall': 0.3697478991596639, 'f1': 0.36514522821576767, 'number': 119} {'precision': 0.793418647166362, 'recall': 0.8150234741784037, 'f1': 0.8040759610930986, 'number': 1065} 0.7250 0.7817 0.7523 0.7938
0.2679 29.0 58 0.6872 {'precision': 0.7095343680709535, 'recall': 0.7911001236093943, 'f1': 0.7481005260081823, 'number': 809} {'precision': 0.3435114503816794, 'recall': 0.37815126050420167, 'f1': 0.36, 'number': 119} {'precision': 0.7789566755083996, 'recall': 0.8272300469483568, 'f1': 0.802367941712204, 'number': 1065} 0.7237 0.7858 0.7534 0.7990
0.2272 30.0 60 0.6887 {'precision': 0.7052401746724891, 'recall': 0.7985166872682324, 'f1': 0.7489855072463769, 'number': 809} {'precision': 0.3706896551724138, 'recall': 0.36134453781512604, 'f1': 0.36595744680851067, 'number': 119} {'precision': 0.7846425419240953, 'recall': 0.8347417840375587, 'f1': 0.8089171974522293, 'number': 1065} 0.7289 0.7918 0.7590 0.7977
0.2263 31.0 62 0.6959 {'precision': 0.6960257787325457, 'recall': 0.8009888751545118, 'f1': 0.7448275862068966, 'number': 809} {'precision': 0.35135135135135137, 'recall': 0.3277310924369748, 'f1': 0.3391304347826087, 'number': 119} {'precision': 0.7912578055307761, 'recall': 0.8328638497652582, 'f1': 0.8115279048490394, 'number': 1065} 0.7277 0.7898 0.7575 0.7973
0.2366 32.0 64 0.6995 {'precision': 0.6982758620689655, 'recall': 0.8009888751545118, 'f1': 0.7461139896373058, 'number': 809} {'precision': 0.3652173913043478, 'recall': 0.35294117647058826, 'f1': 0.35897435897435903, 'number': 119} {'precision': 0.7920792079207921, 'recall': 0.8262910798122066, 'f1': 0.8088235294117647, 'number': 1065} 0.7289 0.7878 0.7572 0.7963
0.214 33.0 66 0.6985 {'precision': 0.7050438596491229, 'recall': 0.7948084054388134, 'f1': 0.747239976757699, 'number': 809} {'precision': 0.36585365853658536, 'recall': 0.37815126050420167, 'f1': 0.371900826446281, 'number': 119} {'precision': 0.7907390917186109, 'recall': 0.8338028169014085, 'f1': 0.8117001828153564, 'number': 1065} 0.7303 0.7908 0.7593 0.7990
0.2189 34.0 68 0.6991 {'precision': 0.7067833698030634, 'recall': 0.7985166872682324, 'f1': 0.7498549042367965, 'number': 809} {'precision': 0.3524590163934426, 'recall': 0.36134453781512604, 'f1': 0.35684647302904565, 'number': 119} {'precision': 0.7921847246891652, 'recall': 0.8375586854460094, 'f1': 0.8142400730260155, 'number': 1065} 0.7313 0.7933 0.7610 0.8012
0.1994 35.0 70 0.7038 {'precision': 0.6935312831389183, 'recall': 0.8084054388133498, 'f1': 0.7465753424657534, 'number': 809} {'precision': 0.3684210526315789, 'recall': 0.35294117647058826, 'f1': 0.3605150214592275, 'number': 119} {'precision': 0.7903225806451613, 'recall': 0.828169014084507, 'f1': 0.8088033012379642, 'number': 1065} 0.7262 0.7918 0.7576 0.7990
0.2139 36.0 72 0.7073 {'precision': 0.6878914405010439, 'recall': 0.8145859085290482, 'f1': 0.745897000565931, 'number': 809} {'precision': 0.3761467889908257, 'recall': 0.3445378151260504, 'f1': 0.3596491228070175, 'number': 119} {'precision': 0.7965641952983725, 'recall': 0.8272300469483568, 'f1': 0.8116075541225242, 'number': 1065} 0.7276 0.7933 0.7590 0.7984
0.2208 37.0 74 0.7039 {'precision': 0.6869109947643979, 'recall': 0.8108776266996292, 'f1': 0.7437641723356009, 'number': 809} {'precision': 0.3853211009174312, 'recall': 0.35294117647058826, 'f1': 0.36842105263157904, 'number': 119} {'precision': 0.7980072463768116, 'recall': 0.8272300469483568, 'f1': 0.8123559243891194, 'number': 1065} 0.7283 0.7923 0.7590 0.8012
0.2015 38.0 76 0.7031 {'precision': 0.7013963480128894, 'recall': 0.8071693448702101, 'f1': 0.7505747126436783, 'number': 809} {'precision': 0.3805309734513274, 'recall': 0.36134453781512604, 'f1': 0.3706896551724138, 'number': 119} {'precision': 0.7974683544303798, 'recall': 0.828169014084507, 'f1': 0.8125287885766928, 'number': 1065} 0.7340 0.7918 0.7618 0.8060
0.2028 39.0 78 0.7049 {'precision': 0.7100656455142232, 'recall': 0.8022249690976514, 'f1': 0.7533372025536854, 'number': 809} {'precision': 0.37606837606837606, 'recall': 0.3697478991596639, 'f1': 0.3728813559322034, 'number': 119} {'precision': 0.7965796579657966, 'recall': 0.8309859154929577, 'f1': 0.8134191176470588, 'number': 1065} 0.7367 0.7918 0.7632 0.8050
0.1794 40.0 80 0.7078 {'precision': 0.7075575027382256, 'recall': 0.7985166872682324, 'f1': 0.7502903600464577, 'number': 809} {'precision': 0.3728813559322034, 'recall': 0.3697478991596639, 'f1': 0.37130801687763715, 'number': 119} {'precision': 0.799819657348963, 'recall': 0.8328638497652582, 'f1': 0.8160073597056118, 'number': 1065} 0.7369 0.7913 0.7631 0.8041
0.1939 41.0 82 0.7132 {'precision': 0.7007534983853606, 'recall': 0.8046971569839307, 'f1': 0.7491369390103566, 'number': 809} {'precision': 0.3793103448275862, 'recall': 0.3697478991596639, 'f1': 0.374468085106383, 'number': 119} {'precision': 0.8, 'recall': 0.8300469483568075, 'f1': 0.8147465437788018, 'number': 1065} 0.7344 0.7923 0.7622 0.8004
0.1763 42.0 84 0.7196 {'precision': 0.697228144989339, 'recall': 0.8084054388133498, 'f1': 0.748712077847739, 'number': 809} {'precision': 0.3826086956521739, 'recall': 0.3697478991596639, 'f1': 0.37606837606837606, 'number': 119} {'precision': 0.7969314079422383, 'recall': 0.8291079812206573, 'f1': 0.8127013345605153, 'number': 1065} 0.7316 0.7933 0.7612 0.7983
0.1864 43.0 86 0.7207 {'precision': 0.7008547008547008, 'recall': 0.8108776266996292, 'f1': 0.7518624641833811, 'number': 809} {'precision': 0.37606837606837606, 'recall': 0.3697478991596639, 'f1': 0.3728813559322034, 'number': 119} {'precision': 0.7992766726943942, 'recall': 0.8300469483568075, 'f1': 0.8143712574850298, 'number': 1065} 0.7337 0.7948 0.7630 0.7991
0.1852 44.0 88 0.7192 {'precision': 0.7067099567099567, 'recall': 0.8071693448702101, 'f1': 0.753606462781304, 'number': 809} {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119} {'precision': 0.7992799279927992, 'recall': 0.8338028169014085, 'f1': 0.8161764705882354, 'number': 1065} 0.7358 0.7953 0.7644 0.8012
0.1821 45.0 90 0.7190 {'precision': 0.7071583514099783, 'recall': 0.8059332509270705, 'f1': 0.753321779318313, 'number': 809} {'precision': 0.3706896551724138, 'recall': 0.36134453781512604, 'f1': 0.36595744680851067, 'number': 119} {'precision': 0.8034265103697025, 'recall': 0.8366197183098592, 'f1': 0.8196872125114996, 'number': 1065} 0.7387 0.7958 0.7662 0.8018
0.1804 46.0 92 0.7194 {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809} {'precision': 0.3739130434782609, 'recall': 0.36134453781512604, 'f1': 0.36752136752136755, 'number': 119} {'precision': 0.8016230838593328, 'recall': 0.8347417840375587, 'f1': 0.8178472861085557, 'number': 1065} 0.7401 0.7943 0.7662 0.8011
0.1879 47.0 94 0.7206 {'precision': 0.7099236641221374, 'recall': 0.8046971569839307, 'f1': 0.7543453070683662, 'number': 809} {'precision': 0.3739130434782609, 'recall': 0.36134453781512604, 'f1': 0.36752136752136755, 'number': 119} {'precision': 0.8052536231884058, 'recall': 0.8347417840375587, 'f1': 0.8197325956662057, 'number': 1065} 0.7411 0.7943 0.7668 0.8015
0.1754 48.0 96 0.7223 {'precision': 0.7074756229685807, 'recall': 0.8071693448702101, 'f1': 0.754041570438799, 'number': 809} {'precision': 0.37719298245614036, 'recall': 0.36134453781512604, 'f1': 0.36909871244635195, 'number': 119} {'precision': 0.8070973612374887, 'recall': 0.8328638497652582, 'f1': 0.8197781885397413, 'number': 1065} 0.7411 0.7943 0.7668 0.8010
0.1712 49.0 98 0.7238 {'precision': 0.705945945945946, 'recall': 0.8071693448702101, 'f1': 0.7531718569780853, 'number': 809} {'precision': 0.37719298245614036, 'recall': 0.36134453781512604, 'f1': 0.36909871244635195, 'number': 119} {'precision': 0.8056312443233424, 'recall': 0.8328638497652582, 'f1': 0.8190212373037857, 'number': 1065} 0.7397 0.7943 0.7660 0.8005
0.1834 50.0 100 0.7243 {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809} {'precision': 0.3706896551724138, 'recall': 0.36134453781512604, 'f1': 0.36595744680851067, 'number': 119} {'precision': 0.8041704442429737, 'recall': 0.8328638497652582, 'f1': 0.8182656826568265, 'number': 1065} 0.7380 0.7943 0.7651 0.8003

Framework versions

  • Transformers 4.22.1
  • Pytorch 1.12.1+cu102
  • Datasets 2.5.1
  • Tokenizers 0.12.1