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bart-base-paraphrasing
This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.309600
- Rouge1: 37.346600
- Rouge2: 31.232000
- Rougel: 35.649300
- Rougelsum: 36.620700
- Gen Len: 20.0
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: 4e-05
- train_batch_size: 27
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
---|---|---|---|---|---|---|---|---|
1.272100 | 15 | 1 | 0.728453 | 35.610300 | 28.460200 | 33.443200 | 34.660100 | 19.957500 |
0.828400 | 30 | 1 | 0.672391 | 35.944600 | 29.183200 | 33.994800 | 35.159600 | 19.961500 |
0.750400 | 45 | 1 | 0.621431 | 36.373600 | 29.659600 | 34.441700 | 35.605300 | 19.977000 |
0.728900 | 60 | 1 | 0.597063 | 36.034900 | 29.380400 | 34.177700 | 35.257100 | 19.970500 |
0.699800 | 75 | 1 | 0.585529 | 35.308700 | 28.488400 | 33.353300 | 34.456300 | 19.971500 |
0.698900 | 90 | 1 | 0.560137 | 35.956300 | 29.453500 | 34.155300 | 35.154300 | 19.970500 |
0.669100 | 105 | 1 | 0.555273 | 36.017400 | 29.399500 | 34.099400 | 35.162900 | 19.972500 |
0.637600 | 120 | 1 | 0.551375 | 36.357600 | 29.783200 | 34.549300 | 35.561400 | 19.976000 |
0.653500 | 135 | 1 | 0.530873 | 36.578900 | 30.080800 | 34.764800 | 35.789300 | 19.970000 |
0.597800 | 150 | 1 | 0.528142 | 36.219800 | 29.791800 | 34.459700 | 35.407700 | 19.974000 |
0.626600 | 165 | 1 | 0.510571 | 36.251200 | 29.698900 | 34.422100 | 35.432400 | 19.972500 |
0.585100 | 180 | 1 | 0.504067 | 36.191500 | 29.743700 | 34.408400 | 35.401300 | 19.969000 |
0.576700 | 195 | 1 | 0.495318 | 36.648900 | 30.248700 | 34.869300 | 35.885300 | 19.974000 |
0.549200 | 210 | 1 | 0.494409 | 36.392600 | 30.035800 | 34.577200 | 35.623000 | 19.972000 |
0.570000 | 225 | 1 | 0.479456 | 36.339100 | 29.928200 | 34.589300 | 35.569900 | 19.965500 |
0.550600 | 240 | 1 | 0.473431 | 36.646300 | 30.312000 | 34.851800 | 35.861300 | 19.964500 |
0.566200 | 255 | 1 | 0.471991 | 36.514700 | 30.070500 | 34.630700 | 35.685000 | 19.968500 |
0.539100 | 270 | 1 | 0.459127 | 36.328600 | 29.984900 | 34.568200 | 35.487200 | 19.968500 |
0.527300 | 285 | 1 | 0.449097 | 36.541300 | 30.132600 | 34.705300 | 35.714000 | 19.968500 |
0.521300 | 300 | 2 | 0.448960 | 35.926800 | 29.508400 | 34.115800 | 35.147400 | 19.973000 |
0.471900 | 315 | 2 | 0.443209 | 36.748400 | 30.365400 | 34.966500 | 35.956900 | 19.968500 |
0.499300 | 330 | 2 | 0.439178 | 36.783700 | 30.461400 | 35.037900 | 36.023900 | 19.968500 |
0.473100 | 345 | 2 | 0.422886 | 36.773600 | 30.514500 | 35.021000 | 35.998200 | 19.973500 |
0.459500 | 360 | 2 | 0.422479 | 37.235700 | 30.945100 | 35.394200 | 36.474400 | 19.970000 |
0.454900 | 375 | 2 | 0.421957 | 36.685800 | 30.390300 | 34.903800 | 35.925700 | 19.968500 |
0.456400 | 390 | 2 | 0.427490 | 36.233400 | 29.811500 | 34.441800 | 35.424200 | 19.971000 |
0.446300 | 405 | 2 | 0.420770 | 36.860900 | 30.457600 | 35.035000 | 36.068700 | 19.968500 |
0.462600 | 420 | 2 | 0.421138 | 36.468000 | 29.979500 | 34.586800 | 35.633500 | 19.971000 |
0.432000 | 435 | 2 | 0.411133 | 37.028300 | 30.761300 | 35.271100 | 36.271500 | 19.971500 |
0.470200 | 450 | 2 | 0.411541 | 36.740200 | 30.499000 | 34.988000 | 35.977300 | 19.968000 |
0.447200 | 465 | 2 | 0.402041 | 37.204600 | 30.997600 | 35.446300 | 36.492300 | 19.960500 |
0.461100 | 480 | 2 | 0.409818 | 36.912900 | 30.706900 | 35.156600 | 36.150000 | 19.966500 |
0.448500 | 495 | 2 | 0.412397 | 36.813800 | 30.550000 | 35.086000 | 36.037500 | 19.965000 |
0.440700 | 510 | 2 | 0.409341 | 36.976300 | 30.703900 | 35.230000 | 36.203300 | 19.968000 |
0.463100 | 525 | 2 | 0.409853 | 37.053500 | 30.862000 | 35.364300 | 36.332600 | 19.971000 |
0.460100 | 540 | 2 | 0.405348 | 36.580600 | 30.349600 | 34.859000 | 35.823700 | 19.966000 |
0.449700 | 555 | 2 | 0.404055 | 36.880000 | 30.500300 | 34.966900 | 36.023600 | 19.973500 |
0.445900 | 570 | 2 | 0.401167 | 37.105100 | 30.894400 | 35.349100 | 36.337700 | 19.969500 |
0.473600 | 585 | 2 | 0.401274 | 36.506000 | 30.272000 | 34.790700 | 35.759000 | 19.971000 |
0.435400 | 600 | 3 | 0.404944 | 37.093100 | 30.850100 | 35.391800 | 36.369500 | 19.971500 |
0.414500 | 615 | 3 | 0.400146 | 36.936300 | 30.789200 | 35.195400 | 36.203700 | 19.966500 |
0.395000 | 630 | 3 | 0.400189 | 37.110100 | 30.915400 | 35.420800 | 36.338100 | 19.966500 |
0.405000 | 645 | 3 | 0.401724 | 36.860300 | 30.623400 | 35.093600 | 36.080900 | 19.969500 |
0.403400 | 660 | 3 | 0.405606 | 36.777100 | 30.546200 | 35.065500 | 36.000200 | 19.969500 |
0.398700 | 675 | 3 | 0.403438 | 36.531700 | 30.283400 | 34.829400 | 35.730400 | 19.969500 |
0.398900 | 690 | 3 | 0.396970 | 36.871100 | 30.672100 | 35.157400 | 36.047400 | 19.970000 |
0.378900 | 705 | 3 | 0.413375 | 37.082500 | 30.848200 | 35.339000 | 36.312200 | 19.966000 |
0.391600 | 720 | 3 | 0.395604 | 37.091600 | 30.925600 | 35.404200 | 36.360200 | 19.969500 |
0.374400 | 735 | 3 | 0.398041 | 37.287600 | 31.112700 | 35.548900 | 36.543700 | 19.969000 |
0.390600 | 750 | 3 | 0.399400 | 37.050800 | 30.844900 | 35.278000 | 36.281900 | 19.969500 |
0.398800 | 765 | 3 | 0.391213 | 37.260900 | 31.090300 | 35.493200 | 36.499800 | 19.961500 |
0.391300 | 780 | 3 | 0.392255 | 37.062100 | 30.859300 | 35.327400 | 36.311500 | 19.968000 |
0.414400 | 795 | 3 | 0.390236 | 37.043600 | 30.738100 | 35.249800 | 36.285500 | 19.968000 |
0.369700 | 810 | 3 | 0.390666 | 36.889500 | 30.710500 | 35.129200 | 36.129500 | 19.968000 |
0.372800 | 825 | 3 | 0.389744 | 37.012200 | 30.853800 | 35.225400 | 36.279300 | 19.966000 |
0.380400 | 840 | 3 | 0.389610 | 36.834300 | 30.671600 | 35.048900 | 36.063700 | 19.966000 |
0.369000 | 855 | 3 | 0.385031 | 37.137800 | 31.043000 | 35.421100 | 36.393500 | 19.964500 |
0.386700 | 870 | 3 | 0.394869 | 36.993300 | 30.773100 | 35.204100 | 36.215400 | 19.966000 |
0.389100 | 885 | 3 | 0.387872 | 36.994300 | 30.764100 | 35.276000 | 36.250300 | 19.969500 |
0.381400 | 900 | 4 | 0.384406 | 37.118600 | 30.899300 | 35.351600 | 36.380200 | 19.969500 |
0.372500 | 915 | 4 | 0.386666 | 37.036800 | 31.053500 | 35.317800 | 36.293100 | 19.966000 |
0.351100 | 930 | 4 | 0.390876 | 36.950600 | 30.806400 | 35.247800 | 36.190500 | 19.963000 |
0.349200 | 945 | 4 | 0.391693 | 37.173400 | 31.020000 | 35.406700 | 36.414900 | 19.966000 |
0.350500 | 960 | 4 | 0.383120 | 37.257700 | 31.094200 | 35.502400 | 36.498700 | 19.966000 |
0.390000 | 975 | 4 | 0.384534 | 37.103900 | 30.999200 | 35.392100 | 36.383800 | 19.966000 |
0.343500 | 990 | 4 | 0.384099 | 37.074300 | 30.941700 | 35.361400 | 36.334900 | 19.969500 |
0.347800 | 1005 | 4 | 0.387656 | 37.011900 | 30.834300 | 35.252600 | 36.246700 | 19.968 |
0.359200 | 1020 | 4 | 0.385008 | 37.240300 | 31.078300 | 35.499300 | 36.470500 | 19.968 |
0.344100 | 1035 | 4 | 0.384319 | 37.118000 | 31.010800 | 35.419600 | 36.401000 | 19.966 |
0.344200 | 1050 | 4 | 0.390927 | 36.891900 | 30.697800 | 35.141600 | 36.116600 | 19.969 |
0.353900 | 1065 | 4 | 0.384563 | 36.790300 | 30.613100 | 35.060500 | 36.012600 | 19.969 |
0.354300 | 1080 | 4 | 0.380220 | 37.132800 | 31.021100 | 35.420000 | 36.377800 | 19.964 |
0.348800 | 1095 | 4 | 0.381104 | 37.158700 | 31.000300 | 35.437500 | 36.430800 | 19.961 |
0.349900 | 1110 | 4 | 0.385718 | 37.154600 | 30.992800 | 35.406500 | 36.413500 | 19.966 |
0.349200 | 1125 | 4 | 0.382857 | 37.023900 | 30.929500 | 35.318300 | 36.293200 | 19.970 |
0.351800 | 1140 | 4 | 0.380331 | 37.171800 | 31.037000 | 35.480200 | 36.478400 | 19.965 |
0.348700 | 1155 | 4 | 0.384382 | 37.249000 | 31.114500 | 35.577100 | 36.544200 | 19.970 |
0.325800 | 1170 | 4 | 0.382947 | 37.177400 | 31.042000 | 35.460600 | 36.450300 | 19.968 |
0.351700 | 1185 | 4 | 0.379098 | 37.160700 | 30.966800 | 35.463100 | 36.449000 | 19.969 |
0.329400 | 1200 | 5 | 0.379832 | 37.211700 | 31.117400 | 35.520400 | 36.500100 | 19.965 |
0.309000 | 1215 | 5 | 0.383461 | 37.303500 | 31.183800 | 35.599000 | 36.614000 | 19.970 |
0.321000 | 1230 | 5 | 0.380275 | 37.177500 | 31.081100 | 35.462400 | 36.473800 | 19.963 |
0.309200 | 1245 | 5 | 0.381899 | 37.235800 | 31.197100 | 35.568800 | 36.528000 | 19.966 |
0.326700 | 1260 | 5 | 0.381356 | 37.410200 | 31.257300 | 35.671300 | 36.697000 | 19.969 |
0.324700 | 1275 | 5 | 0.378781 | 37.407900 | 31.322100 | 35.681000 | 36.683100 | 19.965 |
0.303200 | 1290 | 5 | 0.381087 | 37.355700 | 31.308400 | 35.665500 | 36.628000 | 19.965 |
0.335000 | 1305 | 5 | 0.380627 | 37.274800 | 31.243800 | 35.603400 | 36.559800 | 19.966 |
0.349300 | 1320 | 5 | 0.376487 | 37.299100 | 31.221000 | 35.611200 | 36.573400 | 19.963 |
0.302400 | 1335 | 5 | 0.380785 | 37.333500 | 31.293000 | 35.679900 | 36.650200 | 19.966 |
0.309400 | 1350 | 5 | 0.381105 | 37.280400 | 31.195800 | 35.611700 | 36.565100 | 19.969 |
0.322900 | 1365 | 5 | 0.379658 | 37.368200 | 31.276900 | 35.680000 | 36.654900 | 19.969 |
0.334700 | 1380 | 5 | 0.381676 | 37.362700 | 31.288900 | 35.680600 | 36.643600 | 19.968 |
0.323700 | 1395 | 5 | 0.379920 | 37.312300 | 31.204800 | 35.614800 | 36.583400 | 19.968 |
0.334700 | 1410 | 5 | 0.379366 | 37.310300 | 31.205600 | 35.636400 | 36.595200 | 19.969 |
0.327300 | 1425 | 5 | 0.378289 | 37.275400 | 31.172700 | 35.575500 | 36.549500 | 19.969 |
0.326400 | 1440 | 5 | 0.378255 | 37.270000 | 31.164000 | 35.582100 | 36.543800 | 19.969 |
0.326600 | 1455 | 5 | 0.377739 | 37.300000 | 31.205400 | 35.621500 | 36.586100 | 19.969 |
0.335700 | 1470 | 5 | 0.377524 | 37.287400 | 31.189800 | 35.608700 | 36.578000 | 19.970 |
0.309600 | 1485 | 5 | 0.377617 | 37.346600 | 31.232000 | 35.649300 | 36.620700 | 19.969 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3