How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="admko/sembr2023-distilbert-base-uncased-finetuned-sst-2-english")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("admko/sembr2023-distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForTokenClassification.from_pretrained("admko/sembr2023-distilbert-base-uncased-finetuned-sst-2-english")
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sembr2023-distilbert-base-uncased-finetuned-sst-2-english

This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2242
  • Precision: 0.8042
  • Recall: 0.8338
  • F1: 0.8187
  • Iou: 0.6930
  • Accuracy: 0.9660
  • Balanced Accuracy: 0.9066
  • Overall Accuracy: 0.9529

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: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Iou Accuracy Balanced Accuracy Overall Accuracy
0.3902 0.06 10 0.3870 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.3064 0.12 20 0.3030 1.0 0.0024 0.0048 0.0024 0.9083 0.5012 0.9083
0.2489 0.18 30 0.2335 0.7510 0.6056 0.6705 0.5043 0.9453 0.7926 0.9288
0.1931 0.24 40 0.1983 0.7924 0.6957 0.7409 0.5884 0.9552 0.8386 0.9385
0.1417 0.3 50 0.1830 0.8148 0.7208 0.7649 0.6193 0.9593 0.8521 0.9412
0.1581 0.36 60 0.1756 0.8102 0.7475 0.7776 0.6361 0.9607 0.8649 0.9430
0.1572 0.42 70 0.1681 0.7986 0.7811 0.7898 0.6526 0.9618 0.8806 0.9440
0.1304 0.48 80 0.1617 0.7915 0.7978 0.7946 0.6593 0.9621 0.8883 0.9442
0.1203 0.55 90 0.1554 0.8233 0.7706 0.7961 0.6612 0.9637 0.8769 0.9471
0.1249 0.61 100 0.1782 0.7805 0.8080 0.7940 0.6584 0.9614 0.8925 0.9423
0.1212 0.67 110 0.1502 0.8589 0.7550 0.8036 0.6717 0.9661 0.8712 0.9494
0.0883 0.73 120 0.1555 0.8076 0.8120 0.8098 0.6803 0.9649 0.8962 0.9457
0.0921 0.79 130 0.1561 0.8156 0.7936 0.8044 0.6729 0.9645 0.8877 0.9471
0.112 0.85 140 0.1444 0.7773 0.8389 0.8069 0.6763 0.9631 0.9073 0.9485
0.0858 0.91 150 0.1530 0.8258 0.7806 0.8026 0.6702 0.9647 0.8820 0.9490
0.076 0.97 160 0.1355 0.8478 0.7679 0.8059 0.6749 0.9660 0.8770 0.9537
0.0891 1.03 170 0.1468 0.8333 0.7996 0.8161 0.6893 0.9669 0.8917 0.9512
0.0727 1.09 180 0.1394 0.8659 0.7685 0.8143 0.6868 0.9678 0.8782 0.9543
0.0707 1.15 190 0.1396 0.8585 0.7792 0.8170 0.6906 0.9679 0.8831 0.9546
0.0827 1.21 200 0.1365 0.8231 0.8098 0.8164 0.6898 0.9665 0.8961 0.9542
0.0628 1.27 210 0.1629 0.8189 0.8157 0.8173 0.6910 0.9665 0.8987 0.9507
0.0544 1.33 220 0.1490 0.8179 0.8182 0.8181 0.6921 0.9665 0.8999 0.9536
0.0581 1.39 230 0.1618 0.7956 0.8346 0.8147 0.6873 0.9651 0.9065 0.9489
0.0508 1.45 240 0.1583 0.8032 0.8191 0.8111 0.6822 0.9649 0.8994 0.9526
0.0477 1.52 250 0.1524 0.8149 0.8223 0.8186 0.6929 0.9665 0.9017 0.9544
0.0493 1.58 260 0.1518 0.8422 0.7969 0.8190 0.6934 0.9676 0.8909 0.9551
0.0586 1.64 270 0.1635 0.8112 0.8194 0.8153 0.6881 0.9658 0.9000 0.9510
0.0438 1.7 280 0.1819 0.7835 0.8446 0.8129 0.6848 0.9642 0.9105 0.9479
0.0544 1.76 290 0.1781 0.8208 0.8190 0.8199 0.6947 0.9669 0.9004 0.9505
0.0527 1.82 300 0.1547 0.8213 0.8157 0.8185 0.6927 0.9667 0.8988 0.9538
0.0449 1.88 310 0.1603 0.8095 0.8301 0.8197 0.6944 0.9664 0.9051 0.9533
0.0556 1.94 320 0.1627 0.7995 0.8312 0.8151 0.6879 0.9653 0.9051 0.9519
0.0459 2.0 330 0.1525 0.8324 0.7990 0.8153 0.6882 0.9667 0.8913 0.9542
0.0401 2.06 340 0.1915 0.7856 0.8469 0.8151 0.6879 0.9647 0.9117 0.9480
0.0384 2.12 350 0.1791 0.8060 0.8282 0.8169 0.6905 0.9659 0.9040 0.9512
0.0358 2.18 360 0.1831 0.8265 0.8084 0.8174 0.6911 0.9668 0.8956 0.9515
0.0263 2.24 370 0.1735 0.8188 0.8118 0.8153 0.6882 0.9662 0.8968 0.9515
0.0313 2.3 380 0.1828 0.7911 0.8408 0.8152 0.6880 0.9649 0.9091 0.9510
0.0323 2.36 390 0.1760 0.8245 0.8194 0.8219 0.6977 0.9673 0.9008 0.9542
0.034 2.42 400 0.1693 0.8306 0.8032 0.8167 0.6901 0.9668 0.8933 0.9540
0.0376 2.48 410 0.1928 0.7556 0.8576 0.8033 0.6713 0.9614 0.9147 0.9481
0.0312 2.55 420 0.1761 0.8197 0.8194 0.8195 0.6942 0.9668 0.9006 0.9537
0.0266 2.61 430 0.1805 0.8175 0.8148 0.8161 0.6894 0.9662 0.8982 0.9534
0.0388 2.67 440 0.2017 0.7774 0.8571 0.8153 0.6882 0.9643 0.9161 0.9477
0.0337 2.73 450 0.1764 0.8145 0.8195 0.8170 0.6906 0.9662 0.9003 0.9530
0.0255 2.79 460 0.1786 0.8267 0.8039 0.8152 0.6880 0.9665 0.8934 0.9533
0.0299 2.85 470 0.1844 0.8048 0.8343 0.8193 0.6939 0.9662 0.9069 0.9529
0.0264 2.91 480 0.1865 0.7909 0.8455 0.8173 0.6910 0.9652 0.9114 0.9526
0.0282 2.97 490 0.1816 0.8105 0.8206 0.8155 0.6885 0.9659 0.9006 0.9532
0.0269 3.03 500 0.1969 0.8195 0.8178 0.8187 0.6930 0.9667 0.8998 0.9516
0.0258 3.09 510 0.2061 0.8070 0.8341 0.8203 0.6954 0.9664 0.9070 0.9513
0.0239 3.15 520 0.2138 0.7878 0.8392 0.8127 0.6845 0.9644 0.9082 0.9492
0.0234 3.21 530 0.1984 0.8203 0.8153 0.8178 0.6917 0.9666 0.8986 0.9521
0.0188 3.27 540 0.2032 0.8081 0.8254 0.8166 0.6901 0.9659 0.9027 0.9513
0.0258 3.33 550 0.2045 0.7976 0.8355 0.8162 0.6894 0.9654 0.9070 0.9524
0.0167 3.39 560 0.2052 0.7851 0.8389 0.8111 0.6822 0.9641 0.9078 0.9512
0.0203 3.45 570 0.2261 0.7912 0.8409 0.8153 0.6882 0.9650 0.9092 0.9489
0.0173 3.52 580 0.2094 0.7816 0.8503 0.8145 0.6871 0.9644 0.9131 0.9489
0.0249 3.58 590 0.2101 0.7968 0.8394 0.8175 0.6914 0.9655 0.9088 0.9515
0.0198 3.64 600 0.2015 0.7947 0.8425 0.8179 0.6919 0.9655 0.9102 0.9516
0.0194 3.7 610 0.2160 0.7895 0.8494 0.8184 0.6926 0.9653 0.9132 0.9505
0.0176 3.76 620 0.2121 0.7893 0.8434 0.8155 0.6885 0.9649 0.9103 0.9506
0.021 3.82 630 0.2020 0.8127 0.8282 0.8204 0.6954 0.9666 0.9044 0.9525
0.019 3.88 640 0.2133 0.8157 0.8266 0.8211 0.6965 0.9669 0.9039 0.9523
0.0184 3.94 650 0.2015 0.8056 0.8303 0.8178 0.6917 0.9660 0.9050 0.9536
0.0202 4.0 660 0.2126 0.8106 0.8201 0.8153 0.6883 0.9658 0.9004 0.9513
0.0182 4.06 670 0.2114 0.8027 0.8320 0.8171 0.6907 0.9657 0.9056 0.9528
0.0174 4.12 680 0.2246 0.7973 0.8375 0.8169 0.6905 0.9655 0.9079 0.9511
0.0149 4.18 690 0.2140 0.8123 0.8259 0.8190 0.6935 0.9664 0.9033 0.9533
0.0135 4.24 700 0.2187 0.8029 0.8329 0.8176 0.6915 0.9658 0.9061 0.9523
0.0202 4.3 710 0.2165 0.8118 0.8250 0.8183 0.6925 0.9663 0.9028 0.9530
0.0139 4.36 720 0.2203 0.8007 0.8332 0.8167 0.6901 0.9656 0.9061 0.9519
0.0153 4.42 730 0.2297 0.7920 0.8429 0.8167 0.6901 0.9652 0.9103 0.9511
0.0144 4.48 740 0.2241 0.8090 0.8330 0.8208 0.6961 0.9666 0.9065 0.9527
0.0113 4.55 750 0.2218 0.8015 0.8352 0.8180 0.6920 0.9658 0.9071 0.9530
0.0192 4.61 760 0.2236 0.8021 0.8376 0.8195 0.6942 0.9661 0.9083 0.9523
0.0162 4.67 770 0.2226 0.7928 0.8434 0.8174 0.6911 0.9653 0.9106 0.9521
0.0144 4.73 780 0.2188 0.8054 0.8361 0.8204 0.6955 0.9663 0.9078 0.9531
0.0159 4.79 790 0.2234 0.8022 0.8359 0.8187 0.6931 0.9660 0.9075 0.9525
0.0136 4.85 800 0.2230 0.8033 0.8334 0.8181 0.6921 0.9659 0.9064 0.9529
0.0197 4.91 810 0.2239 0.8020 0.8363 0.8188 0.6932 0.9660 0.9077 0.9525
0.0165 4.97 820 0.2212 0.8048 0.8339 0.8191 0.6936 0.9661 0.9067 0.9524
0.0146 5.03 830 0.2228 0.8071 0.8308 0.8188 0.6932 0.9662 0.9054 0.9528
0.0109 5.09 840 0.2255 0.8079 0.8311 0.8193 0.6940 0.9663 0.9055 0.9530
0.0104 5.15 850 0.2235 0.8066 0.8316 0.8189 0.6934 0.9662 0.9057 0.9534
0.0152 5.21 860 0.2239 0.8051 0.8331 0.8189 0.6933 0.9661 0.9063 0.9532
0.0118 5.27 870 0.2242 0.8002 0.8389 0.8191 0.6936 0.9659 0.9088 0.9526
0.0106 5.33 880 0.2225 0.8047 0.8334 0.8188 0.6932 0.9661 0.9064 0.9527
0.0127 5.39 890 0.2232 0.8017 0.8349 0.8180 0.6920 0.9658 0.9070 0.9526
0.0126 5.45 900 0.2246 0.8026 0.8343 0.8181 0.6922 0.9659 0.9067 0.9527
0.0159 5.52 910 0.2241 0.8041 0.8343 0.8189 0.6933 0.9661 0.9068 0.9529
0.0182 5.58 920 0.2245 0.8060 0.8327 0.8192 0.6937 0.9662 0.9062 0.9529
0.0154 5.64 930 0.2251 0.8041 0.8331 0.8184 0.6926 0.9660 0.9063 0.9527
0.012 5.7 940 0.2245 0.8036 0.8343 0.8186 0.6929 0.9660 0.9068 0.9529
0.0177 5.76 950 0.2246 0.8035 0.8344 0.8186 0.6930 0.9660 0.9069 0.9528
0.0162 5.82 960 0.2245 0.8040 0.8340 0.8187 0.6931 0.9660 0.9067 0.9529
0.0177 5.88 970 0.2243 0.8040 0.8338 0.8186 0.6929 0.9660 0.9066 0.9529
0.0147 5.94 980 0.2242 0.8041 0.8336 0.8186 0.6929 0.9660 0.9065 0.9529
0.0123 6.0 990 0.2242 0.8042 0.8338 0.8187 0.6930 0.9660 0.9066 0.9529
0.0123 6.06 1000 0.2242 0.8042 0.8338 0.8187 0.6930 0.9660 0.9066 0.9529

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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