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services-ucacue

Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
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metadata 
license: apache-2.0
base_model: BSC-LT/roberta-base-bne
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: services-ucacue
    results: []
pipeline_tag: text-classification

services-ucacue

This model is a fine-tuned version of BSC-LT/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2478
  • Accuracy: 0.8352

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: 5e-05
  • train_batch_size: 40
  • eval_batch_size: 48
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3181 0.16 100 0.9069 0.6518
0.7432 0.32 200 0.6677 0.7551
0.6287 0.47 300 0.5875 0.7858
0.5838 0.63 400 0.5399 0.7963
0.5493 0.79 500 0.5858 0.7871
0.517 0.95 600 0.5136 0.8102
0.4556 1.11 700 0.5451 0.7950
0.4213 1.27 800 0.5288 0.7969
0.4168 1.42 900 0.4665 0.8267
0.4234 1.58 1000 0.4680 0.8346
0.4202 1.74 1100 0.4615 0.8327
0.4343 1.9 1200 0.4756 0.8251
0.3699 2.06 1300 0.5059 0.8403
0.2934 2.22 1400 0.4621 0.8321
0.3074 2.37 1500 0.5008 0.8394
0.3213 2.53 1600 0.4685 0.8343
0.309 2.69 1700 0.4761 0.8390
0.2922 2.85 1800 0.4530 0.8387
0.2996 3.01 1900 0.5078 0.8352
0.1917 3.16 2000 0.6382 0.8248
0.1817 3.32 2100 0.5286 0.8305
0.2172 3.48 2200 0.5374 0.8356
0.225 3.64 2300 0.5987 0.8226
0.2306 3.8 2400 0.5182 0.8447
0.2348 3.96 2500 0.5315 0.8346
0.1636 4.11 2600 0.6174 0.8295
0.145 4.27 2700 0.5829 0.8330
0.159 4.43 2800 0.6558 0.8352
0.1546 4.59 2900 0.5983 0.8279
0.1674 4.75 3000 0.5318 0.8349
0.1667 4.91 3100 0.6102 0.8330
0.1553 5.06 3200 0.7027 0.8264
0.1047 5.22 3300 0.8185 0.8324
0.1294 5.38 3400 0.7657 0.8349
0.1287 5.54 3500 0.7114 0.8340
0.1403 5.7 3600 0.6230 0.8321
0.1358 5.85 3700 0.6789 0.8349
0.119 6.01 3800 0.6755 0.8435
0.0812 6.17 3900 0.8343 0.8305
0.0977 6.33 4000 0.8252 0.8251
0.1036 6.49 4100 0.8672 0.8298
0.1011 6.65 4200 0.8164 0.8245
0.1303 6.8 4300 0.7829 0.8311
0.121 6.96 4400 0.6958 0.8343
0.0797 7.12 4500 0.9208 0.8394
0.0832 7.28 4600 0.8302 0.8352
0.0869 7.44 4700 0.9605 0.8333
0.0825 7.59 4800 0.9242 0.8295
0.1019 7.75 4900 0.8342 0.8337
0.1081 7.91 5000 0.8462 0.8305
0.1016 8.07 5100 0.8536 0.8257
0.078 8.23 5200 0.9047 0.8298
0.0778 8.39 5300 0.9631 0.8292
0.0723 8.54 5400 0.9283 0.8327
0.0875 8.7 5500 0.9040 0.8305
0.0899 8.86 5600 0.8884 0.8305
0.0803 9.02 5700 0.9168 0.8321
0.0549 9.18 5800 1.0361 0.8378
0.0697 9.34 5900 1.0312 0.8413
0.0714 9.49 6000 0.9170 0.8381
0.0789 9.65 6100 0.8447 0.8352
0.0673 9.81 6200 0.8850 0.8327
0.0773 9.97 6300 0.9276 0.8403
0.0577 10.13 6400 0.8892 0.8368
0.0517 10.28 6500 1.0524 0.8264
0.0551 10.44 6600 0.9936 0.8260
0.0532 10.6 6700 1.1169 0.8321
0.0726 10.76 6800 1.0498 0.8273
0.0608 10.92 6900 0.9969 0.8343
0.0598 11.08 7000 1.0024 0.8371
0.0502 11.23 7100 1.0547 0.8251
0.0615 11.39 7200 0.9235 0.8298
0.0545 11.55 7300 0.9389 0.8362
0.0565 11.71 7400 0.8622 0.8390
0.0601 11.87 7500 0.9792 0.8381
0.0623 12.03 7600 1.0572 0.8359
0.0494 12.18 7700 1.0454 0.8394
0.0561 12.34 7800 1.0160 0.8390
0.0459 12.5 7900 1.0492 0.8384
0.0539 12.66 8000 0.9913 0.8413
0.052 12.82 8100 0.9678 0.8394
0.0524 12.97 8200 0.9991 0.8359
0.0476 13.13 8300 0.9980 0.8359
0.0384 13.29 8400 1.0535 0.8365
0.0484 13.45 8500 1.0327 0.8416
0.0461 13.61 8600 1.0804 0.8406
0.056 13.77 8700 1.0189 0.8359
0.0499 13.92 8800 1.0734 0.8349
0.0463 14.08 8900 1.0612 0.8343
0.0409 14.24 9000 1.1206 0.8321
0.043 14.4 9100 1.0902 0.8368
0.0391 14.56 9200 1.0407 0.8340
0.0438 14.72 9300 1.0803 0.8352
0.0404 14.87 9400 1.0797 0.8362
0.0514 15.03 9500 1.1111 0.8365
0.0341 15.19 9600 1.1324 0.8337
0.0399 15.35 9700 1.1461 0.8375
0.0486 15.51 9800 1.0840 0.8375
0.0396 15.66 9900 1.1105 0.8340
0.0411 15.82 10000 1.0873 0.8362
0.0391 15.98 10100 1.1769 0.8333
0.0419 16.14 10200 1.1856 0.8324
0.0371 16.3 10300 1.2263 0.8292
0.0361 16.46 10400 1.2021 0.8333
0.0374 16.61 10500 1.2242 0.8292
0.0383 16.77 10600 1.1600 0.8384
0.035 16.93 10700 1.1955 0.8356
0.0378 17.09 10800 1.1868 0.8340
0.0372 17.25 10900 1.2195 0.8302
0.037 17.41 11000 1.2149 0.8324
0.0342 17.56 11100 1.2127 0.8337
0.035 17.72 11200 1.2074 0.8362
0.0405 17.88 11300 1.2263 0.8327
0.0343 18.04 11400 1.2197 0.8333
0.0349 18.2 11500 1.2334 0.8337
0.0378 18.35 11600 1.2108 0.8365
0.0298 18.51 11700 1.2167 0.8356
0.0404 18.67 11800 1.2331 0.8371
0.0342 18.83 11900 1.2202 0.8337
0.0331 18.99 12000 1.2222 0.8346
0.032 19.15 12100 1.2287 0.8337
0.0299 19.3 12200 1.2368 0.8333
0.0332 19.46 12300 1.2439 0.8352
0.0353 19.62 12400 1.2481 0.8359
0.0353 19.78 12500 1.2485 0.8349
0.0304 19.94 12600 1.2478 0.8352

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2