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openllama-3b-finance

Text Classification
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metadata
license: apache-2.0
base_model: openlm-research/open_llama_3b_v2
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - accuracy
model-index:
  - name: openllama-3b-finance
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_50agree
          split: train
          args: sentences_50agree
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4142561983471074

openllama-3b-finance

This model is a fine-tuned version of openlm-research/open_llama_3b_v2 on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

  • Loss: 3.9007
  • Accuracy: 0.4143

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: 0.0002
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy
16.4346 0.01 20 2.5105 0.4143
1.8681 0.01 40 5.7312 0.4143
1.8542 0.02 60 5.0027 0.4143
2.3731 0.02 80 4.2958 0.4143
2.3024 0.03 100 4.9771 0.4143
2.6812 0.03 120 4.6762 0.4143
2.4304 0.04 140 5.2389 0.4143
2.561 0.04 160 4.4461 0.4143
2.08 0.05 180 4.6807 0.4143
4.0186 0.05 200 5.3431 0.4143
2.7261 0.06 220 4.9663 0.4143
1.7432 0.06 240 4.4788 0.4143
2.2759 0.07 260 5.6531 0.4143
1.8702 0.07 280 6.7118 0.4143
2.2412 0.08 300 5.0398 0.4143
1.1515 0.08 320 6.3377 0.4143
2.6582 0.09 340 5.0585 0.4143
2.1056 0.09 360 5.6544 0.4143
3.1513 0.1 380 3.8076 0.4143
2.0003 0.1 400 5.1281 0.4132
19.9181 0.11 420 35.4379 0.4143
29.2872 0.11 440 16.2178 0.4143
3.4213 0.12 460 13.0984 0.4143
1.3358 0.12 480 27.2436 0.4143
4.2725 0.13 500 24.0192 0.4143
4.9844 0.13 520 12.9378 0.1178
7.9312 0.14 540 10.8854 0.4143
1.5126 0.14 560 14.3267 0.4143
3.9021 0.15 580 10.0051 0.4143
3.7081 0.15 600 9.5176 0.1136
3.9107 0.16 620 7.2548 0.4143
2.8381 0.17 640 3.9992 0.4143
3.0625 0.17 660 4.3300 0.4143
1.812 0.18 680 10.6038 0.4143
6.9616 0.18 700 11.0092 0.4143
1.7157 0.19 720 14.8428 0.4143
4.7153 0.19 740 3.6624 0.4143
2.8871 0.2 760 5.7465 0.4143
2.4885 0.2 780 12.4440 0.4143
3.137 0.21 800 14.1504 0.4143
3.0503 0.21 820 14.1326 0.4143
2.9254 0.22 840 16.0438 0.1291
2.711 0.22 860 14.0977 0.4143
4.8591 0.23 880 9.3210 0.1281
2.8734 0.23 900 6.3782 0.4143
2.603 0.24 920 5.1658 0.4143
4.4641 0.24 940 3.9345 0.4143
2.3522 0.25 960 5.5901 0.1436
1.9584 0.25 980 5.0562 0.4143
2.679 0.26 1000 2.5428 0.4143
4.13 0.26 1020 1.3911 0.4143
3.4319 0.27 1040 8.2340 0.4143
1.9382 0.27 1060 8.4589 0.4143
2.2712 0.28 1080 6.0251 0.4143
1.8834 0.28 1100 2.4455 0.1436
0.9941 0.29 1120 8.7371 0.4143
3.3895 0.29 1140 6.2867 0.1426
2.2968 0.3 1160 10.3440 0.4143
4.9047 0.3 1180 8.0926 0.0816
4.6894 0.31 1200 3.7347 0.3698
2.9471 0.32 1220 4.9616 0.4143
2.9446 0.32 1240 5.8887 0.4143
1.6756 0.33 1260 7.0233 0.4143
2.0442 0.33 1280 7.5129 0.1322
3.7822 0.34 1300 3.1115 0.4143
2.0277 0.34 1320 5.9831 0.4143
2.624 0.35 1340 3.2104 0.4143
2.1893 0.35 1360 4.3662 0.1364
3.0973 0.36 1380 3.2219 0.4143
1.9835 0.36 1400 5.1431 0.4143
2.9711 0.37 1420 6.0129 0.4143
3.0045 0.37 1440 3.2609 0.4143
1.0503 0.38 1460 7.6840 0.4143
2.5946 0.38 1480 5.1945 0.4143
2.9221 0.39 1500 3.5226 0.4143
1.5624 0.39 1520 5.3887 0.4143
2.0339 0.4 1540 4.2434 0.4143
2.4852 0.4 1560 4.1994 0.4143
1.7668 0.41 1580 5.5635 0.4143
2.282 0.41 1600 5.1922 0.4143
3.2027 0.42 1620 3.9420 0.4143
2.5766 0.42 1640 4.9683 0.4143
2.268 0.43 1660 6.2959 0.4143
3.2091 0.43 1680 4.8009 0.4143
1.9654 0.44 1700 5.8059 0.4143
2.17 0.44 1720 5.4482 0.4143
2.2219 0.45 1740 4.4156 0.4143
1.9873 0.45 1760 5.1548 0.4143
2.51 0.46 1780 3.1345 0.4143
2.8949 0.46 1800 5.3419 0.4143
1.2941 0.47 1820 6.8446 0.4143
2.3475 0.48 1840 5.9935 0.4143
2.7907 0.48 1860 5.8123 0.4143
2.0038 0.49 1880 6.3927 0.4143
2.0324 0.49 1900 6.4023 0.4143
2.3211 0.5 1920 5.9480 0.4143
2.3883 0.5 1940 5.5011 0.4143
2.7683 0.51 1960 3.7333 0.4143
1.6062 0.51 1980 7.2244 0.1508
2.3866 0.52 2000 4.8682 0.4143
2.3527 0.52 2020 3.9189 0.4143
3.0126 0.53 2040 4.3666 0.4143
1.9683 0.53 2060 5.1474 0.4143
2.5018 0.54 2080 4.5417 0.4143
1.555 0.54 2100 5.0804 0.4143
1.6115 0.55 2120 5.1319 0.4143
2.2321 0.55 2140 5.3196 0.4143
2.3614 0.56 2160 4.0629 0.4143
1.6915 0.56 2180 5.8209 0.4143
2.4031 0.57 2200 4.3059 0.4143
1.5659 0.57 2220 5.1369 0.4143
1.2592 0.58 2240 5.4046 0.4143
1.5577 0.58 2260 5.8448 0.4143
1.7656 0.59 2280 5.6683 0.4143
1.5057 0.59 2300 5.7769 0.4143
2.3733 0.6 2320 5.0004 0.4143
2.118 0.6 2340 5.2127 0.4143
2.2942 0.61 2360 4.8589 0.4143
2.0524 0.61 2380 3.9148 0.4143
1.8707 0.62 2400 3.2284 0.4143
1.6804 0.62 2420 4.9466 0.4143
2.5137 0.63 2440 4.5307 0.4143
1.1823 0.64 2460 4.7444 0.4143
2.9106 0.64 2480 3.7200 0.4143
1.3376 0.65 2500 4.6969 0.4143
1.8187 0.65 2520 4.2458 0.4143
1.8444 0.66 2540 4.6003 0.4143
2.1427 0.66 2560 4.7394 0.4143
2.2483 0.67 2580 4.6959 0.4143
1.5997 0.67 2600 5.5665 0.4143
2.0095 0.68 2620 4.5815 0.4143
1.4664 0.68 2640 3.4096 0.4143
1.4128 0.69 2660 4.2751 0.4143
2.4907 0.69 2680 3.0278 0.4143
1.0484 0.7 2700 3.7867 0.4143
2.7561 0.7 2720 4.0402 0.4143
1.2491 0.71 2740 3.3789 0.4143
1.1299 0.71 2760 2.4017 0.4143
1.9811 0.72 2780 3.3625 0.4143
2.1781 0.72 2800 3.2631 0.4143
1.6062 0.73 2820 2.9967 0.4143
0.928 0.73 2840 5.6052 0.4143
2.5659 0.74 2860 4.8605 0.4143
1.4248 0.74 2880 4.8685 0.4143
2.3335 0.75 2900 4.5013 0.4143
1.8546 0.75 2920 3.7017 0.4143
1.5698 0.76 2940 3.8911 0.4143
1.8653 0.76 2960 4.2637 0.4143
1.4354 0.77 2980 5.1895 0.4143
2.0558 0.77 3000 4.4362 0.4143
2.0876 0.78 3020 4.6924 0.4143
2.4282 0.78 3040 4.6526 0.4143
1.4837 0.79 3060 5.2878 0.4143
2.2982 0.8 3080 5.0637 0.4143
2.2615 0.8 3100 4.6995 0.4143
1.7026 0.81 3120 4.4688 0.4143
1.6352 0.81 3140 4.8815 0.4143
2.782 0.82 3160 3.6835 0.4143
0.3105 0.82 3180 3.8391 0.4143
2.3949 0.83 3200 4.9408 0.4143
3.0385 0.83 3220 4.3234 0.4143
2.146 0.84 3240 3.7336 0.4143
1.9198 0.84 3260 4.2217 0.4143
0.7858 0.85 3280 4.4744 0.4143
0.7785 0.85 3300 5.0257 0.4143
2.7858 0.86 3320 4.8552 0.4143
2.0922 0.86 3340 4.2950 0.4143
1.9892 0.87 3360 3.9094 0.4143
2.2241 0.87 3380 3.7403 0.4143
2.7226 0.88 3400 3.6119 0.4143
1.5888 0.88 3420 3.8878 0.4143
2.7581 0.89 3440 4.0297 0.4143
1.5373 0.89 3460 4.0980 0.4143
1.5419 0.9 3480 4.0983 0.4143
1.7618 0.9 3500 4.2322 0.4143
1.8487 0.91 3520 4.3258 0.4143
1.0667 0.91 3540 4.1975 0.4143
2.0457 0.92 3560 4.2679 0.4143
1.8133 0.92 3580 4.1908 0.4143
1.5844 0.93 3600 4.1348 0.4143
1.7202 0.93 3620 4.1382 0.4143
1.7118 0.94 3640 4.1135 0.4143
1.208 0.95 3660 4.1240 0.4143
1.6942 0.95 3680 4.1595 0.4143
0.9358 0.96 3700 4.2914 0.4143
0.9632 0.96 3720 4.3381 0.4143
1.4406 0.97 3740 4.2782 0.4143
1.5333 0.97 3760 4.1569 0.4143
2.8499 0.98 3780 3.9997 0.4143
1.3767 0.98 3800 3.9549 0.4143
1.0074 0.99 3820 3.9189 0.4143
1.7482 0.99 3840 3.8958 0.4143
1.8591 1.0 3860 3.9007 0.4143

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3