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openllama-3b-finance

Text Classification
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llama
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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: 4.0296
  • 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: cosine
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy
21.9655 0.01 20 8.1663 0.0816
2.231 0.01 40 6.3007 0.4143
2.7452 0.02 60 4.0892 0.4143
2.4561 0.02 80 5.0314 0.4143
2.337 0.03 100 5.6176 0.4143
3.2226 0.03 120 4.4963 0.4143
2.5633 0.04 140 6.1800 0.4143
2.4764 0.04 160 4.7059 0.4143
2.45 0.05 180 5.0602 0.4143
1.4232 0.05 200 5.3418 0.4143
2.7684 0.06 220 5.1805 0.4143
1.7065 0.06 240 4.7568 0.4143
2.3417 0.07 260 6.1062 0.4143
1.907 0.07 280 12.0988 0.5041
14.6043 0.08 300 3.0283 0.0816
1.337 0.08 320 12.7786 0.4143
4.182 0.09 340 7.5619 0.4143
3.7365 0.09 360 7.8581 0.4143
3.209 0.1 380 3.2547 0.4143
3.4836 0.1 400 89.8525 0.0816
4.5805 0.11 420 103.0762 0.4143
4.6351 0.11 440 91.4501 0.4143
11.0873 0.12 460 88.0469 0.4143
1.1274 0.12 480 86.7130 0.4143
2.0398 0.13 500 86.4186 0.4143
18.6924 0.13 520 80.1491 0.4143
1.2216 0.14 540 76.8429 0.4143
1.1179 0.14 560 78.0159 0.4143
10.0981 0.15 580 71.1114 0.4143
9.0123 0.15 600 66.2945 0.4143
1.9539 0.16 620 65.6854 0.4143
8.4729 0.17 640 62.1595 0.4143
7.816 0.17 660 52.0763 0.4143
6.0443 0.18 680 41.1500 0.4143
3.1804 0.18 700 42.8007 0.4143
1.6122 0.19 720 44.0976 0.4143
9.8927 0.19 740 31.6381 0.4143
6.828 0.2 760 12.7483 0.4143
3.1457 0.2 780 13.2981 0.4143
1.9991 0.21 800 12.4846 0.4143
2.5539 0.21 820 13.7669 0.4143
1.3898 0.22 840 12.8919 0.0816
2.9251 0.22 860 15.9149 0.0816
4.0874 0.23 880 10.5282 0.4143
2.4763 0.23 900 3.0281 0.4143
2.2865 0.24 920 12.2460 0.4143
4.2438 0.24 940 10.1961 0.4143
2.547 0.25 960 1.4099 0.4143
0.8659 0.25 980 8.3217 0.4143
3.5331 0.26 1000 6.3990 0.4143
2.4704 0.26 1020 2.2337 0.0816
2.1381 0.27 1040 10.6263 0.4143
1.5927 0.27 1060 11.1989 0.4143
2.485 0.28 1080 8.8174 0.4143
2.8074 0.28 1100 5.5971 0.4143
0.8622 0.29 1120 5.5089 0.4143
2.8085 0.29 1140 5.4300 0.4143
1.2405 0.3 1160 7.5657 0.4143
3.9374 0.3 1180 2.7180 0.4143
1.7494 0.31 1200 4.9639 0.0816
2.6094 0.32 1220 2.1980 0.4143
2.2072 0.32 1240 7.3392 0.4143
0.9978 0.33 1260 7.9127 0.4143
2.3872 0.33 1280 7.0613 0.4143
3.3129 0.34 1300 4.4202 0.4143
1.776 0.34 1320 6.1467 0.4143
3.1179 0.35 1340 6.0607 0.4143
1.272 0.35 1360 5.0484 0.4143
3.0694 0.36 1380 3.1665 0.4143
1.9452 0.36 1400 4.8692 0.4143
2.3689 0.37 1420 4.9375 0.4143
2.7082 0.37 1440 3.2108 0.4143
0.8244 0.38 1460 7.0151 0.4143
2.6032 0.38 1480 5.5645 0.4143
2.8745 0.39 1500 4.2408 0.4143
2.625 0.39 1520 6.8800 0.4143
2.5335 0.4 1540 6.3109 0.4143
2.5495 0.4 1560 4.4017 0.4143
1.7234 0.41 1580 5.1739 0.4143
2.1066 0.41 1600 6.0769 0.4143
2.5541 0.42 1620 3.7539 0.4143
2.4598 0.42 1640 4.2075 0.4143
1.7211 0.43 1660 5.3975 0.4143
2.3993 0.43 1680 4.1427 0.4143
1.6161 0.44 1700 5.0871 0.4143
2.2361 0.44 1720 4.3375 0.4143
2.0841 0.45 1740 4.7357 0.4143
2.137 0.45 1760 5.2737 0.4143
2.3819 0.46 1780 3.1688 0.4143
2.6391 0.46 1800 5.6169 0.4143
1.276 0.47 1820 6.1945 0.4143
2.0694 0.48 1840 6.3761 0.4143
2.3715 0.48 1860 6.1666 0.4143
2.1428 0.49 1880 6.4718 0.4143
2.0409 0.49 1900 6.3259 0.4143
2.1924 0.5 1920 6.0853 0.4143
2.3511 0.5 1940 4.7199 0.4143
2.7335 0.51 1960 4.3591 0.4143
1.6784 0.51 1980 3.7488 0.1612
1.5525 0.52 2000 6.0497 0.4143
2.7457 0.52 2020 3.5952 0.4143
2.3929 0.53 2040 4.7684 0.4143
1.9522 0.53 2060 5.6394 0.4143
2.2257 0.54 2080 4.5801 0.4143
1.6753 0.54 2100 5.0521 0.4143
1.6154 0.55 2120 5.4730 0.4143
1.7723 0.55 2140 5.5251 0.4143
2.6963 0.56 2160 3.5098 0.4143
1.7274 0.56 2180 5.4262 0.4143
2.4059 0.57 2200 4.5019 0.4143
1.6505 0.57 2220 5.1107 0.4143
1.2469 0.58 2240 5.3456 0.4143
1.6702 0.58 2260 5.4103 0.4143
1.615 0.59 2280 5.8024 0.4143
1.5622 0.59 2300 5.6035 0.4143
2.3536 0.6 2320 5.3779 0.4143
2.0512 0.6 2340 5.2498 0.4143
2.1405 0.61 2360 5.2279 0.4143
2.1926 0.61 2380 4.3260 0.4143
2.3995 0.62 2400 4.4445 0.4143
1.4944 0.62 2420 4.9616 0.4143
2.6623 0.63 2440 4.9736 0.4143
1.4095 0.64 2460 4.6506 0.4143
2.4803 0.64 2480 4.0971 0.4143
1.2721 0.65 2500 4.3192 0.4143
1.8372 0.65 2520 4.4907 0.4143
1.8942 0.66 2540 4.7323 0.4143
2.1407 0.66 2560 4.9554 0.4143
2.5039 0.67 2580 5.1599 0.4143
1.7321 0.67 2600 5.6089 0.4143
2.0621 0.68 2620 4.8359 0.4143
2.1664 0.68 2640 4.5581 0.4143
1.8835 0.69 2660 5.1029 0.4143
3.0314 0.69 2680 3.9587 0.4143
1.1781 0.7 2700 4.4584 0.4143
3.3222 0.7 2720 4.7628 0.4143
2.1184 0.71 2740 4.4039 0.4143
1.9293 0.71 2760 3.8755 0.4143
2.2448 0.72 2780 4.4327 0.4143
2.4697 0.72 2800 3.3026 0.4143
1.8569 0.73 2820 3.7722 0.4143
0.8544 0.73 2840 4.9176 0.4143
2.2445 0.74 2860 4.3889 0.4143
1.3723 0.74 2880 4.3280 0.4143
2.2167 0.75 2900 4.4016 0.4143
1.98 0.75 2920 3.8661 0.4143
1.7344 0.76 2940 3.7919 0.4143
1.924 0.76 2960 4.1408 0.4143
1.3811 0.77 2980 4.3730 0.4143
1.8289 0.77 3000 4.2872 0.4143
1.9573 0.78 3020 4.6165 0.4143
2.4877 0.78 3040 4.5988 0.4143
1.1749 0.79 3060 4.7887 0.4143
2.1835 0.8 3080 4.9018 0.4143
2.3752 0.8 3100 4.6911 0.4143
1.9741 0.81 3120 4.5126 0.4143
1.7513 0.81 3140 4.6251 0.4143
3.0666 0.82 3160 4.0260 0.4143
0.5569 0.82 3180 4.0965 0.4143
2.1805 0.83 3200 4.5240 0.4143
2.4319 0.83 3220 4.3080 0.4143
2.126 0.84 3240 3.7823 0.4143
1.6993 0.84 3260 3.8093 0.4143
0.6861 0.85 3280 4.1618 0.4143
0.748 0.85 3300 4.5653 0.4143
2.5721 0.86 3320 4.6628 0.4143
2.0137 0.86 3340 4.2796 0.4143
2.1864 0.87 3360 4.1173 0.4143
2.4881 0.87 3380 3.9617 0.4143
2.6837 0.88 3400 3.7575 0.4143
1.5951 0.88 3420 3.6086 0.4143
2.504 0.89 3440 3.5919 0.4143
1.4982 0.89 3460 3.7519 0.4143
1.8994 0.9 3480 3.7120 0.4143
1.6126 0.9 3500 3.6854 0.4143
2.002 0.91 3520 3.7888 0.4143
1.0264 0.91 3540 3.7990 0.4143
1.9495 0.92 3560 3.9635 0.4143
2.0742 0.92 3580 3.9651 0.4143
1.7803 0.93 3600 3.9518 0.4143
2.0843 0.93 3620 3.9404 0.4143
1.8431 0.94 3640 3.9334 0.4143
1.4987 0.95 3660 3.9609 0.4143
1.8214 0.95 3680 4.0060 0.4143
1.0964 0.96 3700 4.0422 0.4143
0.9669 0.96 3720 4.0549 0.4143
1.6226 0.97 3740 4.0486 0.4143
1.8061 0.97 3760 4.0405 0.4143
2.8738 0.98 3780 4.0317 0.4143
1.684 0.98 3800 4.0319 0.4143
1.1158 0.99 3820 4.0303 0.4143
1.775 0.99 3840 4.0294 0.4143
2.1639 1.0 3860 4.0296 0.4143

Framework versions

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