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stablelm-3b-finance

Text Classification
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PyTorch
Safetensors
gpt_neox
generated_from_trainer
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text-generation-inference
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metadata
license: cc-by-sa-4.0
base_model: stabilityai/stablelm-base-alpha-3b
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - accuracy
model-index:
  - name: stablelm-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: 0.4080578512396694

stablelm-3b-finance

This model is a fine-tuned version of stabilityai/stablelm-base-alpha-3b on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

  • Loss: 4.2656
  • Accuracy: 0.4081

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
37.5127 0.01 20 19.6094 0.2624
19.1885 0.01 40 64.3125 0.0816
14.5964 0.02 60 43.4688 0.4143
5.8184 0.02 80 54.9688 0.4143
25.0629 0.03 100 62.25 0.0816
23.0213 0.03 120 18.4062 0.4143
6.4438 0.04 140 9.4922 0.4143
10.3302 0.04 160 25.0781 0.4143
5.5922 0.05 180 29.0469 0.4143
2.9618 0.05 200 14.5078 0.4143
4.551 0.06 220 18.5156 0.4112
4.5168 0.06 240 29.5156 0.4143
3.0656 0.07 260 27.5469 0.4143
6.9075 0.07 280 40.375 0.4143
6.09 0.08 300 28.4688 0.4143
2.254 0.08 320 35.375 0.4143
8.4998 0.09 340 31.4375 0.4143
8.3815 0.09 360 28.2188 0.4143
7.9155 0.1 380 12.0625 0.0816
2.0166 0.1 400 9.8672 0.3895
5.1889 0.11 420 11.0234 0.4132
3.844 0.11 440 10.4844 0.4132
3.7982 0.12 460 13.4688 0.3864
1.5386 0.12 480 34.3125 0.4143
4.0882 0.13 500 33.875 0.4143
6.1486 0.13 520 11.6406 0.0816
4.0523 0.14 540 9.1328 0.3988
2.5312 0.14 560 11.7969 0.4143
6.0803 0.15 580 15.6016 0.4143
5.4927 0.15 600 8.8828 0.0816
3.9641 0.16 620 9.3672 0.4132
4.2708 0.17 640 4.7852 0.4143
4.4524 0.17 660 6.5312 0.4122
6.081 0.18 680 34.3125 0.4112
7.9303 0.18 700 45.9375 0.0868
1.9309 0.19 720 44.6562 0.4143
10.341 0.19 740 20.5312 0.4143
6.2898 0.2 760 9.4219 0.4143
3.6457 0.2 780 7.6758 0.4143
4.6135 0.21 800 9.6875 0.4143
3.2253 0.21 820 9.5078 0.0888
3.7653 0.22 840 7.4453 0.1178
5.2183 0.22 860 10.2422 0.0837
7.6287 0.23 880 4.2344 0.4143
2.8624 0.23 900 4.2227 0.4143
5.1134 0.24 920 7.375 0.1674
7.5312 0.24 940 3.6641 0.0888
2.8719 0.25 960 7.7148 0.4143
1.5177 0.25 980 6.3320 0.4143
3.0631 0.26 1000 2.9297 0.4143
5.4135 0.26 1020 1.9219 0.4143
4.9254 0.27 1040 17.8281 0.4143
1.6855 0.27 1060 17.9844 0.4143
3.595 0.28 1080 13.2422 0.4143
4.9504 0.28 1100 3.6074 0.4143
1.5584 0.29 1120 13.0469 0.4143
5.144 0.29 1140 17.8594 0.0816
4.8497 0.3 1160 11.6953 0.4143
3.4161 0.3 1180 20.6562 0.0816
6.7763 0.31 1200 15.1406 0.4143
2.8709 0.32 1220 8.9922 0.4143
4.1128 0.32 1240 9.3984 0.4143
1.4908 0.33 1260 12.2891 0.4143
3.2886 0.33 1280 14.5703 0.0816
6.8233 0.34 1300 2.2734 0.4143
2.6123 0.34 1320 7.3281 0.4143
3.4882 0.35 1340 4.6992 0.0826
2.5896 0.35 1360 11.9688 0.4143
6.352 0.36 1380 4.4727 0.4101
2.2885 0.36 1400 5.7578 0.4050
4.2021 0.37 1420 7.1172 0.4143
3.032 0.37 1440 2.7988 0.4132
1.428 0.38 1460 17.5938 0.4143
2.8068 0.38 1480 15.9141 0.4143
7.0014 0.39 1500 4.9883 0.1033
3.2138 0.39 1520 9.625 0.4112
3.0001 0.4 1540 6.2305 0.3833
2.3248 0.4 1560 5.5547 0.3760
2.7573 0.41 1580 9.0859 0.4143
4.8701 0.41 1600 9.1641 0.25
6.5986 0.42 1620 6.5977 0.4143
5.4379 0.42 1640 8.8203 0.0826
4.7142 0.43 1660 6.9219 0.4143
3.1696 0.43 1680 3.9941 0.4163
2.5254 0.44 1700 5.7461 0.4143
1.9537 0.44 1720 3.7441 0.4143
2.4895 0.45 1740 6.8789 0.4143
2.9386 0.45 1760 6.5625 0.4205
4.1816 0.46 1780 2.7070 0.4163
5.2298 0.46 1800 6.3828 0.3967
1.3144 0.47 1820 8.3984 0.4143
2.62 0.48 1840 7.8359 0.4153
2.3815 0.48 1860 6.9297 0.3595
1.2381 0.49 1880 7.125 0.3595
1.6726 0.49 1900 8.7109 0.4132
2.0483 0.5 1920 8.3047 0.4101
3.6178 0.5 1940 8.1719 0.3574
3.0994 0.51 1960 11.5625 0.2572
2.0991 0.51 1980 15.375 0.0816
4.4138 0.52 2000 16.6406 0.4143
4.6666 0.52 2020 3.7168 0.4050
1.8319 0.53 2040 5.375 0.4112
2.2184 0.53 2060 5.3398 0.3337
2.5527 0.54 2080 4.2188 0.4112
2.2984 0.54 2100 6.875 0.4122
2.2836 0.55 2120 6.1836 0.4101
3.0373 0.55 2140 5.7930 0.4019
2.2946 0.56 2160 4.7891 0.4091
1.4506 0.56 2180 13.2891 0.3936
1.7211 0.57 2200 11.1172 0.4112
2.4151 0.57 2220 8.2969 0.3905
1.989 0.58 2240 8.2812 0.4143
0.6184 0.58 2260 8.1562 0.3936
2.8995 0.59 2280 9.8203 0.4174
1.8413 0.59 2300 8.0781 0.3626
1.6959 0.6 2320 8.5703 0.3233
2.7333 0.6 2340 4.8984 0.4163
1.7187 0.61 2360 6.5859 0.3957
2.8341 0.61 2380 5.6133 0.3926
1.8751 0.62 2400 5.1445 0.3399
2.0391 0.62 2420 5.3633 0.4163
1.1569 0.63 2440 6.4570 0.2975
1.8955 0.64 2460 4.6367 0.4153
3.3986 0.64 2480 4.875 0.4153
0.9939 0.65 2500 4.4492 0.4184
3.1304 0.65 2520 3.9414 0.4081
1.7888 0.66 2540 5.5898 0.4081
2.1101 0.66 2560 6.3242 0.3017
1.7795 0.67 2580 6.1992 0.3957
0.565 0.67 2600 7.1016 0.4205
2.1791 0.68 2620 4.4805 0.4215
1.8351 0.68 2640 6.9727 0.3667
2.0573 0.69 2660 7.6797 0.4174
2.077 0.69 2680 4.3242 0.4029
1.3436 0.7 2700 5.7227 0.4153
2.5434 0.7 2720 5.6836 0.3492
1.4306 0.71 2740 4.4844 0.4122
1.8493 0.71 2760 4.3984 0.4070
1.2447 0.72 2780 4.3242 0.3884
3.9 0.72 2800 2.9824 0.4091
2.8007 0.73 2820 3.8008 0.4174
0.7397 0.73 2840 6.1953 0.4153
1.6954 0.74 2860 3.8711 0.4122
1.0559 0.74 2880 6.7578 0.4184
2.354 0.75 2900 4.0195 0.3853
2.2396 0.75 2920 4.1836 0.4143
1.736 0.76 2940 6.1523 0.3657
1.7746 0.76 2960 7.6797 0.4112
2.8225 0.77 2980 7.1172 0.4153
1.723 0.77 3000 4.4570 0.4143
1.591 0.78 3020 4.7188 0.4060
2.2972 0.78 3040 5.4219 0.3461
0.7102 0.79 3060 6.3477 0.4205
2.6331 0.8 3080 5.7031 0.4039
2.2154 0.8 3100 5.9023 0.3223
1.3288 0.81 3120 5.6289 0.4101
2.8541 0.81 3140 4.5391 0.4081
1.1956 0.82 3160 4.2188 0.3326
0.6724 0.82 3180 6.4180 0.4194
3.3572 0.83 3200 5.7930 0.4070
2.1121 0.83 3220 4.6758 0.3771
2.0453 0.84 3240 5.3281 0.4112
1.1715 0.84 3260 6.9141 0.3812
1.0438 0.85 3280 7.3242 0.4143
0.7894 0.85 3300 7.7422 0.4184
3.3288 0.86 3320 6.8945 0.4039
1.9406 0.86 3340 5.5547 0.4143
2.5013 0.87 3360 4.0078 0.4132
1.3637 0.87 3380 4.2031 0.4070
2.9497 0.88 3400 3.9727 0.4091
1.6295 0.88 3420 4.9805 0.4132
1.6557 0.89 3440 5.0586 0.3864
1.0884 0.89 3460 5.2070 0.3977
0.3464 0.9 3480 5.4609 0.4101
1.7069 0.9 3500 5.7188 0.4081
1.9864 0.91 3520 5.8164 0.4112
1.7181 0.91 3540 5.3047 0.4091
3.4296 0.92 3560 4.9375 0.4081
1.1618 0.92 3580 4.8320 0.3905
1.5314 0.93 3600 4.9531 0.3874
2.2965 0.93 3620 4.5625 0.3988
1.7454 0.94 3640 4.4102 0.4081
0.938 0.95 3660 4.3945 0.4060
1.9902 0.95 3680 4.4844 0.4070
0.7222 0.96 3700 4.6758 0.4091
1.4837 0.96 3720 4.6914 0.4112
1.4711 0.97 3740 4.6875 0.4122
1.5978 0.97 3760 4.5859 0.4091
2.7881 0.98 3780 4.5273 0.4101
2.2261 0.98 3800 4.4805 0.4060
1.6863 0.99 3820 4.3203 0.4091
2.0884 0.99 3840 4.2656 0.4081
2.1517 1.0 3860 4.2656 0.4081

Framework versions

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