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distilbert-finance

Text Classification
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distilbert
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update model card README.md

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  1. README.md +34 -34
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@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6993801652892562
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5012
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- - Accuracy: 0.6994
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  ## Model description
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@@ -57,43 +57,43 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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  - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6623 | 0.33 | 20 | 1.4571 | 0.3709 |
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- | 0.424 | 0.66 | 40 | 1.0223 | 0.6126 |
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- | 0.4282 | 0.98 | 60 | 1.0824 | 0.6343 |
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- | 0.3126 | 1.31 | 80 | 0.9320 | 0.6612 |
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- | 0.2464 | 1.64 | 100 | 0.8817 | 0.6963 |
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- | 0.2677 | 1.97 | 120 | 0.9278 | 0.6994 |
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- | 0.1221 | 2.3 | 140 | 1.7929 | 0.6322 |
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- | 0.1392 | 2.62 | 160 | 1.0517 | 0.7004 |
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- | 0.1982 | 2.95 | 180 | 1.0295 | 0.6684 |
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- | 0.1055 | 3.28 | 200 | 0.9028 | 0.7252 |
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- | 0.0704 | 3.61 | 220 | 1.6708 | 0.6188 |
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- | 0.0962 | 3.93 | 240 | 1.1233 | 0.7200 |
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- | 0.0356 | 4.26 | 260 | 1.1614 | 0.7603 |
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- | 0.0553 | 4.59 | 280 | 1.1362 | 0.7149 |
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- | 0.0665 | 4.92 | 300 | 1.2905 | 0.6529 |
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- | 0.0441 | 5.25 | 320 | 1.5180 | 0.6932 |
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- | 0.0425 | 5.57 | 340 | 1.3501 | 0.6808 |
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- | 0.0267 | 5.9 | 360 | 1.2062 | 0.7159 |
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- | 0.0198 | 6.23 | 380 | 1.3289 | 0.7262 |
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- | 0.0228 | 6.56 | 400 | 1.6142 | 0.6694 |
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- | 0.0209 | 6.89 | 420 | 1.8779 | 0.6147 |
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- | 0.0295 | 7.21 | 440 | 1.1260 | 0.6994 |
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- | 0.0138 | 7.54 | 460 | 1.4690 | 0.6756 |
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- | 0.0091 | 7.87 | 480 | 1.2774 | 0.7035 |
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- | 0.0094 | 8.2 | 500 | 1.8177 | 0.6384 |
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- | 0.0075 | 8.52 | 520 | 1.3794 | 0.7004 |
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- | 0.0079 | 8.85 | 540 | 1.4167 | 0.6994 |
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- | 0.0039 | 9.18 | 560 | 1.4824 | 0.6921 |
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- | 0.0023 | 9.51 | 580 | 1.5161 | 0.6932 |
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- | 0.005 | 9.84 | 600 | 1.5012 | 0.6994 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7045454545454546
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7474
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+ - Accuracy: 0.7045
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  ## Model description
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  - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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  - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6755 | 0.33 | 20 | 1.4948 | 0.3709 |
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+ | 0.4585 | 0.66 | 40 | 0.9705 | 0.6147 |
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+ | 0.4267 | 0.98 | 60 | 1.2383 | 0.6012 |
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+ | 0.3298 | 1.31 | 80 | 1.0040 | 0.5764 |
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+ | 0.2955 | 1.64 | 100 | 1.4078 | 0.4845 |
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+ | 0.2521 | 1.97 | 120 | 1.2183 | 0.5702 |
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+ | 0.1614 | 2.3 | 140 | 1.4761 | 0.6570 |
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+ | 0.1842 | 2.62 | 160 | 1.8172 | 0.6002 |
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+ | 0.2124 | 2.95 | 180 | 0.9596 | 0.7211 |
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+ | 0.1016 | 3.28 | 200 | 1.3150 | 0.6952 |
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+ | 0.0949 | 3.61 | 220 | 1.5779 | 0.6498 |
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+ | 0.102 | 3.93 | 240 | 1.9178 | 0.5775 |
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+ | 0.0542 | 4.26 | 260 | 2.0914 | 0.6074 |
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+ | 0.059 | 4.59 | 280 | 1.7965 | 0.6560 |
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+ | 0.0578 | 4.92 | 300 | 2.0358 | 0.5279 |
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+ | 0.0335 | 5.25 | 320 | 1.5614 | 0.6829 |
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+ | 0.0414 | 5.57 | 340 | 1.8126 | 0.6405 |
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+ | 0.0263 | 5.9 | 360 | 1.4405 | 0.6798 |
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+ | 0.0257 | 6.23 | 380 | 1.0230 | 0.7417 |
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+ | 0.0123 | 6.56 | 400 | 1.9126 | 0.6818 |
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+ | 0.0218 | 6.89 | 420 | 1.8622 | 0.6860 |
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+ | 0.0063 | 7.21 | 440 | 2.0173 | 0.6705 |
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+ | 0.014 | 7.54 | 460 | 1.9129 | 0.6870 |
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+ | 0.0037 | 7.87 | 480 | 1.7622 | 0.7035 |
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+ | 0.0155 | 8.2 | 500 | 1.7379 | 0.7004 |
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+ | 0.0087 | 8.52 | 520 | 1.7150 | 0.6994 |
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+ | 0.0055 | 8.85 | 540 | 1.7286 | 0.7025 |
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+ | 0.0051 | 9.18 | 560 | 1.7418 | 0.7014 |
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+ | 0.0049 | 9.51 | 580 | 1.7468 | 0.7035 |
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+ | 0.0056 | 9.84 | 600 | 1.7474 | 0.7045 |
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  ### Framework versions