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Cielciel/aift-model-review-multiple-label-classification_new

Browse files
README.md CHANGED
@@ -15,8 +15,8 @@ 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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4183
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- - Accuracy Thresh: 0.9179
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  ## Model description
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@@ -46,23 +46,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
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- |:-------------:|:-----:|:----:|:---------------:|:---------------:|
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- | No log | 1.0 | 70 | 1.1564 | 0.8786 |
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- | No log | 2.0 | 140 | 0.9926 | 0.8879 |
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- | No log | 3.0 | 210 | 1.0794 | 0.9064 |
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- | No log | 4.0 | 280 | 1.2496 | 0.9221 |
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- | No log | 5.0 | 350 | 1.1574 | 0.9107 |
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- | No log | 6.0 | 420 | 1.3792 | 0.9171 |
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- | No log | 7.0 | 490 | 1.3086 | 0.9179 |
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- | 0.6024 | 8.0 | 560 | 1.4221 | 0.9221 |
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- | 0.6024 | 9.0 | 630 | 1.4423 | 0.9164 |
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- | 0.6024 | 10.0 | 700 | 1.4183 | 0.9179 |
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  ### Framework versions
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- - Transformers 4.37.0.dev0
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  - Pytorch 2.0.0+cu118
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6219
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+ - Accuracy Thresh: 0.9394
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------------:|
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+ | 1.0382 | 1.0 | 1117 | 0.8749 | 0.8385 |
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+ | 0.7924 | 2.0 | 2234 | 0.9227 | 0.8872 |
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+ | 0.577 | 3.0 | 3351 | 1.0330 | 0.9018 |
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+ | 0.4808 | 4.0 | 4468 | 1.5019 | 0.9216 |
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+ | 0.3928 | 5.0 | 5585 | 1.5877 | 0.9250 |
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+ | 0.3569 | 6.0 | 6702 | 1.9525 | 0.9316 |
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+ | 0.3086 | 7.0 | 7819 | 2.0074 | 0.9308 |
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+ | 0.2512 | 8.0 | 8936 | 2.3945 | 0.9361 |
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+ | 0.2249 | 9.0 | 10053 | 2.6052 | 0.9385 |
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+ | 0.242 | 10.0 | 11170 | 2.6219 | 0.9394 |
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  ### Framework versions
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+ - Transformers 4.37.1
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  - Pytorch 2.0.0+cu118
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
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