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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: HBERTv1_emb_compress_48_L10_H64_A2 |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# HBERTv1_emb_compress_48_L10_H64_A2 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.4103 |
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- Accuracy: 0.1281 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 96 |
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- eval_batch_size: 96 |
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- seed: 10 |
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- distributed_type: multi-GPU |
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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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- lr_scheduler_warmup_steps: 10000 |
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- num_epochs: 5 |
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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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| 8.6666 | 0.16 | 10000 | 8.5962 | 0.0465 | |
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| 7.2486 | 0.33 | 20000 | 7.2447 | 0.0467 | |
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| 7.0176 | 0.49 | 30000 | 7.0089 | 0.0670 | |
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| 6.8859 | 0.66 | 40000 | 6.8795 | 0.0840 | |
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| 6.7911 | 0.82 | 50000 | 6.7857 | 0.0918 | |
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| 6.7203 | 0.98 | 60000 | 6.7210 | 0.0952 | |
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| 6.6722 | 1.15 | 70000 | 6.6715 | 0.1004 | |
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| 6.6362 | 1.31 | 80000 | 6.6338 | 0.1033 | |
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| 6.5995 | 1.47 | 90000 | 6.6017 | 0.1065 | |
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| 6.5756 | 1.64 | 100000 | 6.5755 | 0.1092 | |
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| 6.5546 | 1.8 | 110000 | 6.5521 | 0.1118 | |
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| 6.5302 | 1.97 | 120000 | 6.5330 | 0.1138 | |
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| 6.5121 | 2.13 | 130000 | 6.5166 | 0.1156 | |
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| 6.5049 | 2.29 | 140000 | 6.5005 | 0.1174 | |
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| 6.483 | 2.46 | 150000 | 6.4869 | 0.1190 | |
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| 6.4757 | 2.62 | 160000 | 6.4755 | 0.1205 | |
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| 6.4659 | 2.79 | 170000 | 6.4655 | 0.1219 | |
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| 6.4527 | 2.95 | 180000 | 6.4569 | 0.1227 | |
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| 6.4517 | 3.11 | 190000 | 6.4477 | 0.1237 | |
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| 6.441 | 3.28 | 200000 | 6.4422 | 0.1245 | |
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| 6.4385 | 3.44 | 210000 | 6.4353 | 0.1253 | |
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| 6.4308 | 3.6 | 220000 | 6.4295 | 0.1256 | |
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| 6.4188 | 3.77 | 230000 | 6.4250 | 0.1264 | |
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| 6.422 | 3.93 | 240000 | 6.4213 | 0.1269 | |
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| 6.416 | 4.1 | 250000 | 6.4180 | 0.1273 | |
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| 6.4215 | 4.26 | 260000 | 6.4151 | 0.1276 | |
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| 6.4135 | 4.42 | 270000 | 6.4142 | 0.1277 | |
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| 6.4138 | 4.59 | 280000 | 6.4118 | 0.1281 | |
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| 6.41 | 4.75 | 290000 | 6.4097 | 0.1283 | |
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| 6.4114 | 4.92 | 300000 | 6.4103 | 0.1281 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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