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HBERTv1_emb_compress_48_L10_H64_A2

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hybridbert
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README.md ADDED
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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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+
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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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+
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+ # HBERTv1_emb_compress_48_L10_H64_A2
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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