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roberta-petco-fullemailbody-ctr

Text Classification
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roberta
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: FacebookAI/roberta-base
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: roberta-petco-fullemailbody-ctr
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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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+ # roberta-petco-fullemailbody-ctr
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+
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+ This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4048
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+ - Mse: 0.4048
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+ - Rmse: 0.6363
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+ - Mae: 0.4301
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+ - R2: 0.2106
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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: 16
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+ - eval_batch_size: 16
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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: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mse | Rmse | Mae | R2 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
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+ | 1.3784 | 1.0 | 15 | 0.5080 | 0.5080 | 0.7127 | 0.5575 | 0.0095 |
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+ | 0.5017 | 2.0 | 30 | 0.5043 | 0.5043 | 0.7101 | 0.5241 | 0.0166 |
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+ | 0.4986 | 3.0 | 45 | 0.4864 | 0.4864 | 0.6974 | 0.5219 | 0.0515 |
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+ | 0.4621 | 4.0 | 60 | 0.5096 | 0.5096 | 0.7139 | 0.5096 | 0.0063 |
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+ | 0.4483 | 5.0 | 75 | 0.5069 | 0.5069 | 0.7120 | 0.5031 | 0.0116 |
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+ | 0.4396 | 6.0 | 90 | 0.4707 | 0.4707 | 0.6861 | 0.5275 | 0.0822 |
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+ | 0.4145 | 7.0 | 105 | 0.4661 | 0.4661 | 0.6828 | 0.4802 | 0.0910 |
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+ | 0.4293 | 8.0 | 120 | 0.5122 | 0.5122 | 0.7157 | 0.4884 | 0.0012 |
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+ | 0.3681 | 9.0 | 135 | 0.4358 | 0.4358 | 0.6601 | 0.4947 | 0.1502 |
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+ | 0.3349 | 10.0 | 150 | 0.4676 | 0.4676 | 0.6838 | 0.4434 | 0.0882 |
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+ | 0.3003 | 11.0 | 165 | 0.4601 | 0.4601 | 0.6783 | 0.4904 | 0.1028 |
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+ | 0.3132 | 12.0 | 180 | 0.5026 | 0.5026 | 0.7089 | 0.4734 | 0.0200 |
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+ | 0.3446 | 13.0 | 195 | 0.4411 | 0.4411 | 0.6641 | 0.4655 | 0.1399 |
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+ | 0.2935 | 14.0 | 210 | 0.5986 | 0.5986 | 0.7737 | 0.6535 | -0.1672 |
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+ | 0.2301 | 15.0 | 225 | 0.4409 | 0.4409 | 0.6640 | 0.4506 | 0.1403 |
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+ | 0.2152 | 16.0 | 240 | 0.4048 | 0.4048 | 0.6363 | 0.4301 | 0.2106 |
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+ | 0.2056 | 17.0 | 255 | 0.4115 | 0.4115 | 0.6415 | 0.4429 | 0.1976 |
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+ | 0.1885 | 18.0 | 270 | 0.4058 | 0.4058 | 0.6370 | 0.4467 | 0.2087 |
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+ | 0.1754 | 19.0 | 285 | 0.4282 | 0.4282 | 0.6543 | 0.4765 | 0.1651 |
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+ | 0.1758 | 20.0 | 300 | 0.4076 | 0.4076 | 0.6384 | 0.4487 | 0.2052 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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