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Enyonam
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roberta-base-Roberta-Model

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +21 -21
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7606
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- - F1: 0.6658
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  ## Model description
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@@ -49,25 +49,25 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 0.8507 | 0.5 | 500 | 0.8189 | 0.6246 |
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- | 0.7922 | 1.0 | 1000 | 0.7885 | 0.6576 |
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- | 0.7971 | 1.5 | 1500 | 0.7916 | 0.6241 |
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- | 0.8195 | 2.01 | 2000 | 0.7655 | 0.6440 |
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- | 0.8382 | 2.51 | 2500 | 0.7892 | 0.6450 |
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- | 0.7871 | 3.01 | 3000 | 0.7803 | 0.6552 |
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- | 0.7921 | 3.51 | 3500 | 0.8156 | 0.6600 |
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- | 0.79 | 4.01 | 4000 | 0.7654 | 0.6679 |
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- | 0.774 | 4.51 | 4500 | 0.7743 | 0.6473 |
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- | 0.7645 | 5.02 | 5000 | 0.7897 | 0.6522 |
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- | 0.7752 | 5.52 | 5500 | 0.7943 | 0.6232 |
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- | 0.7645 | 6.02 | 6000 | 0.7606 | 0.6658 |
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- | 0.8006 | 6.52 | 6500 | 0.7832 | 0.6643 |
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- | 0.771 | 7.02 | 7000 | 0.7818 | 0.6588 |
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- | 0.7674 | 7.52 | 7500 | 0.7700 | 0.6549 |
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- | 0.7554 | 8.02 | 8000 | 0.7819 | 0.6583 |
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- | 0.787 | 8.53 | 8500 | 0.7782 | 0.6493 |
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- | 0.7577 | 9.03 | 9000 | 0.7777 | 0.6579 |
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- | 0.7646 | 9.53 | 9500 | 0.7812 | 0.6555 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7452
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+ - F1: 0.6666
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.8612 | 0.5 | 500 | 0.7837 | 0.6394 |
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+ | 0.7964 | 1.0 | 1000 | 0.7592 | 0.6672 |
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+ | 0.887 | 1.5 | 1500 | 0.7818 | 0.6677 |
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+ | 0.8249 | 2.01 | 2000 | 0.8492 | 0.6540 |
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+ | 0.7916 | 2.51 | 2500 | 0.7721 | 0.6736 |
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+ | 0.7988 | 3.01 | 3000 | 0.8391 | 0.6230 |
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+ | 0.8691 | 3.51 | 3500 | 0.8104 | 0.6665 |
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+ | 0.8473 | 4.01 | 4000 | 0.7834 | 0.6687 |
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+ | 0.7798 | 4.51 | 4500 | 0.7582 | 0.6697 |
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+ | 0.7886 | 5.02 | 5000 | 0.8004 | 0.6499 |
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+ | 0.9395 | 5.52 | 5500 | 0.9524 | 0.3221 |
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+ | 0.9588 | 6.02 | 6000 | 0.9577 | 0.3224 |
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+ | 0.9539 | 6.52 | 6500 | 0.9560 | 0.2347 |
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+ | 0.9599 | 7.02 | 7000 | 0.9553 | 0.3222 |
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+ | 0.9263 | 7.52 | 7500 | 0.7582 | 0.6653 |
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+ | 0.7897 | 8.02 | 8000 | 0.7783 | 0.6682 |
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+ | 0.7999 | 8.53 | 8500 | 0.8641 | 0.6373 |
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+ | 0.7779 | 9.03 | 9000 | 0.7452 | 0.6666 |
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+ | 0.761 | 9.53 | 9500 | 0.7469 | 0.6684 |
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  ### Framework versions
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