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BonySmoke
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gec_uk_seq2tag

Token Classification
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roberta
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BonySmoke commited on
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Add SPELLCHECKER

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Files changed (7) hide show
  1. README.md +16 -17
  2. config.json +0 -0
  3. model.safetensors +2 -2
  4. runs/Feb23_13-47-23_c24727d2718b/events.out.tfevents.1708696045.c24727d2718b.34.0 +3 -0
  5. runs/Feb23_13-52-15_c24727d2718b/events.out.tfevents.1708696337.c24727d2718b.34.1 +3 -0
  6. runs/Feb23_13-54-18_c24727d2718b/events.out.tfevents.1708696460.c24727d2718b.34.2 +3 -0
  7. training_args.bin +2 -2
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [youscan/ukr-roberta-base](https://huggingface.co/youscan/ukr-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2849
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- - Precision: 0.5985
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- - Recall: 0.4289
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- - F1: 0.4997
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- - Accuracy: 0.9560
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  ## Model description
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@@ -43,29 +43,28 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 10
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  - eval_batch_size: 4
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 40
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 324 | 0.3018 | 0.5718 | 0.3530 | 0.4366 | 0.9518 |
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- | 0.3361 | 2.0 | 648 | 0.2757 | 0.6054 | 0.3981 | 0.4803 | 0.9554 |
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- | 0.3361 | 3.0 | 972 | 0.2762 | 0.5915 | 0.4281 | 0.4967 | 0.9555 |
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- | 0.1932 | 4.0 | 1297 | 0.2767 | 0.6009 | 0.4298 | 0.5012 | 0.9561 |
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- | 0.161 | 5.0 | 1620 | 0.2849 | 0.5985 | 0.4289 | 0.4997 | 0.9560 |
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  ### Framework versions
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- - Transformers 4.36.2
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- - Pytorch 2.1.0+cu121
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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 [youscan/ukr-roberta-base](https://huggingface.co/youscan/ukr-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2885
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+ - Precision: 0.5978
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+ - Recall: 0.4263
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+ - F1: 0.4977
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+ - Accuracy: 0.9550
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 16
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  - eval_batch_size: 4
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  - seed: 42
49
  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 202 | 0.3200 | 0.5468 | 0.3189 | 0.4028 | 0.9493 |
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+ | No log | 2.0 | 405 | 0.2860 | 0.5904 | 0.3765 | 0.4598 | 0.9539 |
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+ | 0.3194 | 3.0 | 608 | 0.2843 | 0.5733 | 0.4437 | 0.5002 | 0.9538 |
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+ | 0.3194 | 4.0 | 811 | 0.2885 | 0.5978 | 0.4263 | 0.4977 | 0.9550 |
 
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  ### Framework versions
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+ - Transformers 4.38.1
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+ - Pytorch 2.1.2
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.1
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