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roberta-finetuned-gesture-prediction-21-classes

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  1. README.md +26 -34
  2. model.safetensors +1 -1
  3. runs/May04_11-01-55_19b9be41010d/events.out.tfevents.1714820519.19b9be41010d.571.3 +2 -2
README.md CHANGED
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  ---
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  license: mit
 
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  tags:
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  - generated_from_trainer
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- datasets: qfrodicio/gesture-prediction-21-classes
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  metrics:
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- - accuracy
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  - precision
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  - recall
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  - f1
 
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  model-index:
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  - name: roberta-finetuned-gesture-prediction-21-classes
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  results: []
@@ -18,20 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # roberta-finetuned-gesture-prediction-21-classes
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7782
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- - Accuracy: 0.8185
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- - Precision: 0.8116
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- - Recall: 0.8185
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- - F1: 0.8072
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-
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- It achieves the following results on the evaluation set:
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- - Loss: 0.8142
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- - Accuracy: 0.8154
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- - Precision: 0.8189
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- - Recall: 0.8154
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- - F1: 0.8125
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  ## Model description
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@@ -43,15 +36,14 @@ More information needed
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  ## Training and evaluation data
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- The model has been trained with the qfrodicio/gesture-prediction-21-classes dataset
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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: 2e-05
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- - weight_decay: 0.01
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -61,23 +53,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 2.2147 | 1.0 | 104 | 1.3857 | 0.7406 | 0.6887 | 0.7406 | 0.7013 |
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- | 1.2291 | 2.0 | 208 | 0.9750 | 0.7907 | 0.7597 | 0.7907 | 0.7618 |
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- | 0.836 | 3.0 | 312 | 0.8609 | 0.8028 | 0.7813 | 0.8028 | 0.7829 |
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- | 0.6129 | 4.0 | 416 | 0.8059 | 0.8078 | 0.8030 | 0.8078 | 0.7973 |
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- | 0.4747 | 5.0 | 520 | 0.7782 | 0.8185 | 0.8116 | 0.8185 | 0.8072 |
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- | 0.3639 | 6.0 | 624 | 0.7825 | 0.8175 | 0.8170 | 0.8175 | 0.8108 |
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- | 0.295 | 7.0 | 728 | 0.7913 | 0.8365 | 0.8283 | 0.8365 | 0.8280 |
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- | 0.236 | 8.0 | 832 | 0.7619 | 0.8273 | 0.8230 | 0.8273 | 0.8229 |
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- | 0.1989 | 9.0 | 936 | 0.7880 | 0.8309 | 0.8258 | 0.8309 | 0.8261 |
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- | 0.1879 | 10.0 | 1040 | 0.7915 | 0.8314 | 0.8247 | 0.8314 | 0.8264 |
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  ### Framework versions
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- - Transformers 4.26.1
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- - Pytorch 1.13.1+cu116
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- - Datasets 2.10.1
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- - Tokenizers 0.13.2
 
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  ---
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  license: mit
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+ base_model: roberta-base
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  tags:
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  - generated_from_trainer
 
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  metrics:
 
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  - precision
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  - recall
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  - f1
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+ - accuracy
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  model-index:
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  - name: roberta-finetuned-gesture-prediction-21-classes
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  results: []
 
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  # roberta-finetuned-gesture-prediction-21-classes
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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: 1.0301
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+ - Precision: 0.8177
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+ - Recall: 0.8177
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+ - F1: 0.8177
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+ - Accuracy: 0.8089
 
 
 
 
 
 
 
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  ## Model description
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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: 4.9033776462709114e-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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.8808 | 1.0 | 104 | 1.0755 | 0.7554 | 0.7554 | 0.7554 | 0.7327 |
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+ | 0.8843 | 2.0 | 208 | 0.8376 | 0.7637 | 0.7637 | 0.7637 | 0.7465 |
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+ | 0.5881 | 3.0 | 312 | 0.7300 | 0.8152 | 0.8152 | 0.8152 | 0.8014 |
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+ | 0.4017 | 4.0 | 416 | 0.8043 | 0.7985 | 0.7985 | 0.7985 | 0.7881 |
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+ | 0.2652 | 5.0 | 520 | 0.8595 | 0.8118 | 0.8118 | 0.8118 | 0.8018 |
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+ | 0.1776 | 6.0 | 624 | 0.9623 | 0.8133 | 0.8133 | 0.8133 | 0.8041 |
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+ | 0.1234 | 7.0 | 728 | 0.9631 | 0.8068 | 0.8068 | 0.8068 | 0.7989 |
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+ | 0.0829 | 8.0 | 832 | 1.0190 | 0.8256 | 0.8256 | 0.8256 | 0.8168 |
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+ | 0.0552 | 9.0 | 936 | 1.0117 | 0.8222 | 0.8222 | 0.8222 | 0.8139 |
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+ | 0.0409 | 10.0 | 1040 | 1.0301 | 0.8177 | 0.8177 | 0.8177 | 0.8089 |
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  ### Framework versions
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+ - Transformers 4.40.1
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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