Training complete
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README.md
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---
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license: apache-2.0
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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: distilbert-finetuned-gesture-prediction-21-classes
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results: []
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# distilbert-finetuned-gesture-prediction-21-classes
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.
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It achieves the following results on the test set:
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- Loss: 0.8332
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- Accuracy: 0.7934
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- Precision: 0.7925
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- Recall: 0.7934
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- F1: 0.7875
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## Model description
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## Training and evaluation data
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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:
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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- Transformers 4.
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- Pytorch
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- Datasets 2.
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- Tokenizers 0.
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---
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license: apache-2.0
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base_model: distilbert-base-cased
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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: distilbert-finetuned-gesture-prediction-21-classes
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results: []
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# distilbert-finetuned-gesture-prediction-21-classes
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9438
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- Precision: 0.7910
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- Recall: 0.7910
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- F1: 0.7910
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- Accuracy: 0.7817
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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: 6.042200829392303e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 2.4003 | 1.0 | 26 | 1.4948 | 0.6512 | 0.6512 | 0.6512 | 0.6206 |
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| 1.2197 | 2.0 | 52 | 1.0008 | 0.7501 | 0.7501 | 0.7501 | 0.7356 |
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| 0.81 | 3.0 | 78 | 0.8907 | 0.7696 | 0.7696 | 0.7696 | 0.7555 |
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| 0.5619 | 4.0 | 104 | 0.9091 | 0.7628 | 0.7628 | 0.7628 | 0.7495 |
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| 0.3944 | 5.0 | 130 | 0.8791 | 0.7853 | 0.7853 | 0.7853 | 0.7749 |
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| 0.2907 | 6.0 | 156 | 0.8973 | 0.7845 | 0.7845 | 0.7845 | 0.7733 |
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| 0.2214 | 7.0 | 182 | 0.9209 | 0.7874 | 0.7874 | 0.7874 | 0.7779 |
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| 0.1722 | 8.0 | 208 | 0.9446 | 0.7878 | 0.7878 | 0.7878 | 0.7787 |
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| 0.1454 | 9.0 | 234 | 0.9406 | 0.7882 | 0.7882 | 0.7882 | 0.7789 |
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| 0.128 | 10.0 | 260 | 0.9438 | 0.7910 | 0.7910 | 0.7910 | 0.7817 |
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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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model.safetensors
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runs/May04_10-50-36_19b9be41010d/events.out.tfevents.1714819841.19b9be41010d.571.1
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