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DBERT_Emotions_tuned

Text Classification
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distilbert
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  1. README.md +104 -0
  2. model.safetensors +1 -1
  3. runs/Mar13_06-05-04_4c6caea91783/events.out.tfevents.1710309989.4c6caea91783.1898.0 +2 -2
  4. runs/Mar13_06-05-04_4c6caea91783/events.out.tfevents.1710311273.4c6caea91783.1898.1 +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - emotion
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: DBERT_Emotions_tuned
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: emotion
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+ type: emotion
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+ config: split
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+ split: validation
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+ args: split
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.925
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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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+ # DBERT_Emotions_tuned
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1828
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+ - Accuracy: 0.925
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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: 5e-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: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.1 | 100 | 0.7513 | 0.7365 |
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+ | No log | 0.2 | 200 | 0.3693 | 0.8895 |
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+ | No log | 0.3 | 300 | 0.3118 | 0.906 |
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+ | No log | 0.4 | 400 | 0.3048 | 0.9055 |
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+ | 0.5368 | 0.5 | 500 | 0.2649 | 0.9225 |
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+ | 0.5368 | 0.6 | 600 | 0.2192 | 0.9235 |
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+ | 0.5368 | 0.7 | 700 | 0.2254 | 0.9245 |
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+ | 0.5368 | 0.8 | 800 | 0.2016 | 0.931 |
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+ | 0.5368 | 0.9 | 900 | 0.1685 | 0.935 |
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+ | 0.2254 | 1.0 | 1000 | 0.1926 | 0.9295 |
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+ | 0.2254 | 1.1 | 1100 | 0.2128 | 0.928 |
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+ | 0.2254 | 1.2 | 1200 | 0.2008 | 0.9325 |
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+ | 0.2254 | 1.3 | 1300 | 0.1662 | 0.9385 |
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+ | 0.2254 | 1.4 | 1400 | 0.1945 | 0.939 |
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+ | 0.1315 | 1.5 | 1500 | 0.1652 | 0.939 |
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+ | 0.1315 | 1.6 | 1600 | 0.1820 | 0.938 |
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+ | 0.1315 | 1.7 | 1700 | 0.1660 | 0.938 |
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+ | 0.1315 | 1.8 | 1800 | 0.1590 | 0.93 |
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+ | 0.1315 | 1.9 | 1900 | 0.1601 | 0.935 |
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+ | 0.1295 | 2.0 | 2000 | 0.1645 | 0.9345 |
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+ | 0.1295 | 2.1 | 2100 | 0.1845 | 0.9305 |
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+ | 0.1295 | 2.2 | 2200 | 0.1784 | 0.9355 |
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+ | 0.1295 | 2.3 | 2300 | 0.2042 | 0.9365 |
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+ | 0.1295 | 2.4 | 2400 | 0.1852 | 0.9365 |
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+ | 0.0891 | 2.5 | 2500 | 0.1797 | 0.94 |
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+ | 0.0891 | 2.6 | 2600 | 0.1741 | 0.9365 |
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+ | 0.0891 | 2.7 | 2700 | 0.1758 | 0.9385 |
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+ | 0.0891 | 2.8 | 2800 | 0.1771 | 0.944 |
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+ | 0.0891 | 2.9 | 2900 | 0.1688 | 0.9385 |
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+ | 0.0848 | 3.0 | 3000 | 0.1671 | 0.94 |
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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 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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