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Fine-tuning Complete
Browse files- README.md +36 -2
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
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- generated_from_trainer
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datasets:
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- emotion
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model-index:
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- name: distilbert-base-uncased-finetuned-emotion
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results:
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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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# distilbert-base-uncased-finetuned-emotion
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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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## Model description
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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:
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### Framework versions
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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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- f1
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model-index:
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- name: distilbert-base-uncased-finetuned-emotion
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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: 0.9295
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- name: F1
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type: f1
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value: 0.9294149161247448
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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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# distilbert-base-uncased-finetuned-emotion
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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: 0.2037
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- Accuracy: 0.9295
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- F1: 0.9294
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## Model description
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| No log | 1.0 | 63 | 0.9206 | 0.7015 | 0.6313 |
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| No log | 2.0 | 126 | 0.4065 | 0.8855 | 0.8799 |
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| 0.978 | 3.0 | 189 | 0.2554 | 0.9205 | 0.9202 |
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| 0.978 | 4.0 | 252 | 0.2115 | 0.9285 | 0.9286 |
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| 0.268 | 5.0 | 315 | 0.2037 | 0.9295 | 0.9294 |
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### Framework versions
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pytorch_model.bin
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training_args.bin
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