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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: |
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- sentiment140 |
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metrics: |
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- accuracy |
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model-index: |
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- name: Sentiment140_XLNET_5E |
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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: sentiment140 |
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type: sentiment140 |
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config: sentiment140 |
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split: train |
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args: sentiment140 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.84 |
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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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# Sentiment140_XLNET_5E |
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This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the sentiment140 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3797 |
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- Accuracy: 0.84 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6687 | 0.08 | 50 | 0.5194 | 0.76 | |
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| 0.5754 | 0.16 | 100 | 0.4500 | 0.7867 | |
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| 0.5338 | 0.24 | 150 | 0.3725 | 0.8333 | |
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| 0.5065 | 0.32 | 200 | 0.4093 | 0.8133 | |
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| 0.4552 | 0.4 | 250 | 0.3910 | 0.8267 | |
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| 0.5352 | 0.48 | 300 | 0.3888 | 0.82 | |
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| 0.415 | 0.56 | 350 | 0.3887 | 0.8267 | |
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| 0.4716 | 0.64 | 400 | 0.3888 | 0.84 | |
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| 0.4565 | 0.72 | 450 | 0.3619 | 0.84 | |
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| 0.4447 | 0.8 | 500 | 0.3758 | 0.8333 | |
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| 0.4407 | 0.88 | 550 | 0.3664 | 0.8133 | |
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| 0.46 | 0.96 | 600 | 0.3797 | 0.84 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0 |
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- Datasets 2.3.2 |
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- Tokenizers 0.13.1 |
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