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--- |
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tags: |
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- generated_from_trainer |
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base_model: cardiffnlp/twitter-roberta-base-sentiment-latest |
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metrics: |
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- accuracy |
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model-index: |
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- name: fine-tuned-twitter-roberta-base-sentiment-latest |
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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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should probably proofread and complete it, then remove this comment. --> |
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# fine-tuned-twitter-roberta-base-sentiment-latest |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3062 |
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- Accuracy: {'accuracy': 0.8868131868131868} |
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- F1score: {'f1': 0.88247351021472} |
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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: 5e-05 |
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- train_batch_size: 8 |
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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 | F1score | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:--------------------------:| |
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| 0.461 | 0.2443 | 500 | 0.3381 | {'accuracy': 0.8565934065934065} | {'f1': 0.8483856477235431} | |
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| 0.3702 | 0.4885 | 1000 | 0.3378 | {'accuracy': 0.865934065934066} | {'f1': 0.8655309886906097} | |
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| 0.3574 | 0.7328 | 1500 | 0.2971 | {'accuracy': 0.8714285714285714} | {'f1': 0.8709435986031107} | |
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| 0.3358 | 0.9770 | 2000 | 0.3062 | {'accuracy': 0.8868131868131868} | {'f1': 0.88247351021472} | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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