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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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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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base_model: distilbert-base-uncased-finetuned-sst-2-english |
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model-index: |
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- name: twitter-data-distilbert-base-uncased-sentiment-finetuned-memes |
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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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# twitter-data-distilbert-base-uncased-sentiment-finetuned-memes |
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This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2474 |
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- Accuracy: 0.9282 |
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- Precision: 0.9290 |
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- Recall: 0.9282 |
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- F1: 0.9282 |
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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: 1e-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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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.3623 | 1.0 | 1762 | 0.3171 | 0.8986 | 0.8995 | 0.8986 | 0.8981 | |
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| 0.271 | 2.0 | 3524 | 0.2665 | 0.9176 | 0.9182 | 0.9176 | 0.9173 | |
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| 0.2386 | 3.0 | 5286 | 0.2499 | 0.9237 | 0.9254 | 0.9237 | 0.9239 | |
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| 0.2136 | 4.0 | 7048 | 0.2494 | 0.9259 | 0.9263 | 0.9259 | 0.9257 | |
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| 0.1974 | 5.0 | 8810 | 0.2454 | 0.9278 | 0.9288 | 0.9278 | 0.9278 | |
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| 0.182 | 6.0 | 10572 | 0.2474 | 0.9282 | 0.9290 | 0.9282 | 0.9282 | |
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### Framework versions |
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- Transformers 4.24.0.dev0 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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