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--- |
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library_name: transformers |
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license: mit |
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base_model: intfloat/e5-base-v2 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: e5-base-v2-sentiment-twitter |
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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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# e5-base-v2-sentiment-twitter |
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This model is a fine-tuned version of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6678 |
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- Accuracy: 0.7186 |
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- F1: 0.7186 |
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- Precision: 0.7219 |
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- Recall: 0.7186 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6842 | 0.1754 | 500 | 0.6403 | 0.7195 | 0.7209 | 0.7232 | 0.7195 | |
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| 0.6395 | 0.3508 | 1000 | 0.6110 | 0.7215 | 0.7254 | 0.7374 | 0.7215 | |
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| 0.6188 | 0.5261 | 1500 | 0.6028 | 0.733 | 0.7360 | 0.7442 | 0.733 | |
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| 0.6291 | 0.7015 | 2000 | 0.5912 | 0.738 | 0.7338 | 0.7403 | 0.738 | |
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| 0.6005 | 0.8769 | 2500 | 0.5705 | 0.752 | 0.7534 | 0.7572 | 0.752 | |
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| 0.3942 | 1.0523 | 3000 | 0.6278 | 0.747 | 0.7469 | 0.7525 | 0.747 | |
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| 0.4603 | 1.2276 | 3500 | 0.6185 | 0.75 | 0.7509 | 0.7536 | 0.75 | |
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| 0.4579 | 1.4030 | 4000 | 0.6348 | 0.751 | 0.7491 | 0.7526 | 0.751 | |
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| 0.4264 | 1.5784 | 4500 | 0.6129 | 0.757 | 0.7573 | 0.7579 | 0.757 | |
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| 0.4196 | 1.7538 | 5000 | 0.6196 | 0.7585 | 0.7582 | 0.7582 | 0.7585 | |
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| 0.4193 | 1.9291 | 5500 | 0.6159 | 0.7625 | 0.7611 | 0.7615 | 0.7625 | |
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### Framework versions |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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