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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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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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model-index: |
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- name: twitter_sentiment_small_4 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/abson-/twitter_sentiment_small/runs/c6phae8a) |
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# twitter_sentiment_small_4 |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4844 |
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- Accuracy: 0.824 |
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- F1-score: 0.8057 |
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- Precision: 0.8295 |
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- Recall: 0.7833 |
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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: 16 |
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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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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:| |
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| 0.643 | 0.0889 | 100 | 0.5191 | 0.774 | 0.7466 | 0.7817 | 0.7146 | |
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| 0.4779 | 0.1778 | 200 | 0.4895 | 0.787 | 0.7677 | 0.7805 | 0.7554 | |
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| 0.4069 | 0.2667 | 300 | 0.4630 | 0.795 | 0.7745 | 0.7946 | 0.7554 | |
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| 0.4215 | 0.3556 | 400 | 0.4562 | 0.8 | 0.7669 | 0.8393 | 0.7060 | |
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| 0.4187 | 0.4444 | 500 | 0.4350 | 0.807 | 0.7992 | 0.7758 | 0.8240 | |
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| 0.4197 | 0.5333 | 600 | 0.4497 | 0.806 | 0.7785 | 0.8317 | 0.7318 | |
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| 0.4034 | 0.6222 | 700 | 0.4335 | 0.817 | 0.8111 | 0.7813 | 0.8433 | |
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| 0.4058 | 0.7111 | 800 | 0.4231 | 0.804 | 0.7996 | 0.7637 | 0.8391 | |
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| 0.4044 | 0.8 | 900 | 0.4404 | 0.805 | 0.8056 | 0.7523 | 0.8670 | |
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| 0.3678 | 0.8889 | 1000 | 0.4000 | 0.815 | 0.8095 | 0.7782 | 0.8433 | |
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| 0.3791 | 0.9778 | 1100 | 0.4451 | 0.814 | 0.814 | 0.7622 | 0.8734 | |
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| 0.3109 | 1.0667 | 1200 | 0.5034 | 0.817 | 0.8039 | 0.8030 | 0.8047 | |
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| 0.2999 | 1.1556 | 1300 | 0.4740 | 0.812 | 0.8105 | 0.7643 | 0.8627 | |
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| 0.2902 | 1.2444 | 1400 | 0.4517 | 0.825 | 0.8066 | 0.8314 | 0.7833 | |
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| 0.2664 | 1.3333 | 1500 | 0.4646 | 0.83 | 0.8225 | 0.8008 | 0.8455 | |
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| 0.2826 | 1.4222 | 1600 | 0.4844 | 0.824 | 0.8057 | 0.8295 | 0.7833 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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