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---
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55
This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2180
- Accuracy: 0.5999
- F1: 0.6008
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.2423 | 1.09 | 500 | 1.2214 | 0.4842 | 0.4591 |
| 1.1465 | 2.17 | 1000 | 1.2081 | 0.5498 | 0.5406 |
| 1.089 | 3.26 | 1500 | 1.2345 | 0.5540 | 0.5476 |
| 1.043 | 4.35 | 2000 | 1.2340 | 0.5756 | 0.5777 |
| 1.01 | 5.43 | 2500 | 1.2397 | 0.5706 | 0.5717 |
| 0.9787 | 6.52 | 3000 | 1.2536 | 0.5718 | 0.5723 |
| 0.9656 | 7.61 | 3500 | 1.2564 | 0.5579 | 0.5603 |
| 0.9505 | 8.7 | 4000 | 1.2641 | 0.5644 | 0.5660 |
| 0.9432 | 9.78 | 4500 | 1.2385 | 0.5880 | 0.5876 |
| 0.9304 | 10.87 | 5000 | 1.2612 | 0.5864 | 0.5862 |
| 0.9245 | 11.96 | 5500 | 1.2567 | 0.5748 | 0.5728 |
| 0.9189 | 13.04 | 6000 | 1.2463 | 0.5745 | 0.5745 |
| 0.9131 | 14.13 | 6500 | 1.2599 | 0.5729 | 0.5738 |
| 0.9098 | 15.22 | 7000 | 1.2614 | 0.5706 | 0.5704 |
| 0.9052 | 16.3 | 7500 | 1.2468 | 0.5741 | 0.5748 |
| 0.9013 | 17.39 | 8000 | 1.2550 | 0.5756 | 0.5775 |
| 0.8972 | 18.48 | 8500 | 1.2661 | 0.5733 | 0.5743 |
| 0.8972 | 19.57 | 9000 | 1.2506 | 0.5783 | 0.5780 |
| 0.8912 | 20.65 | 9500 | 1.2519 | 0.5737 | 0.5752 |
| 0.8903 | 21.74 | 10000 | 1.2313 | 0.5795 | 0.5782 |
| 0.8868 | 22.83 | 10500 | 1.2384 | 0.5895 | 0.5896 |
| 0.8847 | 23.91 | 11000 | 1.2474 | 0.5752 | 0.5736 |
| 0.8834 | 25.0 | 11500 | 1.2458 | 0.5791 | 0.5795 |
| 0.8815 | 26.09 | 12000 | 1.2548 | 0.5748 | 0.5739 |
| 0.8794 | 27.17 | 12500 | 1.2378 | 0.5864 | 0.5857 |
| 0.8791 | 28.26 | 13000 | 1.2327 | 0.5968 | 0.5953 |
| 0.8749 | 29.35 | 13500 | 1.2249 | 0.5949 | 0.5935 |
| 0.8748 | 30.43 | 14000 | 1.2309 | 0.5938 | 0.5905 |
| 0.8734 | 31.52 | 14500 | 1.2242 | 0.5880 | 0.5885 |
| 0.872 | 32.61 | 15000 | 1.2372 | 0.5841 | 0.5856 |
| 0.8712 | 33.7 | 15500 | 1.2394 | 0.5783 | 0.5800 |
| 0.87 | 34.78 | 16000 | 1.2363 | 0.5922 | 0.5921 |
| 0.8692 | 35.87 | 16500 | 1.2375 | 0.5903 | 0.5916 |
| 0.8677 | 36.96 | 17000 | 1.2341 | 0.5968 | 0.5951 |
| 0.8672 | 38.04 | 17500 | 1.2227 | 0.6038 | 0.6013 |
| 0.8657 | 39.13 | 18000 | 1.2250 | 0.5899 | 0.5904 |
| 0.865 | 40.22 | 18500 | 1.2275 | 0.5949 | 0.5952 |
| 0.865 | 41.3 | 19000 | 1.2196 | 0.5953 | 0.5958 |
| 0.864 | 42.39 | 19500 | 1.2375 | 0.5818 | 0.5815 |
| 0.8636 | 43.48 | 20000 | 1.2373 | 0.5849 | 0.5856 |
| 0.8635 | 44.57 | 20500 | 1.2292 | 0.5930 | 0.5940 |
| 0.8622 | 45.65 | 21000 | 1.2243 | 0.5903 | 0.5914 |
| 0.8619 | 46.74 | 21500 | 1.2198 | 0.5984 | 0.5992 |
| 0.8608 | 47.83 | 22000 | 1.2175 | 0.6046 | 0.6054 |
| 0.8621 | 48.91 | 22500 | 1.2179 | 0.5995 | 0.6004 |
| 0.8606 | 50.0 | 23000 | 1.2180 | 0.5999 | 0.6008 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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