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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-PO-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-PO-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: 3.2137
- Accuracy: 0.6150
- F1: 0.6155
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 4.3541 | 1.09 | 500 | 3.1863 | 0.5721 | 0.5729 |
| 3.0722 | 2.17 | 1000 | 3.2433 | 0.5845 | 0.5841 |
| 2.1971 | 3.26 | 1500 | 3.4852 | 0.5907 | 0.5891 |
| 1.6717 | 4.35 | 2000 | 3.3784 | 0.5841 | 0.5859 |
| 1.3144 | 5.43 | 2500 | 3.7616 | 0.5864 | 0.5846 |
| 1.1448 | 6.52 | 3000 | 3.4276 | 0.5779 | 0.5777 |
| 0.9875 | 7.61 | 3500 | 3.3781 | 0.5953 | 0.5946 |
| 0.8762 | 8.7 | 4000 | 3.1471 | 0.5829 | 0.5843 |
| 0.7963 | 9.78 | 4500 | 3.3721 | 0.5752 | 0.5756 |
| 0.7369 | 10.87 | 5000 | 3.5433 | 0.5837 | 0.5839 |
| 0.6786 | 11.96 | 5500 | 3.3185 | 0.5783 | 0.5793 |
| 0.6271 | 13.04 | 6000 | 3.1903 | 0.5903 | 0.5869 |
| 0.5889 | 14.13 | 6500 | 3.3031 | 0.5895 | 0.5899 |
| 0.5569 | 15.22 | 7000 | 3.2597 | 0.5995 | 0.5987 |
| 0.5229 | 16.3 | 7500 | 3.1515 | 0.5883 | 0.5893 |
| 0.4985 | 17.39 | 8000 | 3.1954 | 0.5992 | 0.5997 |
| 0.4751 | 18.48 | 8500 | 3.2670 | 0.5988 | 0.5990 |
| 0.4643 | 19.57 | 9000 | 3.1884 | 0.5961 | 0.5948 |
| 0.4434 | 20.65 | 9500 | 3.2196 | 0.5984 | 0.5972 |
| 0.4381 | 21.74 | 10000 | 3.2211 | 0.5922 | 0.5934 |
| 0.4193 | 22.83 | 10500 | 3.1172 | 0.5992 | 0.5990 |
| 0.4113 | 23.91 | 11000 | 3.3000 | 0.6107 | 0.6114 |
| 0.4008 | 25.0 | 11500 | 3.1179 | 0.6015 | 0.6024 |
| 0.3914 | 26.09 | 12000 | 3.1496 | 0.5926 | 0.5925 |
| 0.3861 | 27.17 | 12500 | 3.2896 | 0.5934 | 0.5942 |
| 0.3768 | 28.26 | 13000 | 3.1737 | 0.6065 | 0.6076 |
| 0.3767 | 29.35 | 13500 | 3.2205 | 0.6038 | 0.6034 |
| 0.3687 | 30.43 | 14000 | 3.3223 | 0.6049 | 0.6046 |
| 0.3663 | 31.52 | 14500 | 3.0866 | 0.6015 | 0.6021 |
| 0.3542 | 32.61 | 15000 | 3.1709 | 0.6107 | 0.6119 |
| 0.3549 | 33.7 | 15500 | 3.2819 | 0.5965 | 0.5976 |
| 0.3476 | 34.78 | 16000 | 3.2686 | 0.5995 | 0.6002 |
| 0.3452 | 35.87 | 16500 | 3.2558 | 0.6015 | 0.6018 |
| 0.346 | 36.96 | 17000 | 3.1651 | 0.5953 | 0.5962 |
| 0.3402 | 38.04 | 17500 | 3.1907 | 0.6084 | 0.6086 |
| 0.3385 | 39.13 | 18000 | 3.1962 | 0.6096 | 0.6098 |
| 0.3389 | 40.22 | 18500 | 3.1716 | 0.6088 | 0.6091 |
| 0.3322 | 41.3 | 19000 | 3.1679 | 0.6092 | 0.6096 |
| 0.3288 | 42.39 | 19500 | 3.2305 | 0.6046 | 0.6044 |
| 0.333 | 43.48 | 20000 | 3.2468 | 0.6011 | 0.6016 |
| 0.33 | 44.57 | 20500 | 3.1011 | 0.6177 | 0.6180 |
| 0.3322 | 45.65 | 21000 | 3.2199 | 0.5980 | 0.5984 |
| 0.3267 | 46.74 | 21500 | 3.2237 | 0.6130 | 0.6132 |
| 0.3268 | 47.83 | 22000 | 3.2034 | 0.6038 | 0.6042 |
| 0.3238 | 48.91 | 22500 | 3.1129 | 0.6103 | 0.6106 |
| 0.324 | 50.0 | 23000 | 3.2137 | 0.6150 | 0.6155 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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