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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ind_roberta |
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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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# ind_roberta |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3951 |
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- Accuracy@en: 0.9367 |
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- F1@en: 0.9341 |
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- Precision@en: 0.9360 |
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- Recall@en: 0.9324 |
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- Loss@en: 0.3951 |
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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: 8 |
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- eval_batch_size: 4 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en | Precision@en | Recall@en | Loss@en | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------------:|:---------:|:-------:| |
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| 0.1854 | 1.0 | 375 | 0.4027 | 0.9033 | 0.8994 | 0.9012 | 0.8979 | 0.4027 | |
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| 0.203 | 2.0 | 750 | 0.4013 | 0.89 | 0.8877 | 0.8845 | 0.8944 | 0.4013 | |
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| 0.1282 | 3.0 | 1125 | 0.6106 | 0.89 | 0.8883 | 0.8858 | 0.8983 | 0.6106 | |
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| 0.0811 | 4.0 | 1500 | 0.3951 | 0.9367 | 0.9341 | 0.9360 | 0.9324 | 0.3951 | |
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| 0.0425 | 5.0 | 1875 | 0.4764 | 0.93 | 0.9282 | 0.9250 | 0.9333 | 0.4764 | |
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| 0.005 | 6.0 | 2250 | 0.5299 | 0.9367 | 0.9343 | 0.9349 | 0.9337 | 0.5299 | |
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| 0.0147 | 7.0 | 2625 | 0.5200 | 0.93 | 0.9285 | 0.9249 | 0.9359 | 0.5200 | |
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| 0.0182 | 8.0 | 3000 | 0.5532 | 0.9267 | 0.9242 | 0.9232 | 0.9253 | 0.5532 | |
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| 0.0125 | 9.0 | 3375 | 0.5398 | 0.9367 | 0.9346 | 0.9331 | 0.9363 | 0.5398 | |
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| 0.0171 | 10.0 | 3750 | 0.5157 | 0.9367 | 0.9349 | 0.9321 | 0.9389 | 0.5157 | |
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| 0.0109 | 11.0 | 4125 | 0.6538 | 0.92 | 0.9184 | 0.9149 | 0.9261 | 0.6538 | |
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| 0.0054 | 12.0 | 4500 | 0.5676 | 0.93 | 0.9281 | 0.9253 | 0.9320 | 0.5676 | |
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| 0.0047 | 13.0 | 4875 | 0.6763 | 0.9167 | 0.9146 | 0.9114 | 0.9195 | 0.6763 | |
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| 0.0076 | 14.0 | 5250 | 0.6970 | 0.9133 | 0.9109 | 0.9084 | 0.9141 | 0.6970 | |
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| 0.0066 | 15.0 | 5625 | 0.6947 | 0.9167 | 0.9146 | 0.9114 | 0.9195 | 0.6947 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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