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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: beto-sentiment-analysis-finetuned-ner |
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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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# beto-sentiment-analysis-finetuned-ner |
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This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9250 |
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- Precision: 0.5603 |
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- Recall: 0.6436 |
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- F1: 0.5991 |
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- Accuracy: 0.9863 |
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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: 8e-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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- num_epochs: 24 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.4102 | 1.0 | 3 | 1.2732 | 0.0455 | 0.0198 | 0.0276 | 0.9723 | |
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| 0.7776 | 2.0 | 6 | 0.9025 | 0.1056 | 0.1485 | 0.1235 | 0.9663 | |
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| 0.6861 | 3.0 | 9 | 0.7874 | 0.1176 | 0.1980 | 0.1476 | 0.9694 | |
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| 0.2837 | 4.0 | 12 | 0.8528 | 0.1067 | 0.2376 | 0.1472 | 0.9534 | |
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| 0.3182 | 5.0 | 15 | 0.7798 | 0.2360 | 0.3762 | 0.2901 | 0.9729 | |
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| 0.1673 | 6.0 | 18 | 0.8645 | 0.1461 | 0.2574 | 0.1864 | 0.9604 | |
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| 0.2065 | 7.0 | 21 | 0.8130 | 0.2941 | 0.5446 | 0.3819 | 0.9765 | |
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| 0.0794 | 8.0 | 24 | 0.6841 | 0.4276 | 0.6139 | 0.5041 | 0.9822 | |
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| 0.0543 | 9.0 | 27 | 0.7113 | 0.4104 | 0.5446 | 0.4681 | 0.9815 | |
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| 0.0278 | 10.0 | 30 | 0.7865 | 0.4565 | 0.6238 | 0.5272 | 0.9833 | |
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| 0.0598 | 11.0 | 33 | 0.8356 | 0.4155 | 0.5842 | 0.4856 | 0.9824 | |
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| 0.0108 | 12.0 | 36 | 0.8104 | 0.4460 | 0.6139 | 0.5167 | 0.9826 | |
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| 0.0235 | 13.0 | 39 | 0.7986 | 0.5194 | 0.6634 | 0.5826 | 0.9844 | |
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| 0.0134 | 14.0 | 42 | 0.8175 | 0.6182 | 0.6733 | 0.6445 | 0.9865 | |
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| 0.0124 | 15.0 | 45 | 0.8575 | 0.6036 | 0.6634 | 0.6321 | 0.9875 | |
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| 0.0049 | 16.0 | 48 | 0.8822 | 0.6019 | 0.6436 | 0.6220 | 0.9871 | |
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| 0.0097 | 17.0 | 51 | 0.8696 | 0.5556 | 0.6436 | 0.5963 | 0.9862 | |
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| 0.0067 | 18.0 | 54 | 0.8728 | 0.5410 | 0.6535 | 0.5919 | 0.9859 | |
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| 0.0045 | 19.0 | 57 | 0.8807 | 0.5159 | 0.6436 | 0.5727 | 0.9848 | |
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| 0.004 | 20.0 | 60 | 0.8938 | 0.52 | 0.6436 | 0.5752 | 0.9851 | |
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| 0.0038 | 21.0 | 63 | 0.9108 | 0.5203 | 0.6337 | 0.5714 | 0.9852 | |
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| 0.004 | 22.0 | 66 | 0.9243 | 0.5702 | 0.6436 | 0.6047 | 0.9864 | |
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| 0.0106 | 23.0 | 69 | 0.9261 | 0.5702 | 0.6436 | 0.6047 | 0.9865 | |
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| 0.004 | 24.0 | 72 | 0.9250 | 0.5603 | 0.6436 | 0.5991 | 0.9863 | |
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### Framework versions |
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- Transformers 4.22.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.2 |
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- Tokenizers 0.12.1 |
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