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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: beto-sentiment-analysis-finetuned-ner
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. -->
# beto-sentiment-analysis-finetuned-ner
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9250
- Precision: 0.5603
- Recall: 0.6436
- F1: 0.5991
- Accuracy: 0.9863
## 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: 8e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.4102 | 1.0 | 3 | 1.2732 | 0.0455 | 0.0198 | 0.0276 | 0.9723 |
| 0.7776 | 2.0 | 6 | 0.9025 | 0.1056 | 0.1485 | 0.1235 | 0.9663 |
| 0.6861 | 3.0 | 9 | 0.7874 | 0.1176 | 0.1980 | 0.1476 | 0.9694 |
| 0.2837 | 4.0 | 12 | 0.8528 | 0.1067 | 0.2376 | 0.1472 | 0.9534 |
| 0.3182 | 5.0 | 15 | 0.7798 | 0.2360 | 0.3762 | 0.2901 | 0.9729 |
| 0.1673 | 6.0 | 18 | 0.8645 | 0.1461 | 0.2574 | 0.1864 | 0.9604 |
| 0.2065 | 7.0 | 21 | 0.8130 | 0.2941 | 0.5446 | 0.3819 | 0.9765 |
| 0.0794 | 8.0 | 24 | 0.6841 | 0.4276 | 0.6139 | 0.5041 | 0.9822 |
| 0.0543 | 9.0 | 27 | 0.7113 | 0.4104 | 0.5446 | 0.4681 | 0.9815 |
| 0.0278 | 10.0 | 30 | 0.7865 | 0.4565 | 0.6238 | 0.5272 | 0.9833 |
| 0.0598 | 11.0 | 33 | 0.8356 | 0.4155 | 0.5842 | 0.4856 | 0.9824 |
| 0.0108 | 12.0 | 36 | 0.8104 | 0.4460 | 0.6139 | 0.5167 | 0.9826 |
| 0.0235 | 13.0 | 39 | 0.7986 | 0.5194 | 0.6634 | 0.5826 | 0.9844 |
| 0.0134 | 14.0 | 42 | 0.8175 | 0.6182 | 0.6733 | 0.6445 | 0.9865 |
| 0.0124 | 15.0 | 45 | 0.8575 | 0.6036 | 0.6634 | 0.6321 | 0.9875 |
| 0.0049 | 16.0 | 48 | 0.8822 | 0.6019 | 0.6436 | 0.6220 | 0.9871 |
| 0.0097 | 17.0 | 51 | 0.8696 | 0.5556 | 0.6436 | 0.5963 | 0.9862 |
| 0.0067 | 18.0 | 54 | 0.8728 | 0.5410 | 0.6535 | 0.5919 | 0.9859 |
| 0.0045 | 19.0 | 57 | 0.8807 | 0.5159 | 0.6436 | 0.5727 | 0.9848 |
| 0.004 | 20.0 | 60 | 0.8938 | 0.52 | 0.6436 | 0.5752 | 0.9851 |
| 0.0038 | 21.0 | 63 | 0.9108 | 0.5203 | 0.6337 | 0.5714 | 0.9852 |
| 0.004 | 22.0 | 66 | 0.9243 | 0.5702 | 0.6436 | 0.6047 | 0.9864 |
| 0.0106 | 23.0 | 69 | 0.9261 | 0.5702 | 0.6436 | 0.6047 | 0.9865 |
| 0.004 | 24.0 | 72 | 0.9250 | 0.5603 | 0.6436 | 0.5991 | 0.9863 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
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