File size: 3,630 Bytes
44f5e04 7206801 44f5e04 3706ccd 44f5e04 7206801 44f5e04 7206801 44f5e04 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_410
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. -->
# populism_classifier_410
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7024
- Accuracy: 0.1263
- 1-f1: 0.2242
- 1-recall: 1.0
- 1-precision: 0.1263
- Balanced Acc: 0.5
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6697 | 1.0 | 50 | 0.6939 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
| 0.6672 | 2.0 | 100 | 0.7424 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
| 0.8553 | 3.0 | 150 | 0.8350 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
| 0.6019 | 4.0 | 200 | 0.7917 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
| 0.5024 | 5.0 | 250 | 0.6939 | 0.1414 | 0.2273 | 1.0 | 0.1282 | 0.5087 |
| 0.6982 | 6.0 | 300 | 0.7261 | 0.1364 | 0.2262 | 1.0 | 0.1276 | 0.5058 |
| 0.8583 | 7.0 | 350 | 0.7103 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
| 0.5487 | 8.0 | 400 | 0.7149 | 0.1364 | 0.2262 | 1.0 | 0.1276 | 0.5058 |
| 0.5309 | 9.0 | 450 | 0.7218 | 0.1313 | 0.2252 | 1.0 | 0.1269 | 0.5029 |
| 0.5436 | 10.0 | 500 | 0.6868 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 |
| 0.5617 | 11.0 | 550 | 0.6876 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 |
| 0.8479 | 12.0 | 600 | 0.6812 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 |
| 1.4176 | 13.0 | 650 | 0.6805 | 0.1818 | 0.2358 | 1.0 | 0.1337 | 0.5318 |
| 0.3742 | 14.0 | 700 | 0.6920 | 0.1566 | 0.2304 | 1.0 | 0.1302 | 0.5173 |
| 0.7671 | 15.0 | 750 | 0.6905 | 0.1465 | 0.2283 | 1.0 | 0.1289 | 0.5116 |
| 0.994 | 16.0 | 800 | 0.6967 | 0.1313 | 0.2252 | 1.0 | 0.1269 | 0.5029 |
| 0.3561 | 17.0 | 850 | 0.6962 | 0.1414 | 0.2273 | 1.0 | 0.1282 | 0.5087 |
| 0.5991 | 18.0 | 900 | 0.7024 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|