File size: 3,395 Bytes
dcfd431 a780843 dcfd431 d54ae6e dcfd431 d54ae6e dcfd431 d54ae6e dcfd431 a780843 dcfd431 a780843 d54ae6e dcfd431 d54ae6e dcfd431 a780843 d54ae6e a780843 dcfd431 d54ae6e a780843 dcfd431 a780843 | 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 | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: Robeczech-CERED2
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. -->
# Robeczech-CERED2
This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1300
- Accuracy: 0.8985
- Micro Precision: 0.8985
- Micro Recall: 0.8985
- Micro F1: 0.8985
- Macro Precision: 0.8711
- Macro Recall: 0.8608
- Macro F1: 0.8632
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
|:-------------:|:------:|:------:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|
| 1.1585 | 1.0000 | 11305 | 1.1208 | 0.8608 | 0.8608 | 0.8608 | 0.8608 | 0.8155 | 0.7878 | 0.7914 |
| 1.0617 | 2.0 | 22611 | 1.0567 | 0.8873 | 0.8873 | 0.8873 | 0.8873 | 0.8547 | 0.8428 | 0.8430 |
| 0.9804 | 3.0000 | 33916 | 1.0558 | 0.8900 | 0.8900 | 0.8900 | 0.8900 | 0.8546 | 0.8414 | 0.8438 |
| 0.9327 | 4.0 | 45222 | 1.0585 | 0.8920 | 0.8920 | 0.8920 | 0.8920 | 0.8557 | 0.8475 | 0.8483 |
| 0.8927 | 5.0000 | 56527 | 1.0820 | 0.8917 | 0.8917 | 0.8917 | 0.8917 | 0.8484 | 0.8499 | 0.8455 |
| 0.861 | 6.0 | 67833 | 1.0774 | 0.8982 | 0.8982 | 0.8982 | 0.8982 | 0.8596 | 0.8567 | 0.8545 |
| 0.8344 | 7.0000 | 79138 | 1.0987 | 0.8979 | 0.8979 | 0.8979 | 0.8979 | 0.8641 | 0.8558 | 0.8567 |
| 0.8222 | 8.0 | 90444 | 1.1113 | 0.8991 | 0.8991 | 0.8991 | 0.8991 | 0.8639 | 0.8544 | 0.8558 |
| 0.8096 | 9.0000 | 101749 | 1.1159 | 0.9001 | 0.9001 | 0.9001 | 0.9001 | 0.8584 | 0.8589 | 0.8552 |
| 0.8071 | 9.9996 | 113050 | 1.1176 | 0.8994 | 0.8994 | 0.8994 | 0.8994 | 0.8561 | 0.8577 | 0.8539 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|