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---
license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
- generated_from_trainer
datasets:
- stulcrad/CNEC2_0_flat
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_2_0_robeczech-base
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cnec
type: cnec
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.853103448275862
- name: Recall
type: recall
value: 0.8848354792560801
- name: F1
type: f1
value: 0.8686797752808989
- name: Accuracy
type: accuracy
value: 0.954457738324971
language:
- cs
---
<!-- 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. -->
# CNEC_2_0_robeczech-base
This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3306
- Precision: 0.8531
- Recall: 0.8848
- F1: 0.8687
- Accuracy: 0.9545
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4499 | 2.22 | 2000 | 0.3871 | 0.7163 | 0.7099 | 0.7131 | 0.9222 |
| 0.2342 | 4.44 | 4000 | 0.2576 | 0.8149 | 0.8251 | 0.8200 | 0.9451 |
| 0.1449 | 6.67 | 6000 | 0.2407 | 0.8231 | 0.8523 | 0.8375 | 0.9492 |
| 0.1027 | 8.89 | 8000 | 0.2267 | 0.8362 | 0.8748 | 0.8551 | 0.9527 |
| 0.0751 | 11.11 | 10000 | 0.2429 | 0.8394 | 0.8712 | 0.8550 | 0.9522 |
| 0.0473 | 13.33 | 12000 | 0.2633 | 0.8439 | 0.8720 | 0.8577 | 0.9535 |
| 0.0369 | 15.56 | 14000 | 0.2821 | 0.8468 | 0.8755 | 0.8609 | 0.9541 |
| 0.0286 | 17.78 | 16000 | 0.2797 | 0.8534 | 0.8827 | 0.8678 | 0.9558 |
| 0.0234 | 20.0 | 18000 | 0.2860 | 0.8550 | 0.8834 | 0.8690 | 0.9558 |
| 0.0168 | 22.22 | 20000 | 0.3146 | 0.8471 | 0.8795 | 0.8630 | 0.9531 |
| 0.0142 | 24.44 | 22000 | 0.3165 | 0.8488 | 0.8816 | 0.8649 | 0.9530 |
| 0.011 | 26.67 | 24000 | 0.3291 | 0.8518 | 0.8816 | 0.8664 | 0.9537 |
| 0.0092 | 28.89 | 26000 | 0.3306 | 0.8531 | 0.8848 | 0.8687 | 0.9545 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 |