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---
base_model: UWB-AIR/Czert-B-base-cased
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_2_0_Czert-B-base-cased
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: 0.8093464273620048
- name: Recall
type: recall
value: 0.8547925608011445
- name: F1
type: f1
value: 0.8314489476430683
- name: Accuracy
type: accuracy
value: 0.9446311123820418
---
<!-- 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_Czert-B-base-cased
This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3352
- Precision: 0.8093
- Recall: 0.8548
- F1: 0.8314
- Accuracy: 0.9446
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5496 | 2.22 | 500 | 0.2782 | 0.7301 | 0.7750 | 0.7519 | 0.9275 |
| 0.2133 | 4.44 | 1000 | 0.2487 | 0.7811 | 0.8219 | 0.8010 | 0.9399 |
| 0.144 | 6.67 | 1500 | 0.2580 | 0.7737 | 0.8290 | 0.8004 | 0.9396 |
| 0.1029 | 8.89 | 2000 | 0.2576 | 0.7997 | 0.8480 | 0.8231 | 0.9446 |
| 0.0776 | 11.11 | 2500 | 0.2849 | 0.7990 | 0.8516 | 0.8244 | 0.9444 |
| 0.0601 | 13.33 | 3000 | 0.2971 | 0.8021 | 0.8523 | 0.8264 | 0.9450 |
| 0.0494 | 15.56 | 3500 | 0.3077 | 0.8014 | 0.8473 | 0.8237 | 0.9440 |
| 0.0408 | 17.78 | 4000 | 0.3145 | 0.8131 | 0.8555 | 0.8337 | 0.9448 |
| 0.0353 | 20.0 | 4500 | 0.3260 | 0.8097 | 0.8569 | 0.8327 | 0.9445 |
| 0.0311 | 22.22 | 5000 | 0.3356 | 0.8076 | 0.8541 | 0.8302 | 0.9441 |
| 0.0281 | 24.44 | 5500 | 0.3352 | 0.8093 | 0.8548 | 0.8314 | 0.9446 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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