---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_xlm-roberta-large
  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: 0.8566729323308271
    - name: Recall
      type: recall
      value: 0.9047146401985111
    - name: F1
      type: f1
      value: 0.8800386193579531
    - name: Accuracy
      type: accuracy
      value: 0.9771662763466042
language:
- cs
---

# CNEC_xlm-roberta-large

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the [cnec](https://lindat.cz/repository/xmlui/handle/11234/1-3493) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1471
- Precision: 0.8567
- Recall: 0.9047
- F1: 0.8800
- Accuracy: 0.9772

## Model description
The entities are described as:
- 'O' = Outside of a named entity
- 'B-A' = Beginning of a complex address number (Postal code, street number, even phone number)
- 'I-A' = Inside of a number in the address
- 'B-G' = Beginning of a geographical name
- 'I-G' = Inside of a geographical name
- 'B-I' = Beginning of an institution name
- 'I-I' = Inside of an institution name
- 'B-M' = Beginning of a media name (email, server, website, tv series, etc.)
- 'I-M' = Inside of a media name
- 'B-O' = Beginning of an artifact name (book, old movies, etc.)
- 'I-O' = Inside of an artifact name
- 'B-P' = Beginning of a person's name
- 'I-P' = Inside of a person's name
- 'B-T' = Beginning of a time expression
- 'I-T' = Inside of a time expression

## Intended uses & limitations

CNEC or Czech named entity corpus is a dataset aimed at the Czech language. This is an edited version of the dataset with only 7 supertypes and 1 type for non-entity.

## Training and evaluation data

The model was trained with an increased dropout rate to 0.2 for hidden_dropout_prob and 0.15 for attention_probs_dropout_prob

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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
- weight_decay = 0.01
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2836        | 1.12  | 500  | 0.1341          | 0.7486    | 0.8467 | 0.7946 | 0.9649   |
| 0.116         | 2.24  | 1000 | 0.1048          | 0.7866    | 0.8655 | 0.8242 | 0.9734   |
| 0.0832        | 3.36  | 1500 | 0.1066          | 0.7967    | 0.8734 | 0.8333 | 0.9746   |
| 0.0577        | 4.47  | 2000 | 0.1112          | 0.8408    | 0.8834 | 0.8616 | 0.9753   |
| 0.0445        | 5.59  | 2500 | 0.1378          | 0.8384    | 0.8883 | 0.8627 | 0.9751   |
| 0.0337        | 6.71  | 3000 | 0.1272          | 0.8505    | 0.8978 | 0.8735 | 0.9770   |
| 0.025         | 7.83  | 3500 | 0.1447          | 0.8462    | 0.9007 | 0.8726 | 0.9760   |
| 0.0191        | 8.95  | 4000 | 0.1471          | 0.8567    | 0.9047 | 0.8800 | 0.9772   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0