---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- nergrit
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-finetuned-ner-nergrit-8H-light
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: nergrit
      type: nergrit
      config: nergrit_ner_seacrowd_seq_label
      split: validation
      args: nergrit_ner_seacrowd_seq_label
    metrics:
    - name: Precision
      type: precision
      value: 0.981006671007531
    - name: Recall
      type: recall
      value: 0.9810548818694482
    - name: F1
      type: f1
      value: 0.9810307758461823
    - name: Accuracy
      type: accuracy
      value: 0.9772770466099682
---

<!-- 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. -->

# roberta-finetuned-ner-nergrit-8H-light

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the nergrit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1130
- Precision: 0.9810
- Recall: 0.9811
- F1: 0.9810
- Accuracy: 0.9773

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9994 | 392  | 0.1196          | 0.9793    | 0.9800 | 0.9796 | 0.9757   |
| 0.1919        | 1.9987 | 784  | 0.1048          | 0.9810    | 0.9814 | 0.9812 | 0.9775   |
| 0.0823        | 2.9981 | 1176 | 0.1130          | 0.9810    | 0.9811 | 0.9810 | 0.9773   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1