---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: german-english-binary-ner-roberta-base-30-final
  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. -->

# german-english-binary-ner-roberta-base-30-final

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1571
- Precision: 0.7113
- Recall: 0.8042
- F1: 0.7549

## 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: 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log        | 2.36  | 250  | 0.0813          | 0.6063    | 0.7778 | 0.6814 |
| 0.0606        | 4.72  | 500  | 0.0871          | 0.6745    | 0.8290 | 0.7438 |
| 0.0606        | 7.08  | 750  | 0.1051          | 0.7218    | 0.7746 | 0.7473 |
| 0.0099        | 9.43  | 1000 | 0.1103          | 0.7428    | 0.7628 | 0.7527 |
| 0.0099        | 11.79 | 1250 | 0.1156          | 0.7349    | 0.7843 | 0.7588 |
| 0.0037        | 14.15 | 1500 | 0.1200          | 0.7323    | 0.7881 | 0.7592 |
| 0.0037        | 16.51 | 1750 | 0.1415          | 0.7139    | 0.7977 | 0.7535 |
| 0.0018        | 18.87 | 2000 | 0.1339          | 0.7218    | 0.7880 | 0.7534 |
| 0.0018        | 21.23 | 2250 | 0.1423          | 0.7533    | 0.7820 | 0.7674 |
| 0.001         | 23.58 | 2500 | 0.1506          | 0.7192    | 0.7806 | 0.7486 |
| 0.001         | 25.94 | 2750 | 0.1521          | 0.7165    | 0.8077 | 0.7594 |
| 0.0006        | 28.3  | 3000 | 0.1571          | 0.7113    | 0.8042 | 0.7549 |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0