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# xlm-roberta-large-ner-silvanus

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.9575
- Recall: 0.9665
- F1: 0.9619
- Accuracy: 0.9889

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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        | 1.0   | 427  | 0.0560          | 0.9339    | 0.9514 | 0.9426 | 0.9828   |
| 0.1405        | 2.0   | 855  | 0.0539          | 0.9430    | 0.9595 | 0.9512 | 0.9859   |
| 0.0449        | 3.0   | 1281 | 0.0495          | 0.9575    | 0.9665 | 0.9619 | 0.9889   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-large", "model-index": [{"name": "xlm-roberta-large-ner-silvanus", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "id", "split": "validation", "args": "id"}, "metrics": [{"type": "precision", "value": 0.9574581228396704, "name": "Precision"}, {"type": "recall", "value": 0.9664519592055824, "name": "Recall"}, {"type": "f1", "value": 0.9619340189662082, "name": "F1"}, {"type": "accuracy", "value": 0.9889216263995286, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors xlm-roberta token-classification generated_from_trainer dataset:wikiann base_model:xlm-roberta-large license:mit model-index autotrain_compatible endpoints_compatible region:us