Instructions to use benitezfj/wikiann-xlm-v-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benitezfj/wikiann-xlm-v-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benitezfj/wikiann-xlm-v-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("benitezfj/wikiann-xlm-v-base") model = AutoModelForTokenClassification.from_pretrained("benitezfj/wikiann-xlm-v-base") - Notebooks
- Google Colab
- Kaggle
wikiann-xlm-v-base
This model is a fine-tuned version of mmaguero/multilingual-bert-gn-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2623
- Precision: 0.8140
- Recall: 0.8431
- F1: 0.8283
- Accuracy: 0.9237
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3445 | 1.0 | 1764 | 0.2937 | 0.7536 | 0.7930 | 0.7728 | 0.9034 |
| 0.2356 | 2.0 | 3528 | 0.2551 | 0.7940 | 0.8295 | 0.8114 | 0.9194 |
| 0.1775 | 3.0 | 5292 | 0.2510 | 0.8124 | 0.8455 | 0.8286 | 0.9247 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for benitezfj/wikiann-xlm-v-base
Base model
mmaguero/multilingual-bert-gn-base-cased