Instructions to use Maeli-k/langid-ner-xlm-v-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maeli-k/langid-ner-xlm-v-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Maeli-k/langid-ner-xlm-v-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maeli-k/langid-ner-xlm-v-base") model = AutoModelForTokenClassification.from_pretrained("Maeli-k/langid-ner-xlm-v-base") - Notebooks
- Google Colab
- Kaggle
langid-ner-xlm-v-base
This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0623
- Precision: 0.6844
- Recall: 0.6565
- F1: 0.6701
- Accuracy: 0.7686
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 72 | 1.4927 | 0.6096 | 0.5201 | 0.5613 | 0.5299 |
| No log | 2.0 | 144 | 1.2215 | 0.6117 | 0.5973 | 0.6044 | 0.6922 |
| No log | 3.0 | 216 | 1.1246 | 0.6264 | 0.6300 | 0.6282 | 0.7487 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for Maeli-k/langid-ner-xlm-v-base
Base model
facebook/xlm-v-base