Instructions to use gechim/xlm-roberta-base-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gechim/xlm-roberta-base-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="gechim/xlm-roberta-base-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("gechim/xlm-roberta-base-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("gechim/xlm-roberta-base-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlm-roberta-base-finetuned-ner
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1768
- F1: 0.9069
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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|---|---|---|---|---|
| 0.3669 | 1.0 | 834 | 0.2067 | 0.8725 |
| 0.1856 | 2.0 | 1668 | 0.1793 | 0.8971 |
| 0.1207 | 3.0 | 2502 | 0.1768 | 0.9069 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for gechim/xlm-roberta-base-finetuned-ner
Base model
FacebookAI/xlm-roberta-base