Token Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("summerdevlin46/glot500_model_ru_taiga")
model = AutoModelForTokenClassification.from_pretrained("summerdevlin46/glot500_model_ru_taiga")Quick Links
glot500_model_ru_taiga
This model is a fine-tuned version of cis-lmu/glot500-base on the universal_dependencies dataset. It achieves the following results on the evaluation set:
- Loss: 0.6914
- Precision: 0.8393
- Recall: 0.8246
- F1: 0.8318
- Accuracy: 0.8492
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 197 | 1.0680 | 0.7495 | 0.7185 | 0.7337 | 0.7598 |
| No log | 2.0 | 394 | 0.6914 | 0.8393 | 0.8246 | 0.8318 | 0.8492 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for summerdevlin46/glot500_model_ru_taiga
Base model
cis-lmu/glot500-baseDataset used to train summerdevlin46/glot500_model_ru_taiga
Evaluation results
- Precision on universal_dependenciestest set self-reported0.839
- Recall on universal_dependenciestest set self-reported0.825
- F1 on universal_dependenciestest set self-reported0.832
- Accuracy on universal_dependenciestest set self-reported0.849
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="summerdevlin46/glot500_model_ru_taiga")