# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Ciphur/xlm-roberta-base_finetuned")
model = AutoModelForTokenClassification.from_pretrained("Ciphur/xlm-roberta-base_finetuned")Quick Links
xlm-roberta-base_finetuned
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2287
- Precision: 0.5953
- Recall: 0.7081
- F1: 0.6468
- Accuracy: 0.9272
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: 8
- eval_batch_size: 8
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.2634 | 0.5563 | 0.6430 | 0.5965 | 0.9173 |
| No log | 2.0 | 250 | 0.2297 | 0.5871 | 0.7093 | 0.6424 | 0.9249 |
| No log | 3.0 | 375 | 0.2287 | 0.5953 | 0.7081 | 0.6468 | 0.9272 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Ciphur/xlm-roberta-base_finetuned
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ciphur/xlm-roberta-base_finetuned")