Token Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("rootacess/xlm-roberta-base-finetuned")
model = AutoModelForTokenClassification.from_pretrained("rootacess/xlm-roberta-base-finetuned")Quick Links
xlm-roberta-base-finetuned
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set:
- Loss: 0.1358
- F1: 0.8638
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 |
|---|---|---|---|---|
| 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 |
| 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 |
| 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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Dataset used to train rootacess/xlm-roberta-base-finetuned
Evaluation results
- F1 on xtremevalidation set self-reported0.864
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rootacess/xlm-roberta-base-finetuned")