metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FULL-3epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
results: []
FULL-3epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
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: 0.2571
- Accuracy: 0.3011
- Precision: 0.7306
- Recall: 0.6846
- F1: 0.5945
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 0.9923 | 97 | 0.5290 | 0.2637 | 0.6408 | 0.5061 | 0.4053 |
| No log | 1.9949 | 195 | 0.2969 | 0.2962 | 0.7163 | 0.6474 | 0.5560 |
| No log | 2.9770 | 291 | 0.2571 | 0.3011 | 0.7306 | 0.6846 | 0.5945 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.19.2
- Tokenizers 0.19.1