metadata
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_xlm-roberta-large-finetuned-conll03
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9136102902833304
- name: Recall
type: recall
value: 0.9305949008498584
- name: F1
type: f1
value: 0.9220243838259802
- name: Accuracy
type: accuracy
value: 0.9833530741897276
my_xlm-roberta-large-finetuned-conll03
This model is a fine-tuned version of xlm-roberta-large on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1119
- Precision: 0.9136
- Recall: 0.9306
- F1: 0.9220
- Accuracy: 0.9834
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2235 | 1.0 | 878 | 0.1090 | 0.8998 | 0.9125 | 0.9061 | 0.9812 |
| 0.0302 | 2.0 | 1756 | 0.1119 | 0.9136 | 0.9306 | 0.9220 | 0.9834 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1