uner_all
This model is a fine-tuned version of xlm-roberta-large on the uner_all dataset. The uner_all dataset combines all training datasets in UNER. It achieves the following results on the evaluation set:
- Loss: 0.1180
- Precision: 0.8566
- Recall: 0.8523
- F1: 0.8544
- Accuracy: 0.9843
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
- 46
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for universalner/uner_all
Base model
FacebookAI/xlm-roberta-largeSpace using universalner/uner_all 1
Evaluation results
- Precision on uner_allself-reported0.857
- Recall on uner_allself-reported0.852
- F1 on uner_allself-reported0.854
- Accuracy on uner_allself-reported0.984