metadata
license: mit
tags:
- generated_from_trainer
datasets:
- conllpp
metrics:
- precision
- recall
- f1
- accuracy
base_model: roberta-large
model-index:
- name: roberta-large-md-conllpp
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conllpp
type: conllpp
config: conllpp
split: train
args: conllpp
metrics:
- type: precision
value: 0.9971177780689113
name: Precision
- type: recall
value: 0.9968043586452576
name: Recall
- type: f1
value: 0.9969610437242934
name: F1
- type: accuracy
value: 0.995003768708948
name: Accuracy
roberta-large-md-conllpp
This model is a fine-tuned version of roberta-large on the conllpp dataset. It achieves the following results on the evaluation set:
- Loss: 0.0457
- Precision: 0.9971
- Recall: 0.9968
- F1: 0.9970
- Accuracy: 0.9950
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: 1e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0748 | 1.0 | 878 | 0.0309 | 0.9959 | 0.9962 | 0.9961 | 0.9935 |
| 0.0111 | 2.0 | 1756 | 0.0346 | 0.9974 | 0.9967 | 0.9970 | 0.9951 |
| 0.0057 | 3.0 | 2634 | 0.0348 | 0.9974 | 0.9960 | 0.9967 | 0.9946 |
| 0.0031 | 4.0 | 3512 | 0.0434 | 0.9976 | 0.9964 | 0.9970 | 0.9951 |
| 0.0017 | 5.0 | 4390 | 0.0457 | 0.9971 | 0.9968 | 0.9970 | 0.9950 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1