metadata
language:
- en
license: mit
base_model: roberta-base
tags:
- pytorch
- RobertaForTokenClassification
- named-entity-recognition
- roberta-base
- generated_from_trainer
metrics:
- recall
- precision
- f1
- accuracy
model-index:
- name: roberta-base-ontonotes
results: []
roberta-base-ontonotes
This model is a fine-tuned version of roberta-base on the tner/ontonotes5 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0695
- Recall: 0.9227
- Precision: 0.9013
- F1: 0.9118
- Accuracy: 0.9820
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: 8e-05
- train_batch_size: 32
- eval_batch_size: 160
- seed: 75241309
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 6000
Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1305 | 0.31 | 600 | 0.1169 | 0.8550 | 0.8139 | 0.8340 | 0.9681 |
| 0.118 | 0.63 | 1200 | 0.0925 | 0.8769 | 0.8592 | 0.8680 | 0.9750 |
| 0.0937 | 0.94 | 1800 | 0.0874 | 0.8939 | 0.8609 | 0.8771 | 0.9764 |
| 0.0698 | 1.25 | 2400 | 0.0821 | 0.9066 | 0.8775 | 0.8918 | 0.9784 |
| 0.0663 | 1.56 | 3000 | 0.0827 | 0.9124 | 0.8764 | 0.8940 | 0.9789 |
| 0.0624 | 1.88 | 3600 | 0.0732 | 0.9179 | 0.8868 | 0.9021 | 0.9804 |
| 0.0364 | 2.19 | 4200 | 0.0750 | 0.9204 | 0.8968 | 0.9085 | 0.9816 |
| 0.0429 | 2.5 | 4800 | 0.0699 | 0.9198 | 0.9031 | 0.9114 | 0.9818 |
| 0.0323 | 2.82 | 5400 | 0.0697 | 0.9227 | 0.9008 | 0.9116 | 0.9819 |
| 0.0334 | 3.13 | 6000 | 0.0695 | 0.9227 | 0.9013 | 0.9118 | 0.9820 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0