metadata
license: mit
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large_ner_wikiann
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: en
metrics:
- name: Precision
type: precision
value: 0.8462551098177787
- name: Recall
type: recall
value: 0.8634242895518167
- name: F1
type: f1
value: 0.8547534903250638
- name: Accuracy
type: accuracy
value: 0.9382388000397338
roberta-large_ner_wikiann
This model is a fine-tuned version of roberta-large on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.2783
- Precision: 0.8463
- Recall: 0.8634
- F1: 0.8548
- Accuracy: 0.9382
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: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3395 | 1.0 | 1250 | 0.2652 | 0.8039 | 0.8308 | 0.8171 | 0.9242 |
| 0.2343 | 2.0 | 2500 | 0.2431 | 0.8354 | 0.8503 | 0.8428 | 0.9329 |
| 0.1721 | 3.0 | 3750 | 0.2315 | 0.8330 | 0.8503 | 0.8416 | 0.9352 |
| 0.1156 | 4.0 | 5000 | 0.2709 | 0.8477 | 0.8634 | 0.8554 | 0.9385 |
| 0.1026 | 5.0 | 6250 | 0.2783 | 0.8463 | 0.8634 | 0.8548 | 0.9382 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1