metadata
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large_ner_wnut_17
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.7345505617977528
- name: Recall
type: recall
value: 0.6255980861244019
- name: F1
type: f1
value: 0.6757105943152455
- name: Accuracy
type: accuracy
value: 0.9650416322379711
roberta-large_ner_wnut_17
This model is a fine-tuned version of roberta-large on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2288
- Precision: 0.7346
- Recall: 0.6256
- F1: 0.6757
- Accuracy: 0.9650
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.1805 | 0.6403 | 0.6089 | 0.6242 | 0.9598 |
| No log | 2.0 | 426 | 0.1925 | 0.7314 | 0.5993 | 0.6588 | 0.9624 |
| 0.1192 | 3.0 | 639 | 0.1883 | 0.7088 | 0.6172 | 0.6598 | 0.9637 |
| 0.1192 | 4.0 | 852 | 0.2144 | 0.7289 | 0.6400 | 0.6815 | 0.9655 |
| 0.0301 | 5.0 | 1065 | 0.2288 | 0.7346 | 0.6256 | 0.6757 | 0.9650 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1