metadata
license: mit
tags:
- generated_from_trainer
datasets:
- plo_dunfiltered_config
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: plo_dunfiltered_config
type: plo_dunfiltered_config
args: PLODunfiltered
metrics:
- name: Precision
type: precision
value: 0.9662545190541101
- name: Recall
type: recall
value: 0.9627013733169376
- name: F1
type: f1
value: 0.9644746737300262
- name: Accuracy
type: accuracy
value: 0.9607518572002093
roberta-large-finetuned-ner
This model is a fine-tuned version of roberta-large on the plo_dunfiltered_config dataset. It achieves the following results on the evaluation set:
- Loss: 0.1393
- Precision: 0.9663
- Recall: 0.9627
- F1: 0.9645
- Accuracy: 0.9608
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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1281 | 1.0 | 14233 | 0.1300 | 0.9557 | 0.9436 | 0.9496 | 0.9457 |
| 0.1056 | 2.0 | 28466 | 0.1076 | 0.9620 | 0.9552 | 0.9586 | 0.9545 |
| 0.0904 | 3.0 | 42699 | 0.1054 | 0.9655 | 0.9585 | 0.9620 | 0.9583 |
| 0.0743 | 4.0 | 56932 | 0.1145 | 0.9658 | 0.9602 | 0.9630 | 0.9593 |
| 0.0523 | 5.0 | 71165 | 0.1206 | 0.9664 | 0.9619 | 0.9641 | 0.9604 |
| 0.044 | 6.0 | 85398 | 0.1393 | 0.9663 | 0.9627 | 0.9645 | 0.9608 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1