metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fin
type: fin
config: fin
split: validation
args: fin
metrics:
- name: Precision
type: precision
value: 0.9408740359897172
- name: Recall
type: recall
value: 0.9682539682539683
- name: F1
type: f1
value: 0.954367666232073
- name: Accuracy
type: accuracy
value: 0.9930041974815111
roberta-base-finetuned-ner
This model is a fine-tuned version of roberta-base on the fin dataset. It achieves the following results on the evaluation set:
- Loss: 0.0331
- Precision: 0.9409
- Recall: 0.9683
- F1: 0.9544
- Accuracy: 0.9930
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: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 64 | 0.0650 | 0.9457 | 0.9206 | 0.9330 | 0.9884 |
| No log | 2.0 | 128 | 0.0366 | 0.9141 | 0.9577 | 0.9354 | 0.9924 |
| No log | 3.0 | 192 | 0.0331 | 0.9409 | 0.9683 | 0.9544 | 0.9930 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1