metadata
license: cc-by-nc-sa-4.0
base_model: Babelscape/wikineural-multilingual-ner
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-Colab
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: validation
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.6477093206951027
- name: Recall
type: recall
value: 0.4904306220095694
- name: F1
type: f1
value: 0.5582028590878148
- name: Accuracy
type: accuracy
value: 0.9344202521095948
bert-finetuned-ner-Colab
This model is a fine-tuned version of Babelscape/wikineural-multilingual-ner on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4102
- Precision: 0.6477
- Recall: 0.4904
- F1: 0.5582
- Accuracy: 0.9344
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: 8
- 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 | 425 | 0.3037 | 0.5963 | 0.5072 | 0.5482 | 0.9321 |
| 0.0672 | 2.0 | 850 | 0.3751 | 0.6604 | 0.4653 | 0.5460 | 0.9316 |
| 0.0451 | 3.0 | 1275 | 0.4102 | 0.6477 | 0.4904 | 0.5582 | 0.9344 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3