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: 0.656957928802589
- name: Recall
type: recall
value: 0.48564593301435405
- name: F1
type: f1
value: 0.5584594222833563
- name: Accuracy
type: accuracy
value: 0.933951453276383
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: 0.3577
- Precision: 0.6570
- Recall: 0.4856
- F1: 0.5585
- Accuracy: 0.9340
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 | 0.3191 | 0.6465 | 0.4091 | 0.5011 | 0.9273 |
| 0.1595 | 2.0 | 850 | 0.3320 | 0.6719 | 0.4629 | 0.5482 | 0.9316 |
| 0.0681 | 3.0 | 1275 | 0.3577 | 0.6570 | 0.4856 | 0.5585 | 0.9340 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3