metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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: 0.5613275613275613
- name: Recall
type: recall
value: 0.465311004784689
- name: F1
type: f1
value: 0.5088293001962066
- name: Accuracy
type: accuracy
value: 0.9229328338239229
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3765
- Precision: 0.5613
- Recall: 0.4653
- F1: 0.5088
- Accuracy: 0.9229
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 425 | 0.3759 | 0.6258 | 0.3600 | 0.4571 | 0.9145 |
| 0.1932 | 2.0 | 850 | 0.3226 | 0.5608 | 0.4522 | 0.5007 | 0.9237 |
| 0.0778 | 3.0 | 1275 | 0.3765 | 0.5613 | 0.4653 | 0.5088 | 0.9229 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3