metadata
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: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5422163588390502
- name: Recall
type: recall
value: 0.3809082483781279
- name: F1
type: f1
value: 0.4474686989657049
- name: Accuracy
type: accuracy
value: 0.9475506540138497
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: 0.2964
- Precision: 0.5422
- Recall: 0.3809
- F1: 0.4475
- Accuracy: 0.9476
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 | 0.2617 | 0.5380 | 0.3086 | 0.3922 | 0.9427 |
0.1895 | 2.0 | 850 | 0.2944 | 0.5930 | 0.3160 | 0.4123 | 0.9443 |
0.0702 | 3.0 | 1275 | 0.2964 | 0.5422 | 0.3809 | 0.4475 | 0.9476 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1