metadata
license: mit
base_model: dslim/bert-large-NER
tags:
- generated_from_trainer
datasets:
- job-titles
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_ner_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: job-titles
type: job-titles
config: job-titles
split: test
args: job-titles
metrics:
- name: Precision
type: precision
value: 0.9863945578231292
- name: Recall
type: recall
value: 0.9954233409610984
- name: F1
type: f1
value: 0.9908883826879271
- name: Accuracy
type: accuracy
value: 0.9953216374269006
my_awesome_ner_model
This model is a fine-tuned version of dslim/bert-large-NER on the job-titles dataset. It achieves the following results on the evaluation set:
- Loss: 0.0080
- Precision: 0.9864
- Recall: 0.9954
- F1: 0.9909
- Accuracy: 0.9953
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 18 | 0.0232 | 0.9864 | 0.9954 | 0.9909 | 0.9953 |
| No log | 2.0 | 36 | 0.0080 | 0.9864 | 0.9954 | 0.9909 | 0.9953 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1