BahAdoR0101's picture
End of training
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---
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_wnut_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: 0.9992003198720512
- name: Recall
type: recall
value: 0.9996
- name: F1
type: f1
value: 0.9994001199760049
- name: Accuracy
type: accuracy
value: 0.6346958244661334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on the job-titles dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6603
- Precision: 0.9992
- Recall: 0.9996
- F1: 0.9994
- Accuracy: 0.6347
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6666 | 1.0 | 4587 | 0.6615 | 1.0 | 1.0 | 1.0 | 0.6331 |
| 0.6617 | 2.0 | 9174 | 0.6603 | 0.9992 | 0.9996 | 0.9994 | 0.6347 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1