|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- indian_names |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: my_awesome_wnut_model |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: indian_names |
|
type: indian_names |
|
config: indian_names |
|
split: train |
|
args: indian_names |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.980007544322897 |
|
- name: Recall |
|
type: recall |
|
value: 0.979145728643216 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9795764469301829 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9962591162591162 |
|
--- |
|
|
|
<!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indian_names dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0148 |
|
- Precision: 0.9800 |
|
- Recall: 0.9791 |
|
- F1: 0.9796 |
|
- Accuracy: 0.9963 |
|
|
|
## 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: 5e-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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 66 | 0.1644 | 0.1903 | 0.1692 | 0.1791 | 0.8817 | |
|
| No log | 2.0 | 132 | 0.1154 | 0.9760 | 0.8181 | 0.8901 | 0.9703 | |
|
| No log | 3.0 | 198 | 0.0921 | 0.9755 | 0.9046 | 0.9387 | 0.9795 | |
|
| No log | 4.0 | 264 | 0.0586 | 0.9616 | 0.9193 | 0.9400 | 0.9849 | |
|
| No log | 5.0 | 330 | 0.0465 | 0.9588 | 0.9219 | 0.9400 | 0.9861 | |
|
| No log | 6.0 | 396 | 0.0346 | 0.9359 | 0.9460 | 0.9409 | 0.9902 | |
|
| No log | 7.0 | 462 | 0.0227 | 0.9708 | 0.9678 | 0.9693 | 0.9941 | |
|
| 0.1068 | 8.0 | 528 | 0.0199 | 0.9753 | 0.9734 | 0.9743 | 0.9946 | |
|
| 0.1068 | 9.0 | 594 | 0.0155 | 0.9801 | 0.9784 | 0.9793 | 0.9961 | |
|
| 0.1068 | 10.0 | 660 | 0.0148 | 0.9800 | 0.9791 | 0.9796 | 0.9963 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.1 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
