|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- ner |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: my_awesome_wnut_model |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: ner |
|
type: ner |
|
config: indian_names |
|
split: train |
|
args: indian_names |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9994470908472269 |
|
- name: Recall |
|
type: recall |
|
value: 0.9994045846978268 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9994258373205741 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9998240191819092 |
|
--- |
|
|
|
<!-- 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 ner dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0015 |
|
- Precision: 0.9994 |
|
- Recall: 0.9994 |
|
- F1: 0.9994 |
|
- Accuracy: 0.9998 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1477 | 1.0 | 626 | 0.0548 | 0.9686 | 0.9604 | 0.9644 | 0.9887 | |
|
| 0.0571 | 2.0 | 1252 | 0.0249 | 0.9833 | 0.9820 | 0.9827 | 0.9949 | |
|
| 0.037 | 3.0 | 1878 | 0.0075 | 0.9962 | 0.9953 | 0.9957 | 0.9987 | |
|
| 0.0101 | 4.0 | 2504 | 0.0027 | 0.9987 | 0.9984 | 0.9986 | 0.9996 | |
|
| 0.004 | 5.0 | 3130 | 0.0015 | 0.9994 | 0.9994 | 0.9994 | 0.9998 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.1 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
