|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2003 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: distilbert-base-uncased-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2003 |
|
type: conll2003 |
|
args: conll2003 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9264494754279403 |
|
- name: Recall |
|
type: recall |
|
value: 0.9384718648618414 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9324219184172502 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9840659602522758 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# distilbert-base-uncased-finetuned-ner |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0613 |
|
- Precision: 0.9264 |
|
- Recall: 0.9385 |
|
- F1: 0.9324 |
|
- Accuracy: 0.9841 |
|
|
|
## 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: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2389 | 1.0 | 878 | 0.0696 | 0.9117 | 0.9198 | 0.9157 | 0.9812 | |
|
| 0.0518 | 2.0 | 1756 | 0.0615 | 0.9215 | 0.9367 | 0.9290 | 0.9837 | |
|
| 0.0308 | 3.0 | 2634 | 0.0613 | 0.9264 | 0.9385 | 0.9324 | 0.9841 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.11.3 |
|
- Pytorch 1.9.0+cu111 |
|
- Datasets 1.12.1 |
|
- Tokenizers 0.10.3 |
|
|
