|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2003 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: albert-large-v2_ner_conll2003 |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2003 |
|
type: conll2003 |
|
args: conll2003 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9396018069265518 |
|
- name: Recall |
|
type: recall |
|
value: 0.9451363177381353 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9423609363201612 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9874810170943499 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# albert-large-v2_ner_conll2003 |
|
|
|
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the conll2003 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0584 |
|
- Precision: 0.9396 |
|
- Recall: 0.9451 |
|
- F1: 0.9424 |
|
- Accuracy: 0.9875 |
|
|
|
## 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: cosine |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2034 | 1.0 | 878 | 0.0653 | 0.9114 | 0.9278 | 0.9195 | 0.9837 | |
|
| 0.0561 | 2.0 | 1756 | 0.0602 | 0.9316 | 0.9280 | 0.9298 | 0.9845 | |
|
| 0.0303 | 3.0 | 2634 | 0.0536 | 0.9380 | 0.9424 | 0.9402 | 0.9872 | |
|
| 0.0177 | 4.0 | 3512 | 0.0535 | 0.9393 | 0.9456 | 0.9425 | 0.9877 | |
|
| 0.011 | 5.0 | 4390 | 0.0584 | 0.9396 | 0.9451 | 0.9424 | 0.9875 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
