|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wikiann |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: albert-large-v2_ner_wikiann |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: wikiann |
|
type: wikiann |
|
args: en |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8239671720684378 |
|
- name: Recall |
|
type: recall |
|
value: 0.8374805598755832 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8306689103912495 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.926951922121784 |
|
--- |
|
|
|
<!-- 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_wikiann |
|
|
|
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the wikiann dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3416 |
|
- Precision: 0.8240 |
|
- Recall: 0.8375 |
|
- F1: 0.8307 |
|
- Accuracy: 0.9270 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.3451 | 1.0 | 2500 | 0.3555 | 0.7745 | 0.7850 | 0.7797 | 0.9067 | |
|
| 0.2995 | 2.0 | 5000 | 0.2927 | 0.7932 | 0.8240 | 0.8083 | 0.9205 | |
|
| 0.252 | 3.0 | 7500 | 0.2936 | 0.8094 | 0.8236 | 0.8164 | 0.9239 | |
|
| 0.1676 | 4.0 | 10000 | 0.3302 | 0.8256 | 0.8359 | 0.8307 | 0.9268 | |
|
| 0.1489 | 5.0 | 12500 | 0.3416 | 0.8240 | 0.8375 | 0.8307 | 0.9270 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
