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---
datasets:
- e9t/nsmc
language:
- ko
metrics:
- accuracy
pipeline_tag: text-classification
---
## Model Description
- **Finetuned from model klue/bert :** (https://huggingface.co/klue/bert-base)
- i got **test_accuracy: 0.9041**
## Uses
- use to sentimental analysis task
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("seongyeon1/klue-base-finetuned-nsmc")
model = AutoModelForSequenceClassification.from_pretrained("seongyeon1/klue-base-finetuned-nsmc")
```
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="seongyeon1/klue-base-finetuned-nsmc")
pipe("진짜 별로더라") # [{'label': 'LABEL_0', 'score': 0.999700665473938}]
pipe("굿굿") # [{'label': 'LABEL_1', 'score': 0.9875587224960327}]
```
## Training Details
### Training Data
- nsmc datasets (https://huggingface.co/datasets/e9t/nsmc)
```python
from datasets import load_dataset
dataset = load_dataset('nsmc')
```
#### Preprocessing
- bert's default is 512, but it costs a lot of time.
- maxlen = 55
```python
def tokenize_function_with_max(examples, maxlen=maxlen):
encodings = tokenizer(examples['document'],max_length=maxlen, truncation=True, padding='max_length')
return encodings
```
#### Training Hyperparameters
- learning rate=2e-5, weight decay=0.01, batch size=32, epochs=2
#### Metrics
- **accuracy**
- label ratio is about almost balanced
#### Result
{'eval_loss': 0.2575262784957886,
'eval_accuracy': 0.9041,
'eval_runtime': 163.2129,
'eval_samples_per_second': 306.348,
'eval_steps_per_second': 9.576,
'epoch': 2.0}
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