Update README.md

README.md

@@ -6,4 +6,95 @@ language:
 metrics:
 - accuracy
 pipeline_tag: text-classification
----
+---
+# Model Card for Model ID
+
+
+## Model Details
+
+{'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}
+
+
+### Model Description
+
+- **Finetuned from model klue/bert :** [More Information Needed]
+- 
+
+## 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
+
+
+#### Preprocessing
+
+- bert's default is 512, but it costs a lot of time.
+ - maxlen = 55
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/634330a304d4ff28aeb8de56/t7axSlo4JI4bPLynUB3OP.png)
+
+```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
+
+#### Speeds, Sizes, Times [optional]
+
+- about 40 minutes
+
+
+[More Information Needed]
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+- nsmc test datasets
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+- accuracy
+- label ratio is about almost balanced