
# Movie Review Sentiment Analysis with KT-AI/midm-bitext-S-7B-inst-v1

## Experiments

The model is a fine-tuned version of the Language Model KT-AI/midm-bitext-S-7B-inst-v1. Its primary purpose is to analyze movie review sentences and determine whether they are positive or negative in sentiment.

The dataset used for training comprises 2,000 samples from the "nsmc" dataset.

## Evaluation

The model was evaluated on a dataset consisting of 1,000 examples from the "nsmc" dataset. Each example includes a movie review sentence and its corresponding sentiment label as a reference.

The evaluation results are as follows:

- True Positives (TP): 448
- True Negatives (TN): 448
- False Positives (FP): 33
- False Negatives (FN): 47

The model's performance metrics are as follows:

- Accuracy: 89.6%
- Precision: 93.1%
- Recall: 90.5%

{"language": ["ko"], "datasets": ["nsmc"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
safetensors text-classification ko dataset:nsmc region:us