# Evaluation

## Model performance in sentence similarity

1. Download SentEval & test data
```bash
git clone https://github.com/facebookresearch/SentEval.git
cd SentEval/data/downstream
./get_transfer_data.bash
```

2. Run test script
```bash
python evaluate.py MODEL_NAME
```

## QPS Test

Please note that `qps_test.py` uses:
- `localhost:8000`: to connect triton client
- `'Hello, world.''`: as test sentence

```bash
python qps_test --model paraphrase-albert-small-v2 --pipe --onnx --triton --num 100
```

**Args:**
- `--model`: mandatory, string, model name
- `--pipe`: optional, on/off flag to enable qps test for pipe
- `--onnx`: optional, on/off flag to enable qps test for onnx
- `--triton`: optional, on/off flag to enable qps for triton (please make sure that triton client is ready)
- `--num`: optional, integer, defaults to 100, batch size in each loop (10 loops in total)
- `--device`: optional, int, defaults to -1, cuda index or use cpu when -1