README.md

metadata

dataset_info:
  features:
    - name: query_id
      dtype: string
    - name: corpus_id
      dtype: string
    - name: score
      dtype: int64
  splits:
    - name: train
      num_bytes: 675736
      num_examples: 24927
    - name: valid
      num_bytes: 39200
      num_examples: 1400
    - name: test
      num_bytes: 35308
      num_examples: 1261
  download_size: 316849
  dataset_size: 750244
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: valid
        path: data/valid-*
      - split: test
        path: data/test-*

Employing the CoIR evaluation framework's dataset version, utilize the code below for assessment:

import coir
from coir.data_loader import get_tasks
from coir.evaluation import COIR
from coir.models import YourCustomDEModel

model_name = "intfloat/e5-base-v2"

# Load the model
model = YourCustomDEModel(model_name=model_name)

# Get tasks
#all task ["codetrans-dl","stackoverflow-qa","apps","codefeedback-mt","codefeedback-st","codetrans-contest","synthetic-
# text2sql","cosqa","codesearchnet","codesearchnet-ccr"]
tasks = get_tasks(tasks=["codetrans-dl"])

# Initialize evaluation
evaluation = COIR(tasks=tasks,batch_size=128)

# Run evaluation
results = evaluation.run(model, output_folder=f"results/{model_name}")
print(results)