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run.sh
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#!/bin/bash
pip install -e .
# for MODEL_NAME in "mistral-nemo" "llama3.1" "gemma2" "phi3" "llama3.2" "qwen2" "mixtral"
# do
for NUM_EXAMPLES in 0 10 20 30 40 50
do
for task_id in 1 2 3 4 5 6 7 8
do
BASE_DIR="/data/bodyct/experiments/luc_t10162/GitHub/LLM_data_extractor"
GROUND_TRUTH_PATH="/data/bodyct/experiments/luc_t10162/DRAGON/debug-test-set"
MODEL_NAME="mistral-nemo"
# NUM_EXAMPLES=0
RUN_NAME="binary_clf_examples_test/${MODEL_NAME}/${NUM_EXAMPLES}_examples"
PREDICTION_PATH="${BASE_DIR}/output/${RUN_NAME}"
OUTPUT_PATH="${PREDICTION_PATH}/metrics.json"
# extract_data \
# --task_id 25 \
# --model_name $MODEL_NAME \
# --num_examples $NUM_EXAMPLES \
# --run_name $RUN_NAME \
# --max_context_len 8192
# post_process \
# --output_path $PREDICTION_PATH \
# --task_ids 25
# evaluate \
# --task_ids 4 \
# --prediction_path $PREDICTION_PATH \
# --ground_truth_path $GROUND_TRUTH_PATH \
# --output_path $OUTPUT_PATH
extract_data \
--task_id $task_id \
--model_name $MODEL_NAME \
--num_examples $NUM_EXAMPLES \
--run_name $RUN_NAME \
--max_context_len 100000 \
--num_predict 1024
done
post_process \
--output_path $PREDICTION_PATH \
--task_ids 1 2 3 4 5 6 7 8
evaluate \
--task_ids 1 2 3 4 5 6 7 8 \
--prediction_path $PREDICTION_PATH \
--ground_truth_path $GROUND_TRUTH_PATH \
--output_path $OUTPUT_PATH
done
# for task_id in {25..28}
# do
# extract_data \
# --task_id $task_id \
# --model_name $MODEL_NAME \
# --num_examples $NUM_EXAMPLES \
# --run_name $RUN_NAME \
# --num_predict 8192 \
# --max_context_len 8192
# done
# done
# post_process \
# --output_path $PREDICTION_PATH \
# --task_ids 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
# evaluate \
# --task_ids 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 \
# --prediction_path $PREDICTION_PATH \
# --ground_truth_path $GROUND_TRUTH_PATH \
# --output_path $OUTPUT_PATH