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.PHONY: clean metrics persuasion behaviour personalisation entity_recognition target_agnostic targeted
# Set the index name
INDEX = SET INDEX NAME HERE
# Using single GPU (device 0) for sequential processing
CUDA_DEVICE = 0
# Model selection: set to 'all' to run all models, or specify a model name
# Available models: llama, gemma, vicuna, qwen, mistral, grok, chatgpt, claude
MODEL = all
# Model name mappings (short name -> full model path)
LLAMA_MODEL = meta-llama/Meta-Llama-3-8B-Instruct
GEMMA_MODEL = google/gemma-2-9b-it
VICUNA_MODEL = lmsys/vicuna-7b-v1.5
QWEN_MODEL = Qwen/Qwen2.5-7B-Instruct
MISTRAL_MODEL = mistralai/Mistral-Nemo-Instruct-2407
GROK_MODEL = grok-2
CHATGPT_MODEL = openai/gpt-4o
CLAUDE_MODEL = claude-3-5-sonnet-20241022
# List of all models for iteration
ALL_MODELS = llama gemma vicuna qwen mistral grok chatgpt claude
target_agnostic:
@if [ "$(MODEL)" = "all" ]; then \
python3 generate_llm_outputs.py $(LLAMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(GEMMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(VICUNA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(QWEN_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(MISTRAL_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(GROK_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
python3 generate_llm_outputs.py $(CHATGPT_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) && python3 batch_apis/process_batch.py "$(CHATGPT_MODEL)" $(INDEX) --batch_size=6000; \
python3 generate_llm_outputs.py $(CLAUDE_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) && python3 batch_apis/process_batch.py "$(CLAUDE_MODEL)" $(INDEX) --batch_size=6000; \
elif [ "$(MODEL)" = "llama" ]; then \
python3 generate_llm_outputs.py $(LLAMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "gemma" ]; then \
python3 generate_llm_outputs.py $(GEMMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "vicuna" ]; then \
python3 generate_llm_outputs.py $(VICUNA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "qwen" ]; then \
python3 generate_llm_outputs.py $(QWEN_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "mistral" ]; then \
python3 generate_llm_outputs.py $(MISTRAL_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "grok" ]; then \
python3 generate_llm_outputs.py $(GROK_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX); \
elif [ "$(MODEL)" = "chatgpt" ]; then \
python3 generate_llm_outputs.py $(CHATGPT_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) && python3 batch_apis/process_batch.py "$(CHATGPT_MODEL)" $(INDEX) --batch_size=6000; \
elif [ "$(MODEL)" = "claude" ]; then \
python3 generate_llm_outputs.py $(CLAUDE_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) && python3 batch_apis/process_batch.py "$(CLAUDE_MODEL)" $(INDEX) --batch_size=6000; \
else \
echo "Error: Unknown model '$(MODEL)'. Available models: $(ALL_MODELS) or 'all'"; \
exit 1; \
fi
targeted:
@if [ "$(MODEL)" = "all" ]; then \
python3 generate_llm_outputs.py $(LLAMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(GEMMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(VICUNA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(QWEN_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(MISTRAL_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(GROK_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
python3 generate_llm_outputs.py $(CHATGPT_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise && python3 batch_apis/process_batch.py "$(CHATGPT_MODEL)" $(INDEX) --batch_size=6000; \
python3 generate_llm_outputs.py $(CLAUDE_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise && python3 batch_apis/process_batch.py "$(CLAUDE_MODEL)" $(INDEX) --batch_size=6000; \
elif [ "$(MODEL)" = "llama" ]; then \
python3 generate_llm_outputs.py $(LLAMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "gemma" ]; then \
python3 generate_llm_outputs.py $(GEMMA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "vicuna" ]; then \
python3 generate_llm_outputs.py $(VICUNA_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "qwen" ]; then \
python3 generate_llm_outputs.py $(QWEN_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "mistral" ]; then \
python3 generate_llm_outputs.py $(MISTRAL_MODEL) --cuda_device=$(CUDA_DEVICE) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "grok" ]; then \
python3 generate_llm_outputs.py $(GROK_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise; \
elif [ "$(MODEL)" = "chatgpt" ]; then \
python3 generate_llm_outputs.py $(CHATGPT_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise && python3 batch_apis/process_batch.py "$(CHATGPT_MODEL)" $(INDEX) --batch_size=6000; \
elif [ "$(MODEL)" = "claude" ]; then \
python3 generate_llm_outputs.py $(CLAUDE_MODEL) --batch_size 100 --index_name $(INDEX) --skip-duplicates-in $(INDEX) --personalise && python3 batch_apis/process_batch.py "$(CLAUDE_MODEL)" $(INDEX) --batch_size=6000; \
else \
echo "Error: Unknown model '$(MODEL)'. Available models: $(ALL_MODELS) or 'all'"; \
exit 1; \
fi
clean:
python3 clean/clean.py \
--index-name $(INDEX) \
--batch-size 50
metrics:
@if [ "$(MODEL)" = "all" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(CHATGPT_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(VICUNA_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(GROK_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(QWEN_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(GEMMA_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(CLAUDE_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(LLAMA_MODEL)" --batch-size 200; \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(MISTRAL_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "llama" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(LLAMA_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "gemma" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(GEMMA_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "vicuna" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(VICUNA_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "qwen" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(QWEN_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "mistral" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(MISTRAL_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "grok" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(GROK_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "chatgpt" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(CHATGPT_MODEL)" --batch-size 200; \
elif [ "$(MODEL)" = "claude" ]; then \
python3 automated_metrics.py --index-name $(INDEX) --model-name "$(CLAUDE_MODEL)" --batch-size 200; \
else \
echo "Error: Unknown model '$(MODEL)'. Available models: $(ALL_MODELS) or 'all'"; \
exit 1; \
fi
persuasion:
python3 run_elg_classifiers.py \
--index-name $(INDEX) \
--run-persuasion \
--run-topic \
--input_field "text" \
--skip-duplicates
behaviour:
python3 llm_processing/generic_pipeline.py \
--index-name $(INDEX) \
--task model_behaviour \
--model-name "google/gemma-2-9b-it" \
--device $(CUDA_DEVICE) \
--batch-size 100
personalisation:
python3 llm_processing/generic_pipeline.py \
--index-name $(INDEX) \
--task personalisation.country \
--model-name "Qwen/Qwen2.5-7B-Instruct" \
--device $(CUDA_DEVICE) \
--batch-size 100 \
--filter '{"must": [{"exists": {"field": "persona.hash"}}]}'
python3 llm_processing/generic_pipeline.py \
--index-name $(INDEX) \
--task personalisation.generation \
--model-name "Qwen/Qwen2.5-7B-Instruct" \
--device $(CUDA_DEVICE) \
--batch-size 100 \
--filter '{"must": [{"exists": {"field": "persona.hash"}}]}'
python3 llm_processing/generic_pipeline.py \
--index-name $(INDEX) \
--task personalisation.political_orientation \
--model-name "Qwen/Qwen2.5-7B-Instruct" \
--device $(CUDA_DEVICE) \
--batch-size 100 \
--filter '{"must": [{"exists": {"field": "persona.hash"}}]}'
entity_recognition:
python3 entity_recognition/entity_recognition_elastic.py \
--index-name $(INDEX) \
--batch-size 100 \
--filter '{ "must": [{"term": {"output_language.keyword": "pt"}}]}' \
--skip-duplicates
python3 entity_recognition/entity_recognition_elastic.py \
--index-name $(INDEX) \
--batch-size 100 \
--filter '{ "must": [{"term": {"output_language.keyword": "en"}}]}' \
--skip-duplicates
python3 entity_recognition/entity_recognition_elastic.py \
--index-name $(INDEX) \
--batch-size 100 \
--filter '{ "must": [{"term": {"output_language.keyword": "ru"}}]}' \
--skip-duplicates
python3 entity_recognition/entity_recognition_elastic.py \
--index-name $(INDEX) \
--batch-size 100 \
--filter '{ "must": [{"term": {"output_language.keyword": "hi"}}]}' \
--skip-duplicates
build_dataset:
python3 pull_from_elastic/pull_from_elastic.py \
--index_names $(INDEX) \
--model llama \
--outdir data/final \
--skip_duplicates