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updating prompts for categorization trial

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mgaughan 2025-05-20 23:12:11 -05:00
parent 7aedc1edbb
commit 17c69a6c92
3 changed files with 29 additions and 55 deletions
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11
models/p1-categorization.py
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@ -7,13 +7,14 @@ olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B-Instruct").t
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B-Instruct")
#priming prompt
first_prompt = "You are a multi-category classifer model. You are tasked with applying qualitative codes to title-abstract pairs of academic studies. We define the following study characteristics below:"
characteristics_prompt = "1. English language empirical studies: academic papers written in Egnlish that study or analyze evidence. Literature reviews are not empirical studies. 2. Focus on FOSS projects: is the focus of the research work on the domain of free and open source software projects. 3. Study FOSS project evolution: is the focus of the research work on longitudinal changes to free and open source projects. 4. Study FOSS project adaptation: is the focus of the research work on intentional changes made by free and open source software projects to better align themselves with their broader environment."
formatting_prompt = "For each code that we have specified, provide a binary YES or NO classification depending on whether or not the code applies to the title-abstract pair. Responses shouldonly include YES or NO responses to each characteristic's inclusion and should be formatted as [characteristic number]:[classification] for ALL four study characteristics that we have defined. Here is the title-abstract pair: "
first_sentence = "Given the following data:"
data_prompt = "Title - Underproduction: An Approach for Measuring Risk in Open Source Software \n Abstract - The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call 'underproduction' which occurs when the supply of software engineering labor becomes out of alignment with the demand of people who rely on the software produced. We present a conceptual framework for identifying relative underproduction in software as well as a statistical method for applying our framework to a comprehensive dataset from the Debian GNU/Linux distribution that includes 21,902 source packages and the full history of 461,656 bugs. We draw on this application to present two experiments: (1) a demonstration of how our technique can be used to identify at-risk software packages in a large FLOSS repository and (2) a validation of these results using an alternate indicator of package risk. Our analysis demonstrates both the utility of our approach and reveals the existence of widespread underproduction in a range of widely-installed software components in Debian. "
data_prompt = "'Title - Underproduction: An Approach for Measuring Risk in Open Source Software \n Abstract - The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call 'underproduction' which occurs when the supply of software engineering labor becomes out of alignment with the demand of people who rely on the software produced. We present a conceptual framework for identifying relative underproduction in software as well as a statistical method for applying our framework to a comprehensive dataset from the Debian GNU/Linux distribution that includes 21,902 source packages and the full history of 461,656 bugs. We draw on this application to present two experiments: (1) a demonstration of how our technique can be used to identify at-risk software packages in a large FLOSS repository and (2) a validation of these results using an alternate indicator of package risk. Our analysis demonstrates both the utility of our approach and reveals the existence of widespread underproduction in a range of widely-installed software components in Debian.'"
prompt = f"{first_prompt}\n{characteristics_prompt}\n{formatting_prompt}\n{data_prompt}"
third_prompt="please categorize it based on the following numbered characteristics: \n\n 1: YES/NO (Characteristic 1. This is an English language empirical study, this an academic papers written in Egnlish that studies or analyzes evidence. Literature reviews are not empirical studies.) \n 2: YES/NO (Characteristic 2. This focuses on FOSS projects, the focus of the research work is on the domain of free and open source software projects.) \n 3: YES/NO (Characteristic 3. This studies FOSS evolution, the data focuses on longitudinal changes to free and open source projects over time.) \n 4: YES/NO (Characteristic 4. This studies FOSS adaptation, the data focuses on intentional changes made by free and open source software projects to better align themselves with their broader environment.) \n\n Only respond with the appropriate number followed by 'YES' if the characteristic is present in the provided data or 'NO' if it is not (e.g. '1: NO; 2: YES;'. Do not provide any additional information."
prompt = f"{first_sentence}\n{data_prompt}\n{third_prompt}"
inputs = tokenizer(prompt, return_tensors='pt', return_token_type_ids=False).to(device)

23
models/p2-ocr.py
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@ -0,0 +1,23 @@
# https://huggingface.co/allenai/olmOCR-7B-0225-preview
import torch
import base64
from io import BytesIO
from PIL import image
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from olmocr.data.renderpdf import render_pdf_to_base64png
from olmocr.prompts import build_finetuning_prompt
from olmocr.prompts.anchor import get_anchor_text
# Initialize the model
model = Qwen2VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-0225-preview", torch_dtype=torch.bfloat16).eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
#for all pages in a pdf
for
#

50
p1-categorization.log
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@ -1,50 +0,0 @@
setting up the environment
running the p1 categorization script
Fetching 14 files: 0%| | 0/14 [00:00<?, ?it/s] Fetching 14 files: 7%|▋ | 1/14 [07:03<1:31:40, 423.09s/it] Fetching 14 files: 14%|█▍ | 2/14 [07:41<39:23, 196.99s/it] Fetching 14 files: 43%|████▎ | 6/14 [07:53<06:19, 47.47s/it] Fetching 14 files: 57%|█████▋ | 8/14 [08:18<03:29, 34.93s/it] Fetching 14 files: 79%|███████▊ | 11/14 [08:36<01:07, 22.47s/it] Fetching 14 files: 79%|███████▊ | 11/14 [08:42<02:22, 47.47s/it]
Traceback (most recent call last):
File "/home/nws8519/git/adaptation-slr/models/p1-categorization.py", line 6, in <module>
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0325-32B-Instruct").to(device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 571, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/modeling_utils.py", line 309, in _wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4420, in from_pretrained
checkpoint_files, sharded_metadata = _get_resolved_checkpoint_files(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/modeling_utils.py", line 1178, in _get_resolved_checkpoint_files
checkpoint_files, sharded_metadata = get_checkpoint_shard_files(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/utils/hub.py", line 1110, in get_checkpoint_shard_files
cached_filenames = cached_files(
^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/utils/hub.py", line 557, in cached_files
raise e
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/transformers/utils/hub.py", line 485, in cached_files
snapshot_download(
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/huggingface_hub/_snapshot_download.py", line 297, in snapshot_download
thread_map(
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 69, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 51, in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/site-packages/tqdm/std.py", line 1181, in __iter__
for obj in iterable:
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
yield _result_or_cancel(fs.pop())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
return fut.result(timeout)
^^^^^^^^^^^^^^^^^^^
File "/home/nws8519/.conda/envs/olmo/lib/python3.11/concurrent/futures/_base.py", line 456, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
object address : 0x14ebbe9730c0