diff --git a/README.md b/README.md
index 2c0d1e295e64ea2f775d1237e01bb57d8d22665a..85447c64daa614c400e4f4e795df5dda268a2e21 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,10 @@
-# BachelorThesisNiklasBretz
+# Code zur Bachelorthesis von Niklas Bretz
+Dieses Repository enthält den wichtigsten Code zur Bachelorthesis von Niklas Bretz.
+Die drei Ordner enthalten Code zu Erstellung eines repräsentativen Datensatzes,
+zum Benchmarking von Persönlichkeitsprompts und zur Anwendung der Erklärbarkeitmethoden.
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.reutlingen-university.de/bretz/bachelorthesisniklasbretz.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.reutlingen-university.de/bretz/bachelorthesisniklasbretz/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Für die Ausführung des Codes muss das LLM LLaMA 3.2 3B von Huggingface heruntergeladen werden.
+Dazu sind ein Huggingface-Account sowie eine Verifizierung auf der Huggingface-Webseite nötig
+Der Download erfolgt dann automatisch im Code und umfasst etwa 5 GB.
diff --git a/benchmark/benchmark.ipynb b/benchmark/benchmark.ipynb
index bd384a86f3ed086bc8e9f9da7807e883491dfc45..ca3e16326606dfb7d3889bb2614835fe8ff33250 100644
--- a/benchmark/benchmark.ipynb
+++ b/benchmark/benchmark.ipynb
@@ -3,8 +3,8 @@
{
"metadata": {
"ExecuteTime": {
- "end_time": "2025-01-31T10:57:48.238168Z",
- "start_time": "2025-01-31T10:57:48.004134Z"
+ "end_time": "2025-01-31T11:11:12.365214Z",
+ "start_time": "2025-01-31T11:11:12.317218Z"
}
},
"cell_type": "code",
@@ -30,26 +30,26 @@
],
"id": "9f7c6187bfc81ba6",
"outputs": [],
- "execution_count": 1
+ "execution_count": 11
},
{
"metadata": {
"ExecuteTime": {
- "end_time": "2025-01-31T10:57:49.498468Z",
- "start_time": "2025-01-31T10:57:49.491470Z"
+ "end_time": "2025-01-31T11:11:14.107618Z",
+ "start_time": "2025-01-31T11:11:14.098522Z"
}
},
"cell_type": "code",
"source": "trait_categories = [\"EXT\", \"EST\", \"AGR\", \"CSN\", \"OPN\"]",
"id": "3e451794c0a28fec",
"outputs": [],
- "execution_count": 2
+ "execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
- "end_time": "2025-01-31T10:57:59.597074Z",
- "start_time": "2025-01-31T10:57:50.704216Z"
+ "end_time": "2025-01-31T11:11:16.998450Z",
+ "start_time": "2025-01-31T11:11:15.329200Z"
}
},
"cell_type": "code",
@@ -92,25 +92,27 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
+ "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
+ "C:\\Users\\qwert\\PycharmProjects\\BachelorArbeit\\.venv\\lib\\site-packages\\accelerate\\utils\\modeling.py:1390: UserWarning: Current model requires 1856 bytes of buffer for offloaded layers, which seems does not fit any GPU's remaining memory. If you are experiencing a OOM later, please consider using offload_buffers=True.\n",
+ " warnings.warn(\n"
]
},
{
- "data": {
- "text/plain": [
- "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "da0a3a61f84d439a800dba81215abff4"
- }
- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "ValueError",
+ "evalue": "Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details. ",
+ "output_type": "error",
+ "traceback": [
+ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+ "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
+ "Cell \u001B[1;32mIn[13], line 11\u001B[0m\n\u001B[0;32m 9\u001B[0m tokenizer \u001B[38;5;241m=\u001B[39m AutoTokenizer\u001B[38;5;241m.\u001B[39mfrom_pretrained(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmeta-llama/Llama-3.2-3B-Instruct\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 10\u001B[0m tokenizer\u001B[38;5;241m.\u001B[39mpad_token \u001B[38;5;241m=\u001B[39m tokenizer\u001B[38;5;241m.\u001B[39meos_token\n\u001B[1;32m---> 11\u001B[0m model \u001B[38;5;241m=\u001B[39m \u001B[43mAutoModelForCausalLM\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_pretrained\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 12\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmeta-llama/Llama-3.2-3B-Instruct\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 13\u001B[0m \u001B[43m \u001B[49m\u001B[43mload_in_8bit\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# This loads the model in 8-bit precision\u001B[39;49;00m\n\u001B[0;32m 14\u001B[0m \u001B[43m \u001B[49m\u001B[43mdevice_map\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mauto\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# Automatically assigns the model to available devices\u001B[39;49;00m\n\u001B[0;32m 15\u001B[0m \u001B[43m)\u001B[49m\n\u001B[0;32m 17\u001B[0m custom_pipeline \u001B[38;5;241m=\u001B[39m pipeline(\n\u001B[0;32m 18\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtext-generation\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 19\u001B[0m model\u001B[38;5;241m=\u001B[39mmodel,\n\u001B[0;32m 20\u001B[0m tokenizer\u001B[38;5;241m=\u001B[39mtokenizer,\n\u001B[0;32m 21\u001B[0m )\n\u001B[0;32m 22\u001B[0m terminators \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m 23\u001B[0m custom_pipeline\u001B[38;5;241m.\u001B[39mtokenizer\u001B[38;5;241m.\u001B[39meos_token_id,\n\u001B[0;32m 24\u001B[0m custom_pipeline\u001B[38;5;241m.\u001B[39mtokenizer\u001B[38;5;241m.\u001B[39mconvert_tokens_to_ids(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[0;32m 25\u001B[0m ]\n",
+ "File \u001B[1;32m~\\PycharmProjects\\BachelorArbeit\\.venv\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:564\u001B[0m, in \u001B[0;36m_BaseAutoModelClass.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001B[0m\n\u001B[0;32m 562\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(config) \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping\u001B[38;5;241m.\u001B[39mkeys():\n\u001B[0;32m 563\u001B[0m model_class \u001B[38;5;241m=\u001B[39m _get_model_class(config, \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping)\n\u001B[1;32m--> 564\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m model_class\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m 565\u001B[0m pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39mmodel_args, config\u001B[38;5;241m=\u001B[39mconfig, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs\n\u001B[0;32m 566\u001B[0m )\n\u001B[0;32m 567\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 568\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnrecognized configuration class \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mconfig\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m for this kind of AutoModel: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 569\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel type should be one of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(c\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mfor\u001B[39;00m\u001B[38;5;250m \u001B[39mc\u001B[38;5;250m \u001B[39m\u001B[38;5;129;01min\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping\u001B[38;5;241m.\u001B[39mkeys())\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 570\u001B[0m )\n",
+ "File \u001B[1;32m~\\PycharmProjects\\BachelorArbeit\\.venv\\lib\\site-packages\\transformers\\modeling_utils.py:3963\u001B[0m, in \u001B[0;36mPreTrainedModel.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001B[0m\n\u001B[0;32m 3960\u001B[0m device_map \u001B[38;5;241m=\u001B[39m infer_auto_device_map(model, dtype\u001B[38;5;241m=\u001B[39mtarget_dtype, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mdevice_map_kwargs)\n\u001B[0;32m 3962\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m hf_quantizer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 3963\u001B[0m \u001B[43mhf_quantizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalidate_environment\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdevice_map\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdevice_map\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3965\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m device_map \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 3966\u001B[0m model\u001B[38;5;241m.\u001B[39mtie_weights()\n",
+ "File \u001B[1;32m~\\PycharmProjects\\BachelorArbeit\\.venv\\lib\\site-packages\\transformers\\quantizers\\quantizer_bnb_8bit.py:101\u001B[0m, in \u001B[0;36mBnb8BitHfQuantizer.validate_environment\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 99\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 100\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcpu\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m device_map_without_lm_head\u001B[38;5;241m.\u001B[39mvalues() \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdisk\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m device_map_without_lm_head\u001B[38;5;241m.\u001B[39mvalues():\n\u001B[1;32m--> 101\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 102\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSome modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 103\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mquantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 104\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124min 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 105\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`from_pretrained`. Check \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhttps://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfor more details. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 108\u001B[0m )\n\u001B[0;32m 110\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m version\u001B[38;5;241m.\u001B[39mparse(importlib\u001B[38;5;241m.\u001B[39mmetadata\u001B[38;5;241m.\u001B[39mversion(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbitsandbytes\u001B[39m\u001B[38;5;124m\"\u001B[39m)) \u001B[38;5;241m<\u001B[39m version\u001B[38;5;241m.\u001B[39mparse(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.37.2\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m 111\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 112\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mYou have a version of `bitsandbytes` that is not compatible with 8bit inference and training\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m make sure you have the latest version of `bitsandbytes` installed\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 114\u001B[0m )\n",
+ "\u001B[1;31mValueError\u001B[0m: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details. "
+ ]
}
],
- "execution_count": 3
+ "execution_count": 13
},
{
"metadata": {
@@ -368,10 +370,8 @@
},
{
"metadata": {
- "jupyter": {
- "is_executing": true
- },
"ExecuteTime": {
+ "end_time": "2025-01-31T11:04:35.503990Z",
"start_time": "2025-01-31T11:01:11.569206Z"
}
},
@@ -455,11 +455,16 @@
"----------------\n",
"LLM: 1 Mensch: 5 | Dfs:AGR Entry:58\n",
"Based on the provided scale, predict the person's response to the new statement considering their previous answers.\n",
- "----------------\n"
+ "----------------\n",
+ "LLM: 1 Mensch: 5 | Dfs:OPN Entry:13\n",
+ "Based on the provided scale, predict the person's response to the new statement considering their previous answers.\n",
+ "----------------\n",
+ "Failed iterations: 0\n",
+ "Accuracy: 34.02298850574713\n"
]
}
],
- "execution_count": null
+ "execution_count": 10
}
],
"metadata": {