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ai_ecosystem

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  1. README.md +92 -3
  2. get_expanded_dataset.ipynb +925 -0
  3. requirements.txt +32 -0
README.md CHANGED
@@ -1,3 +1,92 @@
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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # AI Ecosystem Project
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+
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+ This project contains datasets and analysis tools for exploring the AI ecosystem, particularly focusing on models available on HuggingFace.
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+
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+ ## Setup
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+
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+ ### Virtual Environment
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+
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+ A Python virtual environment has been created with all necessary dependencies installed.
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+
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+ #### To activate the virtual environment:
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+
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+ **Option 1: Use the activation script**
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+ ```bash
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+ ./activate_env.sh
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+ ```
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+
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+ **Option 2: Manual activation**
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+ ```bash
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+ source venv/bin/activate
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+ ```
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+
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+ #### To deactivate:
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+ ```bash
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+ deactivate
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+ ```
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+
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+ ### Dependencies
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+
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+ The following packages are installed in the virtual environment:
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+
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+ #### Core Data Science
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+ - `numpy>=1.24.0` - Numerical computing
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+ - `pandas>=2.0.0` - Data manipulation and analysis
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+ - `seaborn>=0.12.0` - Statistical data visualization
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+ - `matplotlib>=3.7.0` - Plotting library
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+
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+ #### Jupyter and Development
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+ - `jupyter>=1.0.0` - Jupyter notebook environment
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+ - `ipykernel>=6.0.0` - Python kernel for Jupyter
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+ - `notebook>=6.5.0` - Classic Jupyter notebook
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+
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+ #### Data Processing and Analysis
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+ - `scikit-learn>=1.3.0` - Machine learning library
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+ - `scipy>=1.10.0` - Scientific computing
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+
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+ #### Visualization
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+ - `plotly>=5.15.0` - Interactive plotting
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+ - `bokeh>=3.0.0` - Interactive visualization
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+
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+ #### AI/ML Specific
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+ - `torch>=2.0.0` - PyTorch deep learning framework
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+ - `transformers>=4.30.0` - HuggingFace transformers library
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+ - `datasets>=2.12.0` - HuggingFace datasets library
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+
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+ #### Utilities
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+ - `json5>=0.9.0` - JSON handling
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+ - `tqdm>=4.65.0` - Progress bars
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+ - `requests>=2.31.0` - HTTP library
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+ - `python-dotenv>=1.0.0` - Environment variable management
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+
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+ ## Usage
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+
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+ ### Running Jupyter Notebooks
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+
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+ 1. Activate the virtual environment:
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+ ```bash
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+ ./activate_env.sh
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+ ```
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+
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+ 2. Start Jupyter Notebook:
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+ ```bash
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+ jupyter notebook
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+ ```
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+
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+ 3. Or start Jupyter Lab:
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+ ```bash
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+ jupyter lab
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+ ```
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+
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+ ### Project Files
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+
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+ - `get_expanded_dataset.ipynb` - Notebook for expanding JSON datasets into tabular format
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+ - `ai_ecosystem_jsons.csv` - Original JSON dataset
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+ - `ai_ecosystem_dataset copy.csv` - Expanded dataset copy
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+ - `ai_ecosystem_withmodelcards copy.csv` - Dataset with model cards
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+
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+ ## Notes
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+
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+ - The virtual environment is located in the `venv/` directory
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+ - All dependencies are specified in `requirements.txt`
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+ - Use the activation script for convenience when starting work on this project
get_expanded_dataset.ipynb ADDED
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+ {
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+ {
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+ "cell_type": "markdown",
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+ "id": "62acacf6",
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+ "metadata": {},
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+ "source": [
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+ "# Get Expanded Dataset\n",
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+ "\n",
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+ "In this notebook, we take the json-only dataset of models on HuggingFace (given in the document `ai_ecosystems_jsons.csv') and we produce an expanded csv dataset where the json elements are expanded into fields. This will give us the tabular dataset that we post online *without model cards*."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "0c7933f2",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import numpy as np\n",
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+ "import pandas as pd\n",
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+ "\n",
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+ "# Extract parent information from tags\n",
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+ "def extract_parents(tags):\n",
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+ " parent_list = []\n",
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+ " finetune_parents = []\n",
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+ " quantized_parents = []\n",
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+ " adapter_parents = []\n",
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+ " merge_parents = []\n",
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+ " for tag in tags:\n",
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+ " if tag.startswith(\"base_model:\") and tag.count(\":\") == 1:\n",
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+ " parent_list.append(tag[len(\"base_model:\"):])\n",
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+ " if tag.startswith(\"base_model:finetune:\"):\n",
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+ " finetune_parents.append(tag[len(\"base_model:finetune:\"):]) \n",
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+ " elif tag.startswith(\"base_model:quantized:\"):\n",
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+ " quantized_parents.append(tag[len(\"base_model:quantized:\"):])\n",
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+ " elif tag.startswith(\"base_model:adapter:\"):\n",
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+ " adapter_parents.append(tag[len(\"base_model:adapter:\"):])\n",
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+ " elif tag.startswith(\"base_model:merge:\"):\n",
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+ " merge_parents.append(tag[len(\"base_model:merge:\"):])\n",
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+ " return (parent_list, finetune_parents, quantized_parents, adapter_parents, merge_parents)\n",
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+ "\n",
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+ "# Extract tag information from tags\n",
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+ "def extract_languages(tags):\n",
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+ " languages = []\n",
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+ " for tag in tags:\n",
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+ " if len(str(tag))==2 and tag in pycountry.languages.get(alpha_2=tag).name:\n",
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+ " languages.append(tag)\n",
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+ " return languages"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "id": "d7a579df",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Read the raw data\n",
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+ "raw_df = pd.read_csv(\"ai_ecosystem_jsons.csv\")\n",
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+ "\n",
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+ "# Convert the fullJson column to a pandas dataframe\n",
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+ "processed_df = pd.json_normalize(raw_df['fullJson'].apply(eval))"
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+ ]
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+ },
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+ {
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304
+ "1860410 0.0 False 0 \n",
305
+ "\n",
306
+ " tags \\\n",
307
+ "0 [transformers, safetensors, kimi_k2, text-gene... \n",
308
+ "1 [transformers, safetensors, glm4v, image-text-... \n",
309
+ "2 [transformers, safetensors, smollm3, text-gene... \n",
310
+ "3 [diffusers, safetensors, image-generation, flu... \n",
311
+ "4 [vllm, safetensors, mistral, text2text-generat... \n",
312
+ "... ... \n",
313
+ "1860406 [license:apache-2.0, region:us] \n",
314
+ "1860407 [transformers, safetensors, t5, text2text-gene... \n",
315
+ "1860408 [license:apache-2.0, region:us] \n",
316
+ "1860409 [region:us] \n",
317
+ "1860410 [transformers, arxiv:1910.09700, endpoints_com... \n",
318
+ "\n",
319
+ " pipeline_tag library_name createdAt \\\n",
320
+ "0 text-generation transformers 2025-07-11T00:55:12.000Z \n",
321
+ "1 image-text-to-text transformers 2025-06-28T14:24:10.000Z \n",
322
+ "2 text-generation transformers 2025-07-08T10:11:45.000Z \n",
323
+ "3 image-to-image diffusers 2025-05-28T22:23:43.000Z \n",
324
+ "4 text-generation vllm 2025-07-04T14:23:44.000Z \n",
325
+ "... ... ... ... \n",
326
+ "1860406 NaN NaN 2025-07-12T17:13:42.000Z \n",
327
+ "1860407 text-generation transformers 2025-07-12T17:13:59.000Z \n",
328
+ "1860408 NaN NaN 2025-07-12T17:14:00.000Z \n",
329
+ "1860409 NaN NaN 2025-07-12T17:14:07.000Z \n",
330
+ "1860410 NaN transformers 2025-07-12T17:14:09.000Z \n",
331
+ "\n",
332
+ " modelId \n",
333
+ "0 moonshotai/Kimi-K2-Instruct \n",
334
+ "1 THUDM/GLM-4.1V-9B-Thinking \n",
335
+ "2 HuggingFaceTB/SmolLM3-3B \n",
336
+ "3 black-forest-labs/FLUX.1-Kontext-dev \n",
337
+ "4 mistralai/Devstral-Small-2507 \n",
338
+ "... ... \n",
339
+ "1860406 Amal17/NusaBERT-concate-BiGRU-NusaParagraph-emot \n",
340
+ "1860407 jackrvn/bidirectional-dialect-translator \n",
341
+ "1860408 Amal17/NusaBERT-concate-BiGRU-NusaParagraph-topic \n",
342
+ "1860409 ond-ai/ond-agent-1.3-8b-ckpt-1 \n",
343
+ "1860410 jackrvn/biderectional-dialect-translator \n",
344
+ "\n",
345
+ "[1860411 rows x 11 columns]"
346
+ ]
347
+ },
348
+ "execution_count": 5,
349
+ "metadata": {},
350
+ "output_type": "execute_result"
351
+ }
352
+ ],
353
+ "source": [
354
+ "processed_df"
355
+ ]
356
+ },
357
+ {
358
+ "cell_type": "code",
359
+ "execution_count": null,
360
+ "id": "90a612e9",
361
+ "metadata": {},
362
+ "outputs": [],
363
+ "source": [
364
+ "# We'd like region, base_model, license, arxiv, dataset\n",
365
+ "#processed_df['region_count'] = processed_df['tags'].apply(lambda x: x.count('region:'))\n",
366
+ "processed_df['regions'] = processed_df['tags'].apply(lambda x: [tag.replace('region:', '') for tag in x if tag.startswith('region:')])\n",
367
+ "#processed_df['region_count'] = processed_df['regions'].apply(lambda x: len(x))\n",
368
+ "\n",
369
+ "processed_df['licenses'] = processed_df['tags'].apply(lambda x: [tag.replace('license:', '') for tag in x if tag.startswith('license:')])\n",
370
+ "#processed_df['license_count'] = processed_df['licenses'].apply(lambda x: len(x))\n",
371
+ "\n",
372
+ "processed_df['arxiv_papers'] = processed_df['tags'].apply(lambda x: [tag.replace('arxiv:', '') for tag in x if tag.startswith('arxiv:')])\n",
373
+ "#processed_df['arxiv_count'] = processed_df['arxiv_papers'].apply(lambda x: len(x))\n",
374
+ "\n",
375
+ "processed_df['datasets'] = processed_df['tags'].apply(lambda x: [tag.replace('dataset:', '') for tag in x if tag.startswith('dataset:')])\n",
376
+ "#processed_df['dataset_count'] = processed_df['datasets'].apply(lambda x: len(x))\n",
377
+ "\n"
378
+ ]
379
+ },
380
+ {
381
+ "cell_type": "code",
382
+ "execution_count": null,
383
+ "id": "534641f2",
384
+ "metadata": {},
385
+ "outputs": [
386
+ {
387
+ "data": {
388
+ "text/html": [
389
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390
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402
+ "</style>\n",
403
+ "<table border=\"1\" class=\"dataframe\">\n",
404
+ " <thead>\n",
405
+ " <tr style=\"text-align: right;\">\n",
406
+ " <th></th>\n",
407
+ " <th>model_id</th>\n",
408
+ " <th>likes</th>\n",
409
+ " <th>trendingScore</th>\n",
410
+ " <th>private</th>\n",
411
+ " <th>downloads</th>\n",
412
+ " <th>tags</th>\n",
413
+ " <th>pipeline_tag</th>\n",
414
+ " <th>library_name</th>\n",
415
+ " <th>createdAt</th>\n",
416
+ " <th>region_count</th>\n",
417
+ " <th>...</th>\n",
418
+ " <th>license_count</th>\n",
419
+ " <th>arxiv_papers</th>\n",
420
+ " <th>arxiv_count</th>\n",
421
+ " <th>datasets</th>\n",
422
+ " <th>dataset_count</th>\n",
423
+ " <th>parent_model</th>\n",
424
+ " <th>finetune_parent</th>\n",
425
+ " <th>quantized_parent</th>\n",
426
+ " <th>adapter_parent</th>\n",
427
+ " <th>merge_parent</th>\n",
428
+ " </tr>\n",
429
+ " </thead>\n",
430
+ " <tbody>\n",
431
+ " <tr>\n",
432
+ " <th>0</th>\n",
433
+ " <td>moonshotai/Kimi-K2-Instruct</td>\n",
434
+ " <td>479</td>\n",
435
+ " <td>479.0</td>\n",
436
+ " <td>False</td>\n",
437
+ " <td>13356</td>\n",
438
+ " <td>[transformers, safetensors, kimi_k2, text-gene...</td>\n",
439
+ " <td>text-generation</td>\n",
440
+ " <td>transformers</td>\n",
441
+ " <td>2025-07-11T00:55:12.000Z</td>\n",
442
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443
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451
+ " <td>[]</td>\n",
452
+ " <td>[]</td>\n",
453
+ " <td>[]</td>\n",
454
+ " </tr>\n",
455
+ " <tr>\n",
456
+ " <th>1</th>\n",
457
+ " <td>THUDM/GLM-4.1V-9B-Thinking</td>\n",
458
+ " <td>569</td>\n",
459
+ " <td>367.0</td>\n",
460
+ " <td>False</td>\n",
461
+ " <td>33839</td>\n",
462
+ " <td>[transformers, safetensors, glm4v, image-text-...</td>\n",
463
+ " <td>image-text-to-text</td>\n",
464
+ " <td>transformers</td>\n",
465
+ " <td>2025-06-28T14:24:10.000Z</td>\n",
466
+ " <td>1</td>\n",
467
+ " <td>...</td>\n",
468
+ " <td>1</td>\n",
469
+ " <td>[2507.01006]</td>\n",
470
+ " <td>1</td>\n",
471
+ " <td>[]</td>\n",
472
+ " <td>0</td>\n",
473
+ " <td>[THUDM/GLM-4-9B-0414]</td>\n",
474
+ " <td>[THUDM/GLM-4-9B-0414]</td>\n",
475
+ " <td>[]</td>\n",
476
+ " <td>[]</td>\n",
477
+ " <td>[]</td>\n",
478
+ " </tr>\n",
479
+ " <tr>\n",
480
+ " <th>2</th>\n",
481
+ " <td>HuggingFaceTB/SmolLM3-3B</td>\n",
482
+ " <td>351</td>\n",
483
+ " <td>351.0</td>\n",
484
+ " <td>False</td>\n",
485
+ " <td>21863</td>\n",
486
+ " <td>[transformers, safetensors, smollm3, text-gene...</td>\n",
487
+ " <td>text-generation</td>\n",
488
+ " <td>transformers</td>\n",
489
+ " <td>2025-07-08T10:11:45.000Z</td>\n",
490
+ " <td>1</td>\n",
491
+ " <td>...</td>\n",
492
+ " <td>1</td>\n",
493
+ " <td>[]</td>\n",
494
+ " <td>0</td>\n",
495
+ " <td>[]</td>\n",
496
+ " <td>0</td>\n",
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498
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499
+ " <td>[]</td>\n",
500
+ " <td>[]</td>\n",
501
+ " <td>[]</td>\n",
502
+ " </tr>\n",
503
+ " <tr>\n",
504
+ " <th>3</th>\n",
505
+ " <td>black-forest-labs/FLUX.1-Kontext-dev</td>\n",
506
+ " <td>1568</td>\n",
507
+ " <td>247.0</td>\n",
508
+ " <td>False</td>\n",
509
+ " <td>230863</td>\n",
510
+ " <td>[diffusers, safetensors, image-generation, flu...</td>\n",
511
+ " <td>image-to-image</td>\n",
512
+ " <td>diffusers</td>\n",
513
+ " <td>2025-05-28T22:23:43.000Z</td>\n",
514
+ " <td>1</td>\n",
515
+ " <td>...</td>\n",
516
+ " <td>1</td>\n",
517
+ " <td>[2506.15742]</td>\n",
518
+ " <td>1</td>\n",
519
+ " <td>[]</td>\n",
520
+ " <td>0</td>\n",
521
+ " <td>[]</td>\n",
522
+ " <td>[]</td>\n",
523
+ " <td>[]</td>\n",
524
+ " <td>[]</td>\n",
525
+ " <td>[]</td>\n",
526
+ " </tr>\n",
527
+ " <tr>\n",
528
+ " <th>4</th>\n",
529
+ " <td>mistralai/Devstral-Small-2507</td>\n",
530
+ " <td>155</td>\n",
531
+ " <td>155.0</td>\n",
532
+ " <td>False</td>\n",
533
+ " <td>5090</td>\n",
534
+ " <td>[vllm, safetensors, mistral, text2text-generat...</td>\n",
535
+ " <td>text-generation</td>\n",
536
+ " <td>vllm</td>\n",
537
+ " <td>2025-07-04T14:23:44.000Z</td>\n",
538
+ " <td>1</td>\n",
539
+ " <td>...</td>\n",
540
+ " <td>1</td>\n",
541
+ " <td>[]</td>\n",
542
+ " <td>0</td>\n",
543
+ " <td>[]</td>\n",
544
+ " <td>0</td>\n",
545
+ " <td>[mistralai/Mistral-Small-3.1-24B-Instruct-2503]</td>\n",
546
+ " <td>[mistralai/Mistral-Small-3.1-24B-Instruct-2503]</td>\n",
547
+ " <td>[]</td>\n",
548
+ " <td>[]</td>\n",
549
+ " <td>[]</td>\n",
550
+ " </tr>\n",
551
+ " </tbody>\n",
552
+ "</table>\n",
553
+ "<p>5 rows × 23 columns</p>\n",
554
+ "</div>"
555
+ ],
556
+ "text/plain": [
557
+ " model_id likes trendingScore private \\\n",
558
+ "0 moonshotai/Kimi-K2-Instruct 479 479.0 False \n",
559
+ "1 THUDM/GLM-4.1V-9B-Thinking 569 367.0 False \n",
560
+ "2 HuggingFaceTB/SmolLM3-3B 351 351.0 False \n",
561
+ "3 black-forest-labs/FLUX.1-Kontext-dev 1568 247.0 False \n",
562
+ "4 mistralai/Devstral-Small-2507 155 155.0 False \n",
563
+ "\n",
564
+ " downloads tags \\\n",
565
+ "0 13356 [transformers, safetensors, kimi_k2, text-gene... \n",
566
+ "1 33839 [transformers, safetensors, glm4v, image-text-... \n",
567
+ "2 21863 [transformers, safetensors, smollm3, text-gene... \n",
568
+ "3 230863 [diffusers, safetensors, image-generation, flu... \n",
569
+ "4 5090 [vllm, safetensors, mistral, text2text-generat... \n",
570
+ "\n",
571
+ " pipeline_tag library_name createdAt region_count \\\n",
572
+ "0 text-generation transformers 2025-07-11T00:55:12.000Z 1 \n",
573
+ "1 image-text-to-text transformers 2025-06-28T14:24:10.000Z 1 \n",
574
+ "2 text-generation transformers 2025-07-08T10:11:45.000Z 1 \n",
575
+ "3 image-to-image diffusers 2025-05-28T22:23:43.000Z 1 \n",
576
+ "4 text-generation vllm 2025-07-04T14:23:44.000Z 1 \n",
577
+ "\n",
578
+ " ... license_count arxiv_papers arxiv_count datasets dataset_count \\\n",
579
+ "0 ... 1 [] 0 [] 0 \n",
580
+ "1 ... 1 [2507.01006] 1 [] 0 \n",
581
+ "2 ... 1 [] 0 [] 0 \n",
582
+ "3 ... 1 [2506.15742] 1 [] 0 \n",
583
+ "4 ... 1 [] 0 [] 0 \n",
584
+ "\n",
585
+ " parent_model \\\n",
586
+ "0 [] \n",
587
+ "1 [THUDM/GLM-4-9B-0414] \n",
588
+ "2 [] \n",
589
+ "3 [] \n",
590
+ "4 [mistralai/Mistral-Small-3.1-24B-Instruct-2503] \n",
591
+ "\n",
592
+ " finetune_parent quantized_parent \\\n",
593
+ "0 [] [] \n",
594
+ "1 [THUDM/GLM-4-9B-0414] [] \n",
595
+ "2 [] [] \n",
596
+ "3 [] [] \n",
597
+ "4 [mistralai/Mistral-Small-3.1-24B-Instruct-2503] [] \n",
598
+ "\n",
599
+ " adapter_parent merge_parent \n",
600
+ "0 [] [] \n",
601
+ "1 [] [] \n",
602
+ "2 [] [] \n",
603
+ "3 [] [] \n",
604
+ "4 [] [] \n",
605
+ "\n",
606
+ "[5 rows x 23 columns]"
607
+ ]
608
+ },
609
+ "execution_count": 26,
610
+ "metadata": {},
611
+ "output_type": "execute_result"
612
+ }
613
+ ],
614
+ "source": [
615
+ "# Append parent information to the dataset\n",
616
+ "processed_df[['parent_model','finetune_parent', 'quantized_parent', 'adapter_parent', 'merge_parent']] = pd.DataFrame(\n",
617
+ " processed_df['tags'].apply(extract_parents).tolist(), index=processed_df.index\n",
618
+ ")\n",
619
+ "\n",
620
+ "# Drop the columns \"_id\" and \"modelId\" (the former is unneeded, the latter is redundant)\n",
621
+ "processed_df.drop(columns=['_id', 'modelId'], inplace=True)\n",
622
+ "\n",
623
+ "# Rename the column \"id\" to \"model_id\"\n",
624
+ "processed_df.rename(columns={'id': 'model_id'}, inplace=True)\n",
625
+ "\n",
626
+ "processed_df.head()"
627
+ ]
628
+ },
629
+ {
630
+ "cell_type": "code",
631
+ "execution_count": null,
632
+ "id": "405a1f18",
633
+ "metadata": {},
634
+ "outputs": [],
635
+ "source": [
636
+ "import pycountry\n",
637
+ "\n",
638
+ "# Add the languages information\n",
639
+ "processed_df['languages'] = processed_df['tags'].apply(extract_languages)\n",
640
+ "\n",
641
+ "for "
642
+ ]
643
+ },
644
+ {
645
+ "cell_type": "code",
646
+ "execution_count": 28,
647
+ "id": "d63d9aee",
648
+ "metadata": {},
649
+ "outputs": [
650
+ {
651
+ "ename": "ModuleNotFoundError",
652
+ "evalue": "No module named 'pycountry'",
653
+ "output_type": "error",
654
+ "traceback": [
655
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
656
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
657
+ "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpycountry\u001b[39;00m\n\u001b[1;32m 3\u001b[0m pycountry\u001b[38;5;241m.\u001b[39mlanguages\u001b[38;5;241m.\u001b[39mget(alpha_2\u001b[38;5;241m=\u001b[39mtag)\u001b[38;5;241m.\u001b[39mname\n",
658
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pycountry'"
659
+ ]
660
+ }
661
+ ],
662
+ "source": [
663
+ "import pycountry\n",
664
+ "\n",
665
+ "pycountry.languages.get(alpha_2=tag).name"
666
+ ]
667
+ },
668
+ {
669
+ "cell_type": "code",
670
+ "execution_count": null,
671
+ "id": "a97c5f78",
672
+ "metadata": {},
673
+ "outputs": [],
674
+ "source": []
675
+ },
676
+ {
677
+ "cell_type": "code",
678
+ "execution_count": null,
679
+ "id": "e874cfe5",
680
+ "metadata": {},
681
+ "outputs": [],
682
+ "source": []
683
+ },
684
+ {
685
+ "cell_type": "code",
686
+ "execution_count": null,
687
+ "id": "31c88ced",
688
+ "metadata": {},
689
+ "outputs": [],
690
+ "source": []
691
+ },
692
+ {
693
+ "cell_type": "code",
694
+ "execution_count": null,
695
+ "id": "683a3159",
696
+ "metadata": {},
697
+ "outputs": [],
698
+ "source": []
699
+ },
700
+ {
701
+ "cell_type": "code",
702
+ "execution_count": null,
703
+ "id": "1dbc6f23",
704
+ "metadata": {},
705
+ "outputs": [],
706
+ "source": [
707
+ "processed_df['license_count'] = processed_df['tags'].apply(lambda x: x.count('license:'))\n",
708
+ "processed_df['license_list'] = processed_df['tags'].apply(lambda x: [tag.replace('license:', '') for tag in list(ast.literal_eval(x)) if tag.startswith('license:')])\n",
709
+ "\n",
710
+ "processed_df['arxiv_count'] = processed_df['tags'].apply(lambda x: x.count('arxiv:'))\n",
711
+ "processed_df['arxiv_papers'] = processed_df['tags'].apply(lambda x: [tag.replace('arxiv:', '') for tag in list(ast.literal_eval(x)) if tag.startswith('arxiv:')])\n",
712
+ "\n",
713
+ "processed_df['dataset_count'] = processed_df['tags'].apply(lambda x: x.count('dataset:'))\n",
714
+ "processed_df['dataset_list'] = processed_df['tags'].apply(lambda x: [tag.replace('dataset:', '') for tag in list(ast.literal_eval(x)) if tag.startswith('dataset:')])\n"
715
+ ]
716
+ },
717
+ {
718
+ "cell_type": "code",
719
+ "execution_count": null,
720
+ "id": "4760cd6b",
721
+ "metadata": {},
722
+ "outputs": [],
723
+ "source": []
724
+ },
725
+ {
726
+ "cell_type": "code",
727
+ "execution_count": null,
728
+ "id": "38701172",
729
+ "metadata": {},
730
+ "outputs": [],
731
+ "source": []
732
+ },
733
+ {
734
+ "cell_type": "code",
735
+ "execution_count": null,
736
+ "id": "e6ab875b",
737
+ "metadata": {},
738
+ "outputs": [],
739
+ "source": []
740
+ },
741
+ {
742
+ "cell_type": "code",
743
+ "execution_count": null,
744
+ "id": "6d57a559",
745
+ "metadata": {},
746
+ "outputs": [
747
+ {
748
+ "name": "stdout",
749
+ "output_type": "stream",
750
+ "text": [
751
+ "Processed 500000 rows\n",
752
+ "Processed 1000000 rows\n",
753
+ "Processed 1500000 rows\n"
754
+ ]
755
+ }
756
+ ],
757
+ "source": [
758
+ "import ast\n",
759
+ "\n",
760
+ "list_set_all_tag_categories = []\n",
761
+ "set_all_tag_categories = set()\n",
762
+ "prog = 0\n",
763
+ "for index, row in processed_df.iterrows():\n",
764
+ " for tag in row['tags']:\n",
765
+ " if tag.count(\":\") > 0:\n",
766
+ " category = tag.split(\":\")[0]\n",
767
+ " list_set_all_tag_categories.append(category)\n",
768
+ " if category not in set_all_tag_categories:\n",
769
+ " set_all_tag_categories.add(category)\n",
770
+ " #list_all_tags.append(category)\n",
771
+ " prog += 1\n",
772
+ " if prog % 100000 == 0:\n",
773
+ " print(f\"Processed {prog} rows\")\n",
774
+ "\n",
775
+ "\n",
776
+ "\n",
777
+ "\n"
778
+ ]
779
+ },
780
+ {
781
+ "cell_type": "code",
782
+ "execution_count": 13,
783
+ "id": "38eaf55e",
784
+ "metadata": {},
785
+ "outputs": [
786
+ {
787
+ "data": {
788
+ "text/plain": [
789
+ "[('region', 1860414),\n",
790
+ " ('base_model', 1163751),\n",
791
+ " ('license', 671895),\n",
792
+ " ('arxiv', 416367),\n",
793
+ " ('dataset', 229630),\n",
794
+ " ('diffusers', 33337),\n",
795
+ " ('template', 20595),\n",
796
+ " ('loss', 6554),\n",
797
+ " ('dataset_size', 5171),\n",
798
+ " ('doi', 2702),\n",
799
+ " ('BaseLM', 1512),\n",
800
+ " ('adapterhub', 733),\n",
801
+ " ('tags', 187),\n",
802
+ " ('model', 186),\n",
803
+ " ('repo_name', 186),\n",
804
+ " ('file_name', 186),\n",
805
+ " ('pruning_style', 186),\n",
806
+ " ('community', 186),\n",
807
+ " ('pruning_ratio', 186),\n",
808
+ " ('dataset_label', 186),\n",
809
+ " ('sparsity_ratio', 186),\n",
810
+ " ('finetune', 186),\n",
811
+ " ('modules_size', 186),\n",
812
+ " ('modules', 186),\n",
813
+ " ('rank', 186),\n",
814
+ " ('anndata_version', 183),\n",
815
+ " ('tissue', 180),\n",
816
+ " ('modality', 118),\n",
817
+ " ('model_cls_name', 107),\n",
818
+ " ('annotated', 107),\n",
819
+ " ('scvi_version', 105),\n",
820
+ " ('python_version', 77),\n",
821
+ " ('#', 33),\n",
822
+ " ('https', 22),\n",
823
+ " ('pipeline', 11),\n",
824
+ " ('$', 9),\n",
825
+ " ('benchmark', 7),\n",
826
+ " ('arXiv', 7),\n",
827
+ " ('Mi', 6),\n",
828
+ " ('Voice', 6),\n",
829
+ " ('version', 4),\n",
830
+ " ('format', 4),\n",
831
+ " ('library', 3),\n",
832
+ " ('type', 3),\n",
833
+ " ('Dramatical Murder Re', 3),\n",
834
+ " ('sparsity‑2', 3),\n",
835
+ " ('TikTok', 2),\n",
836
+ " ('twitter', 2),\n",
837
+ " ('inference', 2),\n",
838
+ " ('3', 2),\n",
839
+ " ('voice', 2),\n",
840
+ " ('generated', 2),\n",
841
+ " ('cs', 1),\n",
842
+ " ('@', 1),\n",
843
+ " ('*', 1),\n",
844
+ " (' $', 1),\n",
845
+ " ('http', 1),\n",
846
+ " ('mytag', 1),\n",
847
+ " ('Skill', 1),\n",
848
+ " ('ai new new models. It has been generated using [this raw tempfor new models. It has been generated using [this raw template](https',\n",
849
+ " 1),\n",
850
+ " ('lr', 1),\n",
851
+ " ('epochs', 1),\n",
852
+ " ('lora-dropout', 1),\n",
853
+ " ('train-batch', 1),\n",
854
+ " ('optim', 1),\n",
855
+ " ('weight-decay', 1),\n",
856
+ " ('gradient_accumulation_steps', 1),\n",
857
+ " ('lora-r', 1),\n",
858
+ " ('lora-alpha', 1),\n",
859
+ " ('dataset-size', 1),\n",
860
+ " ('about', 1),\n",
861
+ " ('', 1),\n",
862
+ " ('pipeline_tag', 1),\n",
863
+ " ('queued_at', 1),\n",
864
+ " ('costPerHr', 1),\n",
865
+ " ('gpu', 1),\n",
866
+ " ('started_at', 1),\n",
867
+ " ('started_training_at', 1),\n",
868
+ " ('status', 1),\n",
869
+ " ('completed_at', 1),\n",
870
+ " ('Type-Count', 1),\n",
871
+ " ('19', 1),\n",
872
+ " ('- lora - peft - gemma - safesky-ai - ai-safety - sft - hh-rlhf - text-generation - transformers base_model',\n",
873
+ " 1),\n",
874
+ " ('volume', 1),\n",
875
+ " ('adapterhub_tag', 1),\n",
876
+ " ('datasets', 1)]"
877
+ ]
878
+ },
879
+ "execution_count": 13,
880
+ "metadata": {},
881
+ "output_type": "execute_result"
882
+ }
883
+ ],
884
+ "source": [
885
+ "# Count frequency of values in list_set_all_tag_categories\n",
886
+ "from collections import Counter\n",
887
+ "\n",
888
+ "# Count frequency of values in list_set_all_tag_categories\n",
889
+ "tag_category_counts = Counter(list_set_all_tag_categories)\n",
890
+ "\n",
891
+ "# Display the most common tag categories\n",
892
+ "tag_category_counts.most_common()"
893
+ ]
894
+ },
895
+ {
896
+ "cell_type": "code",
897
+ "execution_count": null,
898
+ "id": "ee8c37fd",
899
+ "metadata": {},
900
+ "outputs": [],
901
+ "source": []
902
+ }
903
+ ],
904
+ "metadata": {
905
+ "kernelspec": {
906
+ "display_name": "venv",
907
+ "language": "python",
908
+ "name": "python3"
909
+ },
910
+ "language_info": {
911
+ "codemirror_mode": {
912
+ "name": "ipython",
913
+ "version": 3
914
+ },
915
+ "file_extension": ".py",
916
+ "mimetype": "text/x-python",
917
+ "name": "python",
918
+ "nbconvert_exporter": "python",
919
+ "pygments_lexer": "ipython3",
920
+ "version": "3.9.12"
921
+ }
922
+ },
923
+ "nbformat": 4,
924
+ "nbformat_minor": 5
925
+ }
requirements.txt ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Core data science packages
2
+ numpy>=1.24.0
3
+ pandas>=2.0.0
4
+ seaborn>=0.12.0
5
+ matplotlib>=3.7.0
6
+
7
+ # Jupyter and notebook support
8
+ jupyter>=1.0.0
9
+ ipykernel>=6.0.0
10
+ notebook>=6.5.0
11
+
12
+ # Data processing and analysis
13
+ scikit-learn>=1.3.0
14
+ scipy>=1.10.0
15
+
16
+ # Visualization
17
+ plotly>=5.15.0
18
+ bokeh>=3.0.0
19
+
20
+ # JSON handling (for the json dataset processing)
21
+ json5>=0.9.0
22
+
23
+ # Optional: AI/ML specific packages
24
+ torch>=2.0.0
25
+ transformers>=4.30.0
26
+ datasets>=2.12.0
27
+
28
+ # Development and utility packages
29
+ tqdm>=4.65.0
30
+ requests>=2.31.0
31
+ python-dotenv>=1.0.0
32
+ pycountry>=24.6.0