diff --git a/Makefile b/Makefile
deleted file mode 100644
index bda886b..0000000
--- a/Makefile
+++ /dev/null
@@ -1,52 +0,0 @@
-.PHONY: default
-default: help
-
-.PHONY: help
-help: ## Show this help message
-	@awk 'BEGIN {FS = ":.*?## "} /^[a-zA-Z_-]+:.*?## / {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST)
-
-# =============================================================================
-# ENVIRONMENT SETUP
-# =============================================================================
-
-.PHONY: setup
-setup: ## Setup virtual environment and install dependencies
-	python -m venv .venv
-	source .venv/bin/activate
-	.venv/bin/pip install --upgrade pip
-	.venv/bin/pip install -r requirements.txt
-
-.PHONY: install
-install: ## Install/update dependencies
-	pip install --upgrade pip
-	pip install -r requirements.txt
-
-# =============================================================================
-# DEVELOPMENT & CODE QUALITY
-# =============================================================================
-
-.PHONY: format
-format: ## Format code with black
-	black . --line-length 100
-
-.PHONY: lint
-lint: ## Lint code with flake8
-	flake8 . --max-line-length=100 --ignore=E203,W503 --exclude=.venv
-
-.PHONY: type-check
-type-check: ## Type check with mypy
-	mypy . --ignore-missing-imports
-
-.PHONY: notebook
-notebook: ## Start Jupyter notebook
-	jupyter notebook notebooks/
-
-# =============================================================================
-# DEPLOYMENT & PRODUCTION
-# =============================================================================
-
-.PHONY: backup
-backup: ## Backup datasets and results
-	@mkdir -p backups/$(shell date +%Y%m%d_%H%M%S)
-	@cp -r data/ backups/$(shell date +%Y%m%d_%H%M%S)/data/
-	@echo "Backup created in backups/$(shell date +%Y%m%d_%H%M%S)/"
diff --git a/README.md b/README.md
index 91886e7..a040282 100644
--- a/README.md
+++ b/README.md
@@ -91,11 +91,6 @@ uv run ners research train --name="random_forest" --type="baseline" --env="produ
 uv run ners research train --name="random_forest_native" --type="baseline" --env="production"
 uv run ners research train --name="random_forest_surname" --type="baseline" --env="production"
 
-# svm
-uv run ners research train --name="svm" --type="baseline" --env="production"
-uv run ners research train --name="svm_native" --type="baseline" --env="production"
-uv run ners research train --name="svm_surname" --type="baseline" --env="production"
-
 # naive bayes
 uv run ners research train --name="naive_bayes" --type="baseline" --env="production"
 uv run ners research train --name="naive_bayes_native" --type="baseline" --env="production"
@@ -112,46 +107,6 @@ uv run ners research train --name="xgboost_native" --type="baseline" --env="prod
 uv run ners research train --name="xgboost_surname" --type="baseline" --env="production"
 ```
 
-## TensorFlow on macOS (Intel) with uv
-
-TensorFlow no longer publishes wheels for macOS Intel. To keep using uv and run TF reliably, use a Linux container with TF preinstalled and install project code with minimal extras inside the container.
-
-### One-time build
-
-```bash
-docker compose -f docker/compose.tf.yml build
-
-If you see a message like `tensorflow/tensorflow:<tag>: not found`, update `docker/Dockerfile.tf-cpu` to a tag that exists (e.g., `2.17.0`) and rebuild:
-
-```bash
-sed -n '1,20p' docker/Dockerfile.tf-cpu  # verify the FROM line
-docker pull tensorflow/tensorflow:2.17.0 # quick availability check
-docker compose -f docker/compose.tf.yml build
-```
-```
-
-### Start a shell with uv and TF available
-
-```bash
-docker compose -f docker/compose.tf.yml run --rm tf bash
-```
-
-Inside the container:
-
-```bash
-# Install project in editable mode without pulling full deps
-uv pip install -e . --no-deps
-
-# Install only what research needs alongside TensorFlow
-uv pip install typer pandas scikit-learn seaborn plotly
-
-# Sanity check
-uv run python -c "import tensorflow as tf; print(tf.__version__)"
-
-# Run an experiment
-uv run ners research train --name="lstm" --type="baseline" --env="production"
-```
-
 ## Web Interface
 
 This project includes a user-friendly web interface built with Streamlit, allowing non-technical users to run
diff --git a/compose.yml b/compose.yml
index 640b4cb..abe3bda 100644
--- a/compose.yml
+++ b/compose.yml
@@ -10,10 +10,12 @@ services:
     environment:
       NERS_ENV: production
       STREAMLIT_SERVER_ADDRESS: 0.0.0.0
+      PYTHONPATH: /app/src
     # expose Streamlit for `ners web run`
     ports:
       - "8501:8501"
     volumes:
+      - ./src:/app/src
       - ./assets:/app/assets
       - ./config:/app/config
       - ./data:/app/data
diff --git a/config/pipeline.development.yaml b/config/pipeline.development.yaml
index b9b63fa..6c109db 100644
--- a/config/pipeline.development.yaml
+++ b/config/pipeline.development.yaml
@@ -30,7 +30,7 @@ llm:
 # Data handling configuration
 data:
   split_evaluation: false
-  max_dataset_size: 100_000
+  max_dataset_size: 10_000
   balance_by_sex: true
 
 # Enhanced logging for development
diff --git a/pyproject.toml b/pyproject.toml
index b339851..d51dd97 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ dependencies = [
     "streamlit>=1.50.0",
     "tqdm>=4.67.1",
     "typer>=0.19.2",
+    "tensorflow==2.20.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
     "xgboost>=3.0.5",
 ]
 
@@ -36,6 +37,3 @@ build-backend = "uv_build"
 dev = [
     "ruff>=0.13.3",
 ]
-
-[tool.uv]
-required-environments = ["sys_platform == 'linux' and platform_machine == 'x86_64'"]
diff --git a/src/ners/cli.py b/src/ners/cli.py
index d30cab3..8d717ad 100644
--- a/src/ners/cli.py
+++ b/src/ners/cli.py
@@ -204,7 +204,6 @@ def web_run(
     config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
     env: str = typer.Option("development", help="Environment name"),
 ) -> None:
-    """Launch the Streamlit web app via subprocess."""
     app_path = Path(__file__).parent / "web" / "app.py"
     cmd = [
         sys.executable,
diff --git a/src/ners/core/config/__init__.py b/src/ners/core/config/__init__.py
index 4169761..ce852da 100644
--- a/src/ners/core/config/__init__.py
+++ b/src/ners/core/config/__init__.py
@@ -4,7 +4,6 @@ from typing import Optional, Union
 
 from ners.core.utils import ensure_directories
 from ners.core.config.config_manager import ConfigManager
-from ners.core.config.logging_config import LoggingConfig
 from ners.core.config.pipeline_config import PipelineConfig
 
 config_manager = ConfigManager()
diff --git a/src/ners/research/models/lightgbm_model.py b/src/ners/research/models/lightgbm_model.py
index 1da50b0..4b8c913 100644
--- a/src/ners/research/models/lightgbm_model.py
+++ b/src/ners/research/models/lightgbm_model.py
@@ -16,6 +16,7 @@ class LightGBMModel(TraditionalModel):
         # Store vectorizers and encoders to ensure consistent feature space
         self.vectorizers = {}
         self.label_encoders = {}
+        self.feature_columns = []
 
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
@@ -38,14 +39,16 @@ class LightGBMModel(TraditionalModel):
             random_state=self.config.random_seed,
             objective=params.get("objective", "binary"),
             n_jobs=params.get("n_jobs", -1),
-            verbose=2,
+            verbose=params.get("verbose", -1),
             device=device,
             gpu_platform_id=gpu_platform_id,
             gpu_device_id=gpu_device_id,
+            force_row_wise=params.get("force_row_wise", True),
         )
 
-    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
+    def prepare_features(self, X: pd.DataFrame) -> pd.DataFrame | np.ndarray:
         features = []
+        columns: list[str] = []
 
         for feature_type in self.config.features:
             if feature_type.value in X.columns:
@@ -53,7 +56,9 @@ class LightGBMModel(TraditionalModel):
 
                 if feature_type.value in ["name_length", "word_count"]:
                     # Numerical features
-                    features.append(column.fillna(0).values.reshape(-1, 1))
+                    arr = column.fillna(0).values.reshape(-1, 1)
+                    features.append(arr)
+                    columns.append(feature_type.value)
                 elif feature_type.value in ["full_name", "native_name", "surname"]:
                     # Character-level features for names
                     feature_key = f"vectorizer_{feature_type.value}"
@@ -63,20 +68,24 @@ class LightGBMModel(TraditionalModel):
                         self.vectorizers[feature_key] = CountVectorizer(
                             analyzer="char", ngram_range=(2, 3), max_features=50
                         )
-                        char_features = (
-                            self.vectorizers[feature_key]
-                            .fit_transform(column.fillna("").astype(str))
-                            .toarray()
-                        )
+                        vec = self.vectorizers[feature_key]
+                        char_features = vec.fit_transform(
+                            column.fillna("").astype(str)
+                        ).toarray()
+                        vocab_names = list(vec.get_feature_names_out())
                     else:
                         # Subsequent times - use existing vectorizer
-                        char_features = (
-                            self.vectorizers[feature_key]
-                            .transform(column.fillna("").astype(str))
-                            .toarray()
-                        )
+                        vec = self.vectorizers[feature_key]
+                        char_features = vec.transform(
+                            column.fillna("").astype(str)
+                        ).toarray()
+                        vocab_names = list(vec.get_feature_names_out())
 
                     features.append(char_features)
+                    # Prefix with feature name to avoid collisions
+                    columns.extend(
+                        [f"char_{feature_type.value}_{n}" for n in vocab_names]
+                    )
                 else:
                     # Categorical features
                     feature_key = f"encoder_{feature_type.value}"
@@ -111,5 +120,11 @@ class LightGBMModel(TraditionalModel):
                         )
 
                     features.append(encoded.reshape(-1, 1))
+                    columns.append(f"cat_{feature_type.value}")
+        if not features:
+            return pd.DataFrame(index=X.index)
 
-        return np.hstack(features) if features else np.array([]).reshape(len(X), 0)
+        matrix = np.hstack(features)
+        # Persist column order for consistency
+        self.feature_columns = columns
+        return pd.DataFrame(matrix, index=X.index, columns=columns)
diff --git a/src/ners/research/models/logistic_regression_model.py b/src/ners/research/models/logistic_regression_model.py
index 452397a..983369e 100644
--- a/src/ners/research/models/logistic_regression_model.py
+++ b/src/ners/research/models/logistic_regression_model.py
@@ -1,3 +1,4 @@
+import logging
 import numpy as np
 import pandas as pd
 from sklearn.base import BaseEstimator
@@ -13,22 +14,38 @@ class LogisticRegressionModel(TraditionalModel):
 
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
-        # Character n-grams are strong signals for names; (2,5) balances
+        # Character n-grams are strong signals for names; (2,4) balances
         # capturing prefixes/suffixes with tractable feature size.
+        # Ensure tuple for sklearn API (YAML lists -> tuple)
+        ngram_range = params.get("ngram_range", (2, 4))
+        if isinstance(ngram_range, list):
+            ngram_range = tuple(ngram_range)
+
         vectorizer = CountVectorizer(
             analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 5)),
+            ngram_range=ngram_range,
             max_features=params.get("max_features", 10000),
         )
 
-        # liblinear handles sparse, small-to-medium problems well; n_jobs parallelizes
-        # OvR across classes (no effect for binary). class_weight can mitigate imbalance.
+        # Choose solver and threads. liblinear ignores n_jobs>1 in recent sklearn
+        # versions, which raises a warning; clamp to 1 to avoid noise.
+        solver = params.get("solver", "liblinear")
+        n_jobs = params.get("n_jobs", -1)
+        if solver == "liblinear" and (n_jobs is None or n_jobs != 1):
+            if isinstance(n_jobs, int) and n_jobs != 1:
+                logging.info(
+                    "LogisticRegression(liblinear): forcing n_jobs=1 to avoid sklearn warning"
+                )
+            n_jobs = 1
+
+        # liblinear handles sparse, small-to-medium problems well; class_weight can
+        # mitigate imbalance. For very large, consider solver='saga'.
         classifier = LogisticRegression(
             max_iter=params.get("max_iter", 1000),
             random_state=self.config.random_seed,
             verbose=2,
-            solver=params.get("solver", "liblinear"),
-            n_jobs=params.get("n_jobs", -1),
+            solver=solver,
+            n_jobs=n_jobs,
             class_weight=params.get("class_weight", None),
         )
 
diff --git a/src/ners/research/models/naive_bayes_model.py b/src/ners/research/models/naive_bayes_model.py
index cf8f027..ca476e8 100644
--- a/src/ners/research/models/naive_bayes_model.py
+++ b/src/ners/research/models/naive_bayes_model.py
@@ -15,9 +15,14 @@ class NaiveBayesModel(TraditionalModel):
         params = self.config.model_params
         # Bag-of-character-ngrams aligns with Multinomial NB assumptions; (1,4)
         # includes unigrams for coverage and higher n for suffix/prefix cues.
+        # Ensure tuple for sklearn API (YAML lists -> tuple)
+        ngram_range = params.get("ngram_range", (2, 4))
+        if isinstance(ngram_range, list):
+            ngram_range = tuple(ngram_range)
+
         vectorizer = CountVectorizer(
             analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 5)),
+            ngram_range=ngram_range,
             max_features=params.get("max_features", 8000),
         )
 
diff --git a/src/ners/research/models/svm_model.py b/src/ners/research/models/svm_model.py
deleted file mode 100644
index 2d56a9e..0000000
--- a/src/ners/research/models/svm_model.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn.base import BaseEstimator
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.pipeline import Pipeline
-from sklearn.svm import SVC
-
-from ners.research.traditional_model import TraditionalModel
-
-
-class SVMModel(TraditionalModel):
-    """Support Vector Machine with character n-grams and RBF kernel"""
-
-    def build_model(self) -> BaseEstimator:
-        params = self.config.model_params
-        # TF-IDF downweights very common patterns; char n-grams (2,4) are effective
-        # for distinguishing name morphology under RBF kernels.
-        vectorizer = TfidfVectorizer(
-            analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 4)),
-            max_features=params.get("max_features", 5000),
-        )
-
-        # RBF kernel captures non-linear interactions between n-grams; probability=True
-        # adds calibration at some cost. Larger cache helps speed kernel computations.
-        classifier = SVC(
-            kernel=params.get("kernel", "rbf"),
-            C=params.get("C", 1.0),
-            gamma=params.get("gamma", "scale"),
-            probability=True,  # Enable probability prediction
-            class_weight=params.get("class_weight", None),
-            cache_size=params.get("cache_size", 1000),
-            random_state=self.config.random_seed,
-            verbose=2,
-        )
-
-        return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
-
-    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
-        text_features = []
-
-        for feature_type in self.config.features:
-            if feature_type.value in X.columns:
-                text_features.append(X[feature_type.value].astype(str))
-
-        if len(text_features) == 1:
-            return text_features[0].values
-        else:
-            combined = text_features[0].astype(str)
-            for feature in text_features[1:]:
-                combined = combined + " " + feature.astype(str)
-            return combined.values
diff --git a/src/ners/research/models/transformer_model.py b/src/ners/research/models/transformer_model.py
index 2fc460f..1a9876f 100644
--- a/src/ners/research/models/transformer_model.py
+++ b/src/ners/research/models/transformer_model.py
@@ -24,20 +24,21 @@ class TransformerModel(NeuralNetworkModel):
 
     def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         params = kwargs
+        # Use a single resolved max_len everywhere to avoid shape mismatches
+        max_len = int(params.get("max_len", 6))
 
         # Build Transformer model
-        inputs = Input(shape=(params.get("max_len", 8),))
+        inputs = Input(shape=(max_len,))
         x = Embedding(
             input_dim=vocab_size,
             output_dim=params.get("embedding_dim", 64),
-            input_length=params.get("max_len", 8),
             mask_zero=True,
         )(inputs)
 
         # Add positional encoding
-        positions = tf.range(start=0, limit=params.get("max_len", 8), delta=1)
+        positions = tf.range(start=0, limit=max_len, delta=1)
         pos_embedding = Embedding(
-            input_dim=params.get("max_len", 8),
+            input_dim=max_len,
             output_dim=params.get("embedding_dim", 64),
         )(positions)
         x = x + pos_embedding
@@ -85,6 +86,6 @@ class TransformerModel(NeuralNetworkModel):
 
         # Convert to sequences
         sequences = self.tokenizer.texts_to_sequences(text_data)
-        max_len = self.config.model_params.get("max_len", 6)
+        max_len = int(self.config.model_params.get("max_len", 6))
 
         return pad_sequences(sequences, maxlen=max_len, padding="post")
diff --git a/src/ners/research/models/xgboost_model.py b/src/ners/research/models/xgboost_model.py
index eaf34e1..a567508 100644
--- a/src/ners/research/models/xgboost_model.py
+++ b/src/ners/research/models/xgboost_model.py
@@ -20,13 +20,12 @@ class XGBoostModel(TraditionalModel):
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
 
-        # Optional GPU acceleration
+        # Optional GPU acceleration. With modern XGBoost, setting tree_method is
+        # sufficient and you typically don't need to pass `predictor`; doing so can
+        # trigger "Parameters ... are not used" warnings with the sklearn API.
         use_gpu = bool(params.get("use_gpu", False))
         default_tree_method = "gpu_hist" if use_gpu else "hist"
         tree_method = params.get("tree_method", default_tree_method)
-        predictor = params.get(
-            "predictor", "gpu_predictor" if tree_method.startswith("gpu") else "auto"
-        )
 
         # Histogram-based trees and parallelism provide fast training; default
         # logloss metric suits binary classification of gender.
@@ -40,8 +39,7 @@ class XGBoostModel(TraditionalModel):
             eval_metric="logloss",
             n_jobs=params.get("n_jobs", -1),
             tree_method=tree_method,
-            predictor=predictor,
-            verbosity=2,
+            verbosity=params.get("verbosity", 0),
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
diff --git a/src/ners/research/traditional_model.py b/src/ners/research/traditional_model.py
index bce368c..6193947 100644
--- a/src/ners/research/traditional_model.py
+++ b/src/ners/research/traditional_model.py
@@ -61,18 +61,22 @@ class TraditionalModel(BaseModel):
                 f"Fitting model with {X_prepared.shape[0]} samples and {X_prepared.shape[1]} features"
             )
 
-        logging.info(X_prepared[0])
+        try:
+            # Log a small sample safely for arrays or DataFrames
+            if hasattr(X_prepared, "iloc"):
+                logging.info(X_prepared.iloc[0].to_dict())
+            else:
+                logging.info(X_prepared[0])
+        except Exception:
+            pass
         logging.info(f"Model parameters: {self.config.model_params}")
 
-        history = self.model.fit(X_prepared, y_encoded)
+        # Fit scikit-learn compatible model. Unlike Keras, sklearn's fit returns
+        # the estimator itself and does not provide a training history object.
+        # We therefore do not populate training_history here.
+        self.model.fit(X_prepared, y_encoded)
         self.is_fitted = True
-
-        self.training_history = {
-            "accuracy": history.history["accuracy"],
-            "loss": history.history["loss"],
-            "val_accuracy": history.history.get("val_accuracy", []),
-            "val_loss": history.history.get("val_loss", []),
-        }
+        self.training_history = {}
 
         return self
 
diff --git a/uv.lock b/uv.lock
index cf26eff..7c4d45c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,10 +2,20 @@ version = 1
 revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
     "python_full_version < '3.12'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "altair"
 version = "5.5.0"
@@ -45,6 +55,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
 ]
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+    { name = "wheel" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f3/af/4182184d3c338792894f34a62672919db7ca008c89abee9b564dd34d8029/astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872", size = 18290, upload-time = "2019-12-22T18:12:13.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8", size = 12732, upload-time = "2019-12-22T18:12:11.297Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.3.0"
@@ -344,6 +367,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/74/65/c162fbac63e867a055240b6600b92ef96c0eb7a1895312ac53c4be93d056/cymem-2.0.11-cp313-cp313-win_amd64.whl", hash = "sha256:25da111adf425c29af0cfd9fecfec1c71c8d82e2244a85166830a0817a66ada7", size = 39090, upload-time = "2025-01-16T21:50:24.239Z" },
 ]
 
+[[package]]
+name = "flatbuffers"
+version = "25.9.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/1f/3ee70b0a55137442038f2a33469cc5fddd7e0ad2abf83d7497c18a2b6923/flatbuffers-25.9.23.tar.gz", hash = "sha256:676f9fa62750bb50cf531b42a0a2a118ad8f7f797a511eda12881c016f093b12", size = 22067, upload-time = "2025-09-24T05:25:30.106Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ee/1b/00a78aa2e8fbd63f9af08c9c19e6deb3d5d66b4dda677a0f61654680ee89/flatbuffers-25.9.23-py2.py3-none-any.whl", hash = "sha256:255538574d6cb6d0a79a17ec8bc0d30985913b87513a01cce8bcdb6b4c44d0e2", size = 30869, upload-time = "2025-09-24T05:25:28.912Z" },
+]
+
 [[package]]
 name = "fonttools"
 version = "4.60.1"
@@ -393,6 +425,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175, upload-time = "2025-09-29T21:13:24.134Z" },
 ]
 
+[[package]]
+name = "gast"
+version = "0.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/14/c566f5ca00c115db7725263408ff952b8ae6d6a4e792ef9c84e77d9af7a1/gast-0.6.0.tar.gz", hash = "sha256:88fc5300d32c7ac6ca7b515310862f71e6fdf2c029bbec7c66c0f5dd47b6b1fb", size = 27708, upload-time = "2024-06-27T20:31:49.527Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a3/61/8001b38461d751cd1a0c3a6ae84346796a5758123f3ed97a1b121dfbf4f3/gast-0.6.0-py3-none-any.whl", hash = "sha256:52b182313f7330389f72b069ba00f174cfe2a06411099547288839c6cbafbd54", size = 21173, upload-time = "2024-07-09T13:15:15.615Z" },
+]
+
 [[package]]
 name = "geopandas"
 version = "1.1.1"
@@ -434,6 +475,41 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/01/61/d4b89fec821f72385526e1b9d9a3a0385dda4a72b206d28049e2c7cd39b8/gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77", size = 208168, upload-time = "2025-07-24T03:45:52.517Z" },
 ]
 
+[[package]]
+name = "google-pasta"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/35/4a/0bd53b36ff0323d10d5f24ebd67af2de10a1117f5cf4d7add90df92756f1/google-pasta-0.2.0.tar.gz", hash = "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e", size = 40430, upload-time = "2020-03-13T18:57:50.34Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl", hash = "sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed", size = 57471, upload-time = "2020-03-13T18:57:48.872Z" },
+]
+
+[[package]]
+name = "grpcio"
+version = "1.75.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/f7/8963848164c7604efb3a3e6ee457fdb3a469653e19002bd24742473254f8/grpcio-1.75.1.tar.gz", hash = "sha256:3e81d89ece99b9ace23a6916880baca613c03a799925afb2857887efa8b1b3d2", size = 12731327, upload-time = "2025-09-26T09:03:36.887Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/3c/35ca9747473a306bfad0cee04504953f7098527cd112a4ab55c55af9e7bd/grpcio-1.75.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:573855ca2e58e35032aff30bfbd1ee103fbcf4472e4b28d4010757700918e326", size = 5709761, upload-time = "2025-09-26T09:01:28.528Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/42/5f628abe360b84dfe8dd8f32be6b0606dc31dc04d3358eef27db791ea4d5/grpcio-1.75.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0049a7bf547dafaeeb1db17079ce79596c298bfe308fc084d023c8907a845b9a", size = 6470166, upload-time = "2025-09-26T09:01:39.474Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/b6/4bf9aacff45deca5eac5562547ed212556b831064da77971a4e632917da3/grpcio-1.75.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b10ad908118d38c2453ade7ff790e5bce36580c3742919007a2a78e3a1e521ca", size = 7503290, upload-time = "2025-09-26T09:01:49.28Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/81/42be79e73a50aaa20af66731c2defeb0e8c9008d9935a64dd8ea8e8c44eb/grpcio-1.75.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:7b888b33cd14085d86176b1628ad2fcbff94cfbbe7809465097aa0132e58b018", size = 5668314, upload-time = "2025-09-26T09:01:55.424Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/9c/eda9fe57f2b84343d44c1b66cf3831c973ba29b078b16a27d4587a1fdd47/grpcio-1.75.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7d4fa6ccc3ec2e68a04f7b883d354d7fea22a34c44ce535a2f0c0049cf626ddf", size = 6435419, upload-time = "2025-09-26T09:02:05.055Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/7c/48455b2d0c5949678d6982c3e31ea4d89df4e16131b03f7d5c590811cbe9/grpcio-1.75.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3652516048bf4c314ce12be37423c79829f46efffb390ad64149a10c6071e8de", size = 7466181, upload-time = "2025-09-26T09:02:12.279Z" },
+    { url = "https://files.pythonhosted.org/packages/46/74/bac4ab9f7722164afdf263ae31ba97b8174c667153510322a5eba4194c32/grpcio-1.75.1-cp313-cp313-linux_armv7l.whl", hash = "sha256:3bed22e750d91d53d9e31e0af35a7b0b51367e974e14a4ff229db5b207647884", size = 5672779, upload-time = "2025-09-26T09:02:19.11Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/6f/076ac0df6c359117676cacfa8a377e2abcecec6a6599a15a672d331f6680/grpcio-1.75.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0ee119f4f88d9f75414217823d21d75bfe0e6ed40135b0cbbfc6376bc9f7757d", size = 6436149, upload-time = "2025-09-26T09:02:30.971Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/7e/bb80b1bba03c12158f9254762cdf5cced4a9bc2e8ed51ed335915a5a06ef/grpcio-1.75.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5cebe13088b9254f6e615bcf1da9131d46cfa4e88039454aca9cb65f639bd3bc", size = 7463822, upload-time = "2025-09-26T09:02:38.26Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/1b/9a0a5cecd24302b9fdbcd55d15ed6267e5f3d5b898ff9ac8cbe17ee76129/grpcio-1.75.1-cp314-cp314-linux_armv7l.whl", hash = "sha256:c05da79068dd96723793bffc8d0e64c45f316248417515f28d22204d9dae51c7", size = 5673319, upload-time = "2025-09-26T09:02:44.742Z" },
+    { url = "https://files.pythonhosted.org/packages/78/66/044d412c98408a5e23cb348845979a2d17a2e2b6c3c34c1ec91b920f49d0/grpcio-1.75.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:07a554fa31c668cf0e7a188678ceeca3cb8fead29bbe455352e712ec33ca701c", size = 6437492, upload-time = "2025-09-26T09:02:55.542Z" },
+    { url = "https://files.pythonhosted.org/packages/67/8e/3204b94ac30b0f675ab1c06540ab5578660dc8b690db71854d3116f20d00/grpcio-1.75.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:aad1c774f4ebf0696a7f148a56d39a3432550612597331792528895258966dc0", size = 7464478, upload-time = "2025-09-26T09:03:03.096Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -443,6 +519,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
 ]
 
+[[package]]
+name = "h5py"
+version = "3.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5d/57/dfb3c5c3f1bf5f5ef2e59a22dec4ff1f3d7408b55bfcefcfb0ea69ef21c6/h5py-3.14.0.tar.gz", hash = "sha256:2372116b2e0d5d3e5e705b7f663f7c8d96fa79a4052d250484ef91d24d6a08f4", size = 424323, upload-time = "2025-06-06T14:06:15.01Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/21/d4/d461649cafd5137088fb7f8e78fdc6621bb0c4ff2c090a389f68e8edc136/h5py-3.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:723a40ee6505bd354bfd26385f2dae7bbfa87655f4e61bab175a49d72ebfc06b", size = 4516618, upload-time = "2025-06-06T14:04:52.467Z" },
+    { url = "https://files.pythonhosted.org/packages/86/f9/f00de11c82c88bfc1ef22633557bfba9e271e0cb3189ad704183fc4a2644/h5py-3.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0cbd41f4e3761f150aa5b662df991868ca533872c95467216f2bec5fcad84882", size = 4929422, upload-time = "2025-06-06T14:05:18.399Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/ec/86f59025306dcc6deee5fda54d980d077075b8d9889aac80f158bd585f1b/h5py-3.14.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d90e6445ab7c146d7f7981b11895d70bc1dd91278a4f9f9028bc0c95e4a53f13", size = 4921632, upload-time = "2025-06-06T14:05:43.464Z" },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -528,6 +618,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
 ]
 
+[[package]]
+name = "keras"
+version = "3.11.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "h5py" },
+    { name = "ml-dtypes" },
+    { name = "namex" },
+    { name = "numpy" },
+    { name = "optree" },
+    { name = "packaging" },
+    { name = "rich" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6a/89/646425fe9a46f9053430e1271f817c36041c6f33469950a3caafc3d2591e/keras-3.11.3.tar.gz", hash = "sha256:efda616835c31b7d916d72303ef9adec1257320bc9fd4b2b0138840fc65fb5b7", size = 1065906, upload-time = "2025-08-21T22:08:57.643Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/5b/4c778cc921ce4b864b238f63f8e3ff6e954ab19b80c9fa680593ad8093d4/keras-3.11.3-py3-none-any.whl", hash = "sha256:f484f050e05ee400455b05ec8c36ed35edc34de94256b6073f56cfe68f65491f", size = 1408438, upload-time = "2025-08-21T22:08:55.858Z" },
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.9"
@@ -642,6 +751,17 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5d/e9/5a5ffd9b286db82be70d677d0a91e4d58f7912bb8dd026ddeeb4abe70679/language_data-1.3.0-py3-none-any.whl", hash = "sha256:e2ee943551b5ae5f89cd0e801d1fc3835bb0ef5b7e9c3a4e8e17b2b214548fbf", size = 5385760, upload-time = "2024-11-19T10:21:36.005Z" },
 ]
 
+[[package]]
+name = "libclang"
+version = "18.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6e/5c/ca35e19a4f142adffa27e3d652196b7362fa612243e2b916845d801454fc/libclang-18.1.1.tar.gz", hash = "sha256:a1214966d08d73d971287fc3ead8dfaf82eb07fb197680d8b3859dbbbbf78250", size = 39612, upload-time = "2024-03-17T16:04:37.434Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/fc/716c1e62e512ef1c160e7984a73a5fc7df45166f2ff3f254e71c58076f7c/libclang-18.1.1-py2.py3-none-manylinux2010_x86_64.whl", hash = "sha256:c533091d8a3bbf7460a00cb6c1a71da93bffe148f172c7d03b1c31fbf8aa2a0b", size = 24515943, upload-time = "2024-03-17T16:03:45.942Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/2f/d920822c2b1ce9326a4c78c0c2b4aa3fde610c7ee9f631b600acb5376c26/libclang-18.1.1-py2.py3-none-manylinux2014_armv7l.whl", hash = "sha256:cf4a99b05376513717ab5d82a0db832c56ccea4fd61a69dbb7bccf2dfb207dbe", size = 20259606, upload-time = "2024-03-17T16:17:42.437Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/c2/de1db8c6d413597076a4259cea409b83459b2db997c003578affdd32bf66/libclang-18.1.1-py2.py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:69f8eb8f65c279e765ffd28aaa7e9e364c776c17618af8bff22a8df58677ff4f", size = 24921494, upload-time = "2024-03-17T16:14:20.132Z" },
+]
+
 [[package]]
 name = "lightgbm"
 version = "4.6.0"
@@ -715,6 +835,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9a/61/c4efc044141429e67e8fd5536be86d76303f250179c7f92b2cc0c72e8d0b/marisa_trie-1.3.1-cp314-cp314t-win_amd64.whl", hash = "sha256:9e6496bbad3068e3bbbb934b1e1307bf1a9cb4609f9ec47b57e8ea37f1b5ee40", size = 162564, upload-time = "2025-08-26T15:13:06.112Z" },
 ]
 
+[[package]]
+name = "markdown"
+version = "3.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8d/37/02347f6d6d8279247a5837082ebc26fc0d5aaeaf75aa013fcbb433c777ab/markdown-3.9.tar.gz", hash = "sha256:d2900fe1782bd33bdbbd56859defef70c2e78fc46668f8eb9df3128138f2cb6a", size = 364585, upload-time = "2025-09-04T20:25:22.885Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/70/ae/44c4a6a4cbb496d93c6257954260fe3a6e91b7bed2240e5dad2a717f5111/markdown-3.9-py3-none-any.whl", hash = "sha256:9f4d91ed810864ea88a6f32c07ba8bee1346c0cc1f6b1f9f6c822f2a9667d280", size = 107441, upload-time = "2025-09-04T20:25:21.784Z" },
+]
+
 [[package]]
 name = "markdown-it-py"
 version = "4.0.0"
@@ -874,6 +1003,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
 ]
 
+[[package]]
+name = "ml-dtypes"
+version = "0.5.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/78/a7/aad060393123cfb383956dca68402aff3db1e1caffd5764887ed5153f41b/ml_dtypes-0.5.3.tar.gz", hash = "sha256:95ce33057ba4d05df50b1f3cfefab22e351868a843b3b15a46c65836283670c9", size = 692316, upload-time = "2025-07-29T18:39:19.454Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/dc/72992b68de367741bfab8df3b3fe7c29f982b7279d341aa5bf3e7ef737ea/ml_dtypes-0.5.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c3f5ae0309d9f888fd825c2e9d0241102fadaca81d888f26f845bc8c13c1e4ee", size = 4938435, upload-time = "2025-07-29T18:38:29.193Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/a9/b98b86426c24900b0c754aad006dce2863df7ce0bb2bcc2c02f9cc7e8489/ml_dtypes-0.5.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1b255acada256d1fa8c35ed07b5f6d18bc21d1556f842fbc2d5718aea2cd9e55", size = 4928805, upload-time = "2025-07-29T18:38:38.29Z" },
+    { url = "https://files.pythonhosted.org/packages/14/f3/091ba84e5395d7fe5b30c081a44dec881cd84b408db1763ee50768b2ab63/ml_dtypes-0.5.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6936283b56d74fbec431ca57ce58a90a908fdbd14d4e2d22eea6d72bb208a7b7", size = 4933109, upload-time = "2025-07-29T18:38:46.405Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/38/6266604dffb43378055394ea110570cf261a49876fc48f548dfe876f34cc/ml_dtypes-0.5.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdf40d2aaabd3913dec11840f0d0ebb1b93134f99af6a0a4fd88ffe924928ab4", size = 5285422, upload-time = "2025-07-29T18:38:56.603Z" },
+    { url = "https://files.pythonhosted.org/packages/51/66/273c2a06ae44562b104b61e6b14444da00061fd87652506579d7eb2c40b1/ml_dtypes-0.5.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c205cac07d24a29840c163d6469f61069ce4b065518519216297fc2f261f8db9", size = 4930911, upload-time = "2025-07-29T18:39:02.405Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/3c/541c4b30815ab90ebfbb51df15d0b4254f2f9f1e2b4907ab229300d5e6f2/ml_dtypes-0.5.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ab039ffb40f3dc0aeeeba84fd6c3452781b5e15bef72e2d10bcb33e4bbffc39", size = 5285284, upload-time = "2025-07-29T18:39:11.532Z" },
+]
+
 [[package]]
 name = "murmurhash"
 version = "1.0.13"
@@ -903,6 +1049,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/18/25/addbc1d28f83252732ac3e57334d42f093890b4c2cce483ba01a42bc607c/murmurhash-1.0.13-cp313-cp313-win_amd64.whl", hash = "sha256:c451a22f14c2f40e7abaea521ee24fa0e46fbec480c4304c25c946cdb6e81883", size = 24880, upload-time = "2025-05-22T12:35:47.625Z" },
 ]
 
+[[package]]
+name = "namex"
+version = "0.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0c/c0/ee95b28f029c73f8d49d8f52edaed02a1d4a9acb8b69355737fdb1faa191/namex-0.1.0.tar.gz", hash = "sha256:117f03ccd302cc48e3f5c58a296838f6b89c83455ab8683a1e85f2a430aa4306", size = 6649, upload-time = "2025-05-26T23:17:38.918Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b2/bc/465daf1de06409cdd4532082806770ee0d8d7df434da79c76564d0f69741/namex-0.1.0-py3-none-any.whl", hash = "sha256:e2012a474502f1e2251267062aae3114611f07df4224b6e06334c57b0f2ce87c", size = 5905, upload-time = "2025-05-26T23:17:37.695Z" },
+]
+
 [[package]]
 name = "narwhals"
 version = "2.6.0"
@@ -932,6 +1087,7 @@ dependencies = [
     { name = "seaborn" },
     { name = "spacy" },
     { name = "streamlit" },
+    { name = "tensorflow", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "tqdm" },
     { name = "typer" },
     { name = "xgboost" },
@@ -959,6 +1115,7 @@ requires-dist = [
     { name = "seaborn", specifier = ">=0.13.2" },
     { name = "spacy", specifier = ">=3.8.7" },
     { name = "streamlit", specifier = ">=1.50.0" },
+    { name = "tensorflow", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = "==2.20.0" },
     { name = "tqdm", specifier = ">=4.67.1" },
     { name = "typer", specifier = ">=0.19.2" },
     { name = "xgboost", specifier = ">=3.0.5" },
@@ -1070,6 +1227,47 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b5/c1/edc9f41b425ca40b26b7c104c5f6841a4537bb2552bfa6ca66e81405bb95/ollama-0.6.0-py3-none-any.whl", hash = "sha256:534511b3ccea2dff419ae06c3b58d7f217c55be7897c8ce5868dfb6b219cf7a0", size = 14130, upload-time = "2025-09-24T22:46:01.19Z" },
 ]
 
+[[package]]
+name = "opt-einsum"
+version = "3.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8c/b9/2ac072041e899a52f20cf9510850ff58295003aa75525e58343591b0cbfb/opt_einsum-3.4.0.tar.gz", hash = "sha256:96ca72f1b886d148241348783498194c577fa30a8faac108586b14f1ba4473ac", size = 63004, upload-time = "2024-09-26T14:33:24.483Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/23/cd/066e86230ae37ed0be70aae89aabf03ca8d9f39c8aea0dec8029455b5540/opt_einsum-3.4.0-py3-none-any.whl", hash = "sha256:69bb92469f86a1565195ece4ac0323943e83477171b91d24c35afe028a90d7cd", size = 71932, upload-time = "2024-09-26T14:33:23.039Z" },
+]
+
+[[package]]
+name = "optree"
+version = "0.17.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/56/c7/0853e0c59b135dff770615d2713b547b6b3b5cde7c10995b4a5825244612/optree-0.17.0.tar.gz", hash = "sha256:5335a5ec44479920620d72324c66563bd705ab2a698605dd4b6ee67dbcad7ecd", size = 163111, upload-time = "2025-07-25T11:26:11.586Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1b/e6/48a97aefd18770b55e5ed456d8183891f325cdb6d90592e5f072ed6951f8/optree-0.17.0-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1a2bd263e6b5621d000d0f94de1f245414fd5dbce365a24b7b89b1ed0ef56cf9", size = 417557, upload-time = "2025-07-25T11:24:42.396Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/b1/4e280edab8a86be47ec1f9bd9ed4b685d2e15f0950ae62b613b26d12a1da/optree-0.17.0-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:9b37daca4ad89339b1f5320cc61ac600dcf976adbb060769d36d5542d6ebfedf", size = 414174, upload-time = "2025-07-25T11:24:43.51Z" },
+    { url = "https://files.pythonhosted.org/packages/db/3b/49a9a1986215dd342525974deeb17c260a83fee8fad147276fd710ac8718/optree-0.17.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a146a6917f3e28cfdc268ff1770aa696c346482dd3da681c3ff92153d94450ea", size = 402000, upload-time = "2025-07-25T11:24:44.819Z" },
+    { url = "https://files.pythonhosted.org/packages/54/a3/64b184a79373753f4f46a5cd301ea581f71d6dc1a5c103bd2394f0925d40/optree-0.17.0-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:970ae4e47727b4c5526fc583b87d29190e576f6a2b6c19e8671589b73d256250", size = 420686, upload-time = "2025-07-25T11:24:54.212Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/6d/b6051b0b1ef9a49df96a66e9e62fc02620d2115d1ba659888c94e67fcfc9/optree-0.17.0-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54177fd3e6e05c08b66329e26d7d44b85f24125f25c6b74c921499a1b31b8f70", size = 421225, upload-time = "2025-07-25T11:24:55.213Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/f1/940bc959aaef9eede8bb1b1127833b0929c6ffa9268ec0f6cb19877e2027/optree-0.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e1959cfbc38c228c8195354967cda64887b96219924b7b3759e5ee355582c1ec", size = 408819, upload-time = "2025-07-25T11:24:56.315Z" },
+    { url = "https://files.pythonhosted.org/packages/21/04/9706d11b880186e9e9d66d7c21ce249b2ce0212645137cc13fdd18247c26/optree-0.17.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:b5995a3efce4b00a14049268a81ab0379656a41ddf3c3761e3b88937fca44d48", size = 348177, upload-time = "2025-07-25T11:25:00.999Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/4b/0415c18816818ac871c9f3d5c7c5f4ceb83baff03ed511c9c94591ace4bc/optree-0.17.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d06e8143d16fe6c0708f3cc2807b5b65f815d60ee2b52f3d79e4022c95563482", size = 354389, upload-time = "2025-07-25T11:25:02.337Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/16/0a0d6139022e9a53ecb1212fb6fbc5b60eff824371071ef5f5fa481d8167/optree-0.17.0-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ea8bef525432b38a84e7448348da1a2dc308375bce79c77675cc50a501305851", size = 423294, upload-time = "2025-07-25T11:25:08.043Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/60/2e083dabb6aff6d939d8aab16ba3dbe6eee9429597a13f3fca57b33cdcde/optree-0.17.0-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f95b81aa67538d38316b184a6ff39a3725ee5c8555fba21dcb692f8d7c39302e", size = 424633, upload-time = "2025-07-25T11:25:09.141Z" },
+    { url = "https://files.pythonhosted.org/packages/af/fd/0e4229b5fa3fd9d3c779a606c0f358ffbdfee717f49b3477facd04de2cec/optree-0.17.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e808a1125169ae90de623456ef2423eb84a8578a74f03fe48b06b8561c2cc31d", size = 414866, upload-time = "2025-07-25T11:25:10.214Z" },
+    { url = "https://files.pythonhosted.org/packages/84/17/a4833006e925c6ed5c45ceb02e65c9e9a260e70da6523858fcf628481847/optree-0.17.0-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b4c1d030ac1c881803f5c8e23d241159ae403fd00cdf57625328f282fc671ebd", size = 439198, upload-time = "2025-07-25T11:25:19.865Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/d1/c08fc60f6dfcb1b86ca1fdc0add08a98412a1596cd45830acbdc309f2cdb/optree-0.17.0-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bd7738709970acab5d963896192b63b2718be93bb6c0bcea91895ea157fa2b13", size = 439391, upload-time = "2025-07-25T11:25:20.942Z" },
+    { url = "https://files.pythonhosted.org/packages/05/8f/461e10201003e6ad6bff3c594a29a7e044454aba68c5f795f4c8386ce47c/optree-0.17.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1644bc24b6e93cafccfdeee44157c3d4ae9bb0af3e861300602d716699865b1a", size = 426555, upload-time = "2025-07-25T11:25:21.968Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/8b/2c0a38c0d0c2396d698b97216cd6814d6754d11997b6ac66c57d87d71bae/optree-0.17.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:87938255749a45979c4e331627cb33d81aa08b0a09d024368b3e25ff67f0e9f2", size = 424461, upload-time = "2025-07-25T11:25:31.116Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/77/08fda3f97621190d50762225ee8bad87463a8b3a55fba451a999971ff130/optree-0.17.0-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3432858145fd1955a3be12207507466ac40a6911f428bf5d2d6c7f67486530a2", size = 427234, upload-time = "2025-07-25T11:25:32.289Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/b5/b4f19952c36d6448c85a6ef6be5f916dd13548de2b684ab123f04b450850/optree-0.17.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5afe3e9e2f6da0a0a5c0892f32f675eb88965036b061aa555b74e6c412a05e17", size = 413863, upload-time = "2025-07-25T11:25:33.379Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/d3/8819a2d5105a240d6793d11a61d597db91756ce84da5cee08808c6b8f61f/optree-0.17.0-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:875c017890a4b5d566af5593cab67fe3c4845544942af57e6bb9dea17e060297", size = 439080, upload-time = "2025-07-25T11:25:42.605Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/ef/9dbd34dfd1ad89feb239ca9925897a14ac94f190379a3bd991afdfd94186/optree-0.17.0-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ffa5686191139f763e13445a169765c83517164bc28e60dbedb19bed2b2655f1", size = 439422, upload-time = "2025-07-25T11:25:43.672Z" },
+    { url = "https://files.pythonhosted.org/packages/86/ca/a7a7549af2951925a692df508902ed2a6a94a51bc846806d2281b1029ef9/optree-0.17.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:575cf48cc2190acb565bd2b26b6f9b15c4e3b60183e86031215badc9d5441345", size = 426579, upload-time = "2025-07-25T11:25:44.765Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/99/23b7a484da8dfb814107b20ef2c93ef27c04f36aeb83bd976964a5b69e06/optree-0.17.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:58b0a83a967d2ef0f343db7182f0ad074eb1166bcaea909ae33909462013f151", size = 404649, upload-time = "2025-07-25T11:26:09.463Z" },
+]
+
 [[package]]
 name = "packaging"
 version = "25.0"
@@ -2166,6 +2364,77 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248, upload-time = "2025-04-02T08:25:07.678Z" },
 ]
 
+[[package]]
+name = "tensorboard"
+version = "2.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "grpcio" },
+    { name = "markdown" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "protobuf" },
+    { name = "setuptools" },
+    { name = "tensorboard-data-server" },
+    { name = "werkzeug" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680, upload-time = "2025-07-17T19:20:49.638Z" },
+]
+
+[[package]]
+name = "tensorboard-data-server"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb", size = 2356, upload-time = "2023-10-23T21:23:32.16Z" },
+    { url = "https://files.pythonhosted.org/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530", size = 6590363, upload-time = "2023-10-23T21:23:35.583Z" },
+]
+
+[[package]]
+name = "tensorflow"
+version = "2.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "astunparse" },
+    { name = "flatbuffers" },
+    { name = "gast" },
+    { name = "google-pasta" },
+    { name = "grpcio" },
+    { name = "h5py" },
+    { name = "keras" },
+    { name = "libclang" },
+    { name = "ml-dtypes" },
+    { name = "numpy" },
+    { name = "opt-einsum" },
+    { name = "packaging" },
+    { name = "protobuf" },
+    { name = "requests" },
+    { name = "setuptools" },
+    { name = "six" },
+    { name = "tensorboard" },
+    { name = "termcolor" },
+    { name = "typing-extensions" },
+    { name = "wrapt" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ff/0c/7df285ee8a88139fab0b237003634d90690759fae9c18f55ddb7c04656ec/tensorflow-2.20.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:481499fd0f824583de8945be61d5e827898cdaa4f5ea1bc2cc28ca2ccff8229e", size = 620570129, upload-time = "2025-08-13T16:51:55.104Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/d1/6aa15085d672056d5f08b5f28b1c7ce01c4e12149a23b0c98e3c79d04441/tensorflow-2.20.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:25265b0bc527e0d54b1e9cc60c44a24f44a809fe27666b905f0466471f9c52ec", size = 620682547, upload-time = "2025-08-13T16:52:46.396Z" },
+    { url = "https://files.pythonhosted.org/packages/43/fb/8be8547c128613d82a2b006004026d86ed0bd672e913029a98153af4ffab/tensorflow-2.20.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5fa3729b0126f75a99882b89fb7d536515721eda8014a63e259e780ba0a37372", size = 620815537, upload-time = "2025-08-13T16:53:42.577Z" },
+]
+
+[[package]]
+name = "termcolor"
+version = "3.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ca/6c/3d75c196ac07ac8749600b60b03f4f6094d54e132c4d94ebac6ee0e0add0/termcolor-3.1.0.tar.gz", hash = "sha256:6a6dd7fbee581909eeec6a756cff1d7f7c376063b14e4a298dc4980309e55970", size = 14324, upload-time = "2025-04-30T11:37:53.791Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4f/bd/de8d508070629b6d84a30d01d57e4a65c69aa7f5abe7560b8fad3b50ea59/termcolor-3.1.0-py3-none-any.whl", hash = "sha256:591dd26b5c2ce03b9e43f391264626557873ce1d379019786f99b0c2bee140aa", size = 7684, upload-time = "2025-04-30T11:37:52.382Z" },
+]
+
 [[package]]
 name = "thinc"
 version = "8.3.6"
@@ -2362,6 +2631,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/87/abd57374044e1f627f0a905ac33c1a7daab35a3a815abfea4e1bafd3fdb1/weasel-0.4.1-py3-none-any.whl", hash = "sha256:24140a090ea1ac512a2b2f479cc64192fd1d527a7f3627671268d08ed5ac418c", size = 50270, upload-time = "2024-05-15T08:52:52.977Z" },
 ]
 
+[[package]]
+name = "werkzeug"
+version = "3.1.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9f/69/83029f1f6300c5fb2471d621ab06f6ec6b3324685a2ce0f9777fd4a8b71e/werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746", size = 806925, upload-time = "2024-11-08T15:52:18.093Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/52/24/ab44c871b0f07f491e5d2ad12c9bd7358e527510618cb1b803a88e986db1/werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e", size = 224498, upload-time = "2024-11-08T15:52:16.132Z" },
+]
+
+[[package]]
+name = "wheel"
+version = "0.45.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/98/2d9906746cdc6a6ef809ae6338005b3f21bb568bea3165cfc6a243fdc25c/wheel-0.45.1.tar.gz", hash = "sha256:661e1abd9198507b1409a20c02106d9670b2576e916d58f520316666abca6729", size = 107545, upload-time = "2024-11-23T00:18:23.513Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl", hash = "sha256:708e7481cc80179af0e556bbf0cc00b8444c7321e2700b8d8580231d13017248", size = 72494, upload-time = "2024-11-23T00:18:21.207Z" },
+]
+
 [[package]]
 name = "wrapt"
 version = "1.17.3"