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MLmodel

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+artifact_path: /content/mlruns/1/models/m-8a7257ac0857427e9daa6ba9105c4f56/artifacts
+flavors:
+  lightgbm:
+    code: null
+    data: model.lgb
+    lgb_version: 4.6.0
+    model_class: lightgbm.basic.Booster
+    serialization_format: skops
+    skops_trusted_types:
+    - collections.OrderedDict
+    - lightgbm.sklearn.LGBMRegressor
+    - lightgbm.sklearn.LGBMClassifier
+    - lightgbm.basic.Booster
+  python_function:
+    data: model.lgb
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.lightgbm
+    python_version: 3.12.13
+mlflow_version: 3.14.0
+model_id: m-8a7257ac0857427e9daa6ba9105c4f56
+model_size_bytes: 4584488
+model_uuid: m-8a7257ac0857427e9daa6ba9105c4f56
+prompts: null
+run_id: a23d317e18624b96856fa72e526548bf
+utc_time_created: '2026-06-27 18:10:13.388419'

README.md

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+---
+language:
+- en
+- hi
+- mr
+- bn
+- ta
+- te
+tags:
+- text-classification
+- onnx
+- intent-classification
+- customer-support
+- multilingual
+datasets:
+- bitext-cs-train
+---
+
+# Agent Intent Classification Model
+
+This model is an ONNX-based classifier designed to route customer support queries to the appropriate agent. It was trained on the `bitext-cs-train.csv` dataset and supports both English and multiple Indian languages.
+
+## Preprocessing
+
+To use this model, the input text must first be converted into dense vector embeddings using the `ibm-granite/granite-embedding-97m-multilingual-r2` model from Hugging Face.
+
+```python
+from langchain_huggingface import HuggingFaceEmbeddings
+import numpy as np
+
+# 1. Load the embedding model
+embeddings_model = HuggingFaceEmbeddings(model_name="ibm-granite/granite-embedding-97m-multilingual-r2")
+
+# 2. Embed the text
+text = "I need help with my order."
+embedding = embeddings_model.embed_query(text)
+
+# 3. Convert to float32 numpy array
+X_dense = np.asarray([embedding], dtype=np.float32)
+```
+
+## Inference
+
+Once the input is preprocessed into an embedding vector, it can be passed to the ONNX model for inference.
+
+```python
+import onnxruntime as rt
+
+# 1. Load the ONNX model
+sess = rt.InferenceSession("model.onnx")
+input_name = sess.get_inputs()[0].name
+
+# 2. Run inference
+preds, probs = sess.run(None, {input_name: X_dense})
+
+# 3. Map prediction to string class
+classes = ['billing', 'order', 'product', 'shipping', 'undefined']
+predicted_class = classes[preds[0]]
+
+print(f"Predicted Agent: {predicted_class}")
+```
+
+## Classes
+
+The model predicts one of the following 5 classes:
+- `billing`
+- `order`
+- `product`
+- `shipping`
+- `undefined`
+
+
+# ONNX Model Multilingual Evaluation Report (Target: Agent)
+
+This report evaluates the ONNX model's ability to predict the `agent` column across different Indian languages and English. Embeddings used: `ibm-granite/granite-embedding-97m-multilingual-r2`.
+
+## Overall Metrics
+
+- **Accuracy**: 0.6914
+- **Precision (Weighted)**: 0.7091
+- **Recall (Weighted)**: 0.6914
+- **F1 Score (Weighted)**: 0.6874
+
+## Metrics by Language
+
+| Language | Samples | Accuracy | Precision | Recall | F1 Score |
+|---|---|---|---|---|---|
+| en | 54 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
+| hi | 54 | 0.9074 | 0.9235 | 0.9074 | 0.9055 |
+| bn | 54 | 0.7593 | 0.7661 | 0.7593 | 0.7497 |
+| te | 54 | 0.5741 | 0.5756 | 0.5741 | 0.4982 |
+| ta | 54 | 0.2407 | 0.2923 | 0.2407 | 0.2372 |
+| mr | 54 | 0.6667 | 0.6665 | 0.6667 | 0.6623 |
+
+## Production Eligibility Analysis
+
+For the model to be eligible for production in Indian languages, we expect the F1 score in the translated languages to be comparable to English. If scores are significantly lower, the ONNX model (which was likely trained on English embeddings) might not generalize well even with multilingual embeddings, or it needs fine-tuning on the multilingual embedding space.
+

conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.13
+- pip<=24.1.2
+- pip:
+  - mlflow==3.14.0
+  - bottleneck==1.4.2
+  - cupy-cuda12x==14.0.1
+  - defusedxml==0.7.1
+  - distributed-ucxx-cu12==0.48.0
+  - graphviz==0.21
+  - lightgbm==4.6.0
+  - matplotlib==3.10.0
+  - mmh3==5.2.1
+  - numpy==2.0.2
+  - optuna==4.9.0
+  - pandas==2.2.2
+  - pyarrow==18.1.0
+  - python-snappy==0.7.3
+  - scikit-learn==1.6.1
+  - scipy==1.16.3
+  - xarray==2025.12.0
+  - xxhash==3.7.0
+  - zstandard==0.25.0
+name: mlflow-env

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1a722978f645d4c7bfb49f50c4d22bffda871bf58513f6b55484ea26b34fc57e
+size 3060619

python_env.yaml

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+python: 3.12.13
+build_dependencies:
+- pip==24.1.2
+- setuptools==75.2.0
+- wheel==0.47.0
+dependencies:
+- -r requirements.txt

requirements.txt

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+mlflow==3.14.0
+bottleneck==1.4.2
+cupy-cuda12x==14.0.1
+defusedxml==0.7.1
+distributed-ucxx-cu12==0.48.0
+graphviz==0.21
+lightgbm==4.6.0
+matplotlib==3.10.0
+mmh3==5.2.1
+numpy==2.0.2
+optuna==4.9.0
+pandas==2.2.2
+pyarrow==18.1.0
+python-snappy==0.7.3
+scikit-learn==1.6.1
+scipy==1.16.3
+xarray==2025.12.0
+xxhash==3.7.0
+zstandard==0.25.0