Update README.md

README.md

@@ -1,3 +1,107 @@
 ---
 license: mit
 ---
+---
+language: en
+tags:
+  - text-classification
+  - onnx
+  - job-classification
+  - it
+license: mit
+base_model: intfloat/e5-base-v2
+---
+
+# IT vs Non-IT Job Title Classifier
+
+Binary classifier that determines whether a job title belongs to an IT/tech role or not. Built on top of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) embeddings with a logistic regression head, exported to ONNX for fast, lightweight inference with no heavy ML dependencies at runtime.
+
+## Repository contents
+
+| File | Description |
+|---|---|
+| `e5_it_classifier.onnx` | Logistic regression classifier head (ONNX) |
+
+The encoder (`intfloat/e5-base-v2`) is loaded separately at inference time — it is not bundled here since it is a public model.
+
+## How it works
+
+1. The job title is prefixed with `"query: "` — required by the e5-v2 instruction format
+2. The prefixed title is encoded by `intfloat/e5-base-v2` with mean pooling and L2 normalization, producing a 768-dim embedding
+3. The embedding is passed through the logistic regression ONNX model
+4. The output is a probability for class `1` (IT) and class `0` (Non-IT)
+
+## Training
+
+- **Encoder:** `intfloat/e5-base-v2` via `sentence-transformers`, embeddings L2-normalized
+- **Classifier:** `sklearn.linear_model.LogisticRegression(C=1.0, max_iter=1000, class_weight='balanced')`
+- **Input:** job title only
+- **Labels:** `1` = IT role, `0` = Non-IT role
+- **Class balancing:** enabled via `class_weight='balanced'` to handle uneven label distribution
+
+## Inference
+
+### Python
+
+```python
+from sentence_transformers import SentenceTransformer
+import onnxruntime as ort
+import numpy as np
+
+encoder = SentenceTransformer("intfloat/e5-base-v2")
+sess = ort.InferenceSession("e5_it_classifier.onnx")
+
+def classify(title: str) -> dict:
+    emb = encoder.encode(["query: " + title], normalize_embeddings=True)
+    probs = sess.run(["probabilities"], {"input": emb.astype(np.float32)})[0]
+    return {
+        "label": "IT" if probs[0][1] > probs[0][0] else "Non-IT",
+        "it_probability": float(probs[0][1]),
+    }
+
+print(classify("Senior Software Engineer"))  # IT
+print(classify("Regional Sales Manager"))    # Non-IT
+```
+
+### JavaScript / TypeScript (Bun or Node)
+
+```typescript
+import { pipeline } from "@huggingface/transformers";
+import * as ort from "onnxruntime-node";
+
+const extractor = await pipeline("feature-extraction", "intfloat/e5-base-v2", { quantized: false });
+const session = await ort.InferenceSession.create("./e5_it_classifier.onnx");
+
+async function classify(title: string) {
+  const output = await extractor("query: " + title, { pooling: "mean", normalize: true });
+
+  const results = await session.run({
+    input: new ort.Tensor("float32", output.data as Float32Array, [1, 768]),
+  });
+
+  const probs = results.probabilities.data as Float32Array;
+  return {
+    label: probs[1] > probs[0] ? "IT" : "Non-IT",
+    it_probability: probs[1],
+  };
+}
+
+console.log(await classify("Senior Software Engineer")); // IT
+console.log(await classify("Regional Sales Manager"));   // Non-IT
+```
+
+```bash
+bun add @huggingface/transformers onnxruntime-node
+# or
+npm install @huggingface/transformers onnxruntime-node
+```
+
+## Intended use
+
+Designed for automated job pipeline filtering — quickly classifying job titles as IT or non-IT before downstream enrichment or processing steps. Works well as a lightweight pre-filter given that it only requires a job title with no description needed.
+
+## Limitations
+
+- Trained and evaluated on job title text only — unusual or highly abbreviated titles may score less reliably
+- English job titles only
+- Edge cases like hybrid roles (e.g. "IT Sales Manager") may produce probabilities close to 0.5