Update README.md

README.md

@@ -6,4 +6,64 @@ base_model:
 - loyoladatamining/task-classifier-mini-improved2
 pipeline_tag: text-classification
 ---
-Improved version of `task-classifier-mini-improved2`, fine-tuned on more curated examples from a large job postings corpus.
+
+# task-classifier-mini-v3
+
+task-classifier-mini-v3 is an efficient, lightweight binary sequence classification model designed to identify texts that contain task statements (i.e., to be peformed in a work role). Built on top of `prajjwal1/bert-tiny`, it is optimized for high-speed, high-throughput filtering pipelines.
+
+This particular version is an improved iteration of `task-classifier-mini-improved2`, fine-tuned on more curated examples from a large job postings corpus. We include validation results below.
+
+## Basic Usage
+
+You can easily use this model with the standard Hugging Face text-classification pipeline.
+
+```python
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "loyoladatamining/task-classifier-mini-v3"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, max_length=64, truncation=True)
+
+# Create text classification pipeline
+nlp = pipeline(
+    "text-classification", 
+    model=model, 
+    tokenizer=tokenizer,
+    max_length=64,
+    truncation=True
+)
+
+# Inference
+text = "Manage and maintain the internal database servers on a weekly basis."
+result = nlp(text)
+print(result)
+```
+
+## Output Format
+
+The model returns a list containing a single classification result with the predicted binary label and its associated confidence score:
+
+```json
+[
+  {
+    "label": "LABEL_1",
+    "score": 0.9845
+  }
+]
+```
+
+### Label Mapping
+ - `LABEL_0`: The text is not a valid task statement.
+ - `LABEL_1`: The text is a task statement.
+
+## Citation
+
+If you find this model useful in your work, please consider citing:
+
+```
+@article{meisenbacher2025extracting,
+  title={Extracting O* NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data},
+  author={Meisenbacher, Stephen and Nestorov, Svetlozar and Norlander, Peter},
+  year={2025}
+}
+```