| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - zero-shot-classification |
| - nli |
| - cross-encoder |
| - intent-routing |
| - modernbert |
| pipeline_tag: zero-shot-classification |
| widget: |
| - text: Generate a formal project proposal in Word format. |
| candidate_labels: a Word document creation request, an Excel spreadsheet creation |
| request, a PowerPoint presentation deck creation request, a PDF document analysis |
| query, general chitchat |
| --- |
| |
| # MarkOne |
|
|
| **MarkOne** is a high-precision, zero-shot NLI cross-encoder model powered by **ModernBERT** designed for dynamic intent routing and query classification within modular AI architectures. |
|
|
| It evaluates user prompts against arbitrary numbers of candidate choice labels (N = 3, 6, 8,..) by computing sequence pair entailment probabilities using modern attention mechanics and an expanded context window. |
|
|
| --- |
|
|
| ## Key Features |
|
|
| - **ModernBERT Backbone:** Built on ModernBERT architecture featuring FlashAttention, unpadded processing, and Rotary Positional Embeddings (RoPE) for blazing-fast inference. |
| - **Extended Context Window:** Natively supports up to 8,192 tokens, allowing for rich, detailed candidate descriptions and long-form document inputs. |
| - **Dynamic Choice Counts:** Handles variable numbers of candidate labels on the fly across different routing domains. |
| - **Cross-Encoder Accuracy:** Outperforms single-text classifiers by evaluating joint attention over `(Premise, Hypothesis)` pairs. |
|
|
| --- |
|
|
| ## Quickstart & Usage |
|
|
| MarkOne operates as an NLI cross-encoder, evaluating candidate choices by pairing the input prompt as a Premise with a set of generated candidate Hypotheses. |
|
|
| ### Zero-Shot NLI Classification (Python) |
|
|
| ```python |
| import json |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| MODEL_ID = "ChandruK/MarkOne" |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID).to(device) |
| model.eval() |
| |
| ENTAILMENT_IDX = 1 |
| |
| CANDIDATE_LABELS = [ |
| "a Word document creation request", |
| "an Excel spreadsheet creation request", |
| "a PowerPoint presentation deck creation request", |
| "a PDF document analysis or extraction query", |
| "general chitchat or off-topic conversation", |
| "a Question answer related", |
| ] |
| HYPOTHESIS_TEMPLATE = "This user prompt is {}." |
| |
| prompt = "Generate a formal project proposal in Word format." |
| |
| hypotheses = [HYPOTHESIS_TEMPLATE.format(label) for label in CANDIDATE_LABELS] |
| premises = [prompt] * len(CANDIDATE_LABELS) |
| |
| inputs = tokenizer( |
| text=premises, |
| text_pair=hypotheses, |
| padding=True, |
| truncation=True, |
| max_length=1024, |
| return_tensors="pt", |
| ).to(device) |
| |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
| entailment_logits = logits[:, ENTAILMENT_IDX] if logits.shape[-1] >= 3 else logits[:, 1] |
| |
| probabilities = F.softmax(entailment_logits / 0.5, dim=-1) |
| top_prob, top_idx = torch.max(probabilities, dim=-1) |
| |
| predicted_label = CANDIDATE_LABELS[top_idx.item()] |
| confidence = top_prob.item() * 100 |
| |
| label_scores = { |
| label: round(prob.item() * 100, 2) |
| for label, prob in zip(CANDIDATE_LABELS, probabilities) |
| } |
| |
| output_json_data = { |
| "prompt": prompt, |
| "predicted_label": predicted_label, |
| "confidence": round(confidence, 2), |
| "all_scores": label_scores, |
| } |
| |
| json_output = json.dumps(output_json_data, indent=2) |
| print(json_output) |
