Text Classification
Transformers
Safetensors
English
modernbert
feature-extraction
decision-model
decision-transformer
ettin
cross-encoder
zero-shot-classification
classification
calibrated-probabilities
routing
multiple-choice
tool-calling
agent-decision
text-embeddings-inference
Instructions to use lCREEl/pick-150m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lCREEl/pick-150m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lCREEl/pick-150m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lCREEl/pick-150m") model = AutoModel.from_pretrained("lCREEl/pick-150m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: clarify Evaluated Questions (440 = 290 JevAlt + 150 Fast) and exact calibrated metrics
Browse files
README.md
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@@ -60,22 +60,24 @@ PICK-150M was evaluated across 16 standardized decision benchmarks spanning agen
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### 🧠 Document Verification, NLI & Causal Reasoning
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| Benchmark Task | Domain / Task Type | Metric | Score (Raw) | Score (Calibrated) | Notes |
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| :--- | :--- | :---: | :---: | :---: | :--- |
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| **ContractNLI** | Legal Contract Clause Verification | Macro-F1 | **77.
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| **RAGTruth** | LLM Output Hallucination Detection | Class-F1 | **72.
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| **HoVer** | Multi-Hop Evidence Claim Verification | Accuracy | **71.
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| **NLI4CT** | Clinical Trial Protocol Inference | Macro-F1 | **69.
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| **CLadder** | Formal Causal Graph & Rung 2/3 Reasoning | Accuracy | **61.
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| **HellaSwag** | Commonsense Situation Completion | Accuracy | **44.
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| **ACOS** | Quadruple Aspect-Category Sentiment | Per-Review F1 | **19.
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| **Overall Suite Mean** | **16-Benchmark Standardized Mean** | **Mean** | **68.
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### 🎯 Held-Out Zero-Shot Generalization (Unseen Datasets)
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| :--- | :---: | :---: | :---: | :---: |
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| **All Heldout** | 200 / 200 | **
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| **JevAlt-en (General Decisions)** | 145 / 145 | **
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| **Fast-Decisions (Domain Routing)** | 55 / 55 | **
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### 🧠 Document Verification, NLI & Causal Reasoning
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| Benchmark Task | Domain / Task Type | Metric | Score (Raw) | Score (Calibrated) | Notes |
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| :--- | :--- | :---: | :---: | :---: | :--- |
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| **ContractNLI** | Legal Contract Clause Verification | Macro-F1 | **77.81%** | **77.31%** | Full-contract + clause reasoning |
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| **RAGTruth** | LLM Output Hallucination Detection | Class-F1 | **72.00%** | **70.27%** | Response-level grounding check |
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| **HoVer** | Multi-Hop Evidence Claim Verification | Accuracy | **71.00%** | **71.00%** | Cross-document fact verification |
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| **NLI4CT** | Clinical Trial Protocol Inference | Macro-F1 | **69.85%** | **67.91%** | Medical protocol reasoning |
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| **CLadder** | Formal Causal Graph & Rung 2/3 Reasoning | Accuracy | **61.00%** | **62.00%** | Interventional & counterfactual |
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| **HellaSwag** | Commonsense Situation Completion | Accuracy | **44.00%** | **44.00%** | 4-choice situational completion |
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| **ACOS** | Quadruple Aspect-Category Sentiment | Per-Review F1 | **19.56%** | **33.20%** | Multi-quad aspect extraction |
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| **Overall Suite Mean** | **16-Benchmark Standardized Mean** | **Mean** | **68.84%** | **69.83%** | **1,873 total requests (Cal. Peak: 70.09%)** |
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### 🎯 Held-Out Zero-Shot Generalization (Unseen Datasets)
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*Note: In Decision Index, accuracy and Brier score are computed at the **question level** (`Answered Questions`), as each request state contains multiple packed questions.*
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| Evaluation Suite | Completed Requests | Evaluated Questions | Accuracy (Hits / N) | Brier Score |
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| **All Heldout** | 200 / 200 | **440** | **72.05%** (317 / 440) | **0.3892** |
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| **JevAlt-en (General Decisions)** | 145 / 145 | **290** | **82.07%** (238 / 290) | **0.2541** |
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| **Fast-Decisions (Domain Routing)** | 55 / 55 | **150** | **52.67%** (79 / 150) | **0.6502** |
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