Update README.md

README.md

@@ -23,6 +23,113 @@ configs:
   - split: test
     path: main/test-*
 ---
-# Dataset Card for "Numclaim"
+## Dataset Card for “NumClaim: Numerical Claim Detection in Finance”
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Table of Contents
+1. [Dataset Description](#dataset-description)  
+2. [Supported Tasks](#supported-tasks)  
+3. [Dataset Structure](#dataset-structure)  
+4. [Data Fields](#data-fields)  
+5. [Data Splits](#data-splits)  
+6. [Dataset Creation](#dataset-creation)  
+7. [Usage](#usage)  
+8. [License](#license)  
+9. [Citation](#citation)  
+
+---
+
+### Dataset Description  
+
+**NumClaim** is a sentence‑level corpus for detecting **numerical claims** in financial text.  
+A sentence is labelled as **`INCLAIM`** if it expresses a forward‑looking or speculative financial forecast, and **`OUTOFCLAIM`** if it states factual past or present information.:contentReference[oaicite:0]{index=0} The dataset combines analyst reports and earnings‑call transcripts, enabling research on the influence of numerical forecasts on market reactions.:contentReference[oaicite:1]{index=1}  
+
+---
+
+### Supported Tasks  
+
+| Task | Objective | Typical Metrics |
+|------|-----------|-----------------|
+| **Numerical Claim Classification** | Classify a sentence as `INCLAIM` or `OUTOFCLAIM`. | Accuracy, Precision, Recall, F1 |
+| **Optimism Scoring** | Produce a continuous optimism score derived from claim likelihood (research use‑case in paper). | Spearman / Pearson correlation with returns |
+
+---
+
+### Dataset Structure  
+
+```yaml
+configs:
+  - config_name: main
+    data_files:
+      - split: train
+        path: main/train-*
+      - split: test
+        path: main/test-*
+dataset_info:
+  features:
+    - context: string   # Financial sentence
+    - response: string  # Label: INCLAIM / OUTOFCLAIM
+  splits:
+    - name: train
+      num_examples: 2_144
+      num_bytes:    317_988
+    - name: test
+      num_examples:   537
+      num_bytes:     79_011
+  download_size: 208_114
+  dataset_size: 396_999
+```
+
+### Data Fields  
+
+| Field   | Type   | Description                                                       |
+|---------|--------|-------------------------------------------------------------------|
+| context | string | Sentence from an analyst report or earnings call.                 |
+| response| string | `INCLAIM` or `OUTOFCLAIM` label.                                  |
+
+---
+
+### Data Splits  
+
+| Split | # Sentences | Portion |
+|-------|------------:|--------:|
+| Train | 2 144       | 80 %    |
+| Test  |   537       | 20 %    |
+| **Total** | **2 681** | **100 %** |
+
+---
+
+### Dataset Creation  
+
+- **Source Collection** – Analyst reports (Thomson Reuters) and quarterly earnings‑call transcripts for U.S. public firms (2010 – 2023).  
+- **Sentence Filtering** – Retained only sentences containing a financial term, numeric value, and currency/percentage symbol.  
+- **Annotation** – Weak‑supervision rules augmented with subject‑matter‑expert knowledge produced initial labels; a subset was manually validated.  
+- **Quality Control** – Manual spot‑checks achieved > 0.9 label accuracy, and noisy sentences were removed.  
+
+---
+
+### Usage  
+
+```python
+from datasets import load_dataset
+
+numclaim = load_dataset("numclaim", "main")  # replace with actual repo ID
+sample = numclaim["train"][0]
+
+print(sample["context"])
+print(sample["response"])
+```
+
+### License
+Released under Creative Commons Attribution 4.0 International (CC BY 4.0).
+
+### Citation
+``` bibtex
+
+@inproceedings{shah2024numclaim,
+  title     = {Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis},
+  author    = {Shah, Agam and Hiray, Arnav and Shah, Pratvi and Banerjee, Arkaprabha and Singh, Anushka and Eidnani, Dheeraj and Chava, Sahasra and Chaudhury, Bhaskar and Chava, Sudheer},
+  booktitle = {Findings of the Association for Computational Linguistics},
+  year      = {2024},
+  url       = {https://arxiv.org/abs/2402.11728}
+}
+```