---
dataset_info:
  config_name: main
  features:
  - name: context
    dtype: string
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 317988
    num_examples: 2144
  - name: test
    num_bytes: 79011
    num_examples: 537
  download_size: 208114
  dataset_size: 396999
configs:
- config_name: main
  data_files:
  - split: train
    path: main/train-*
  - split: test
    path: main/test-*
---
## Dataset Card for “NumClaim: Numerical Claim Detection in Finance”

### 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("gtfintechlab/Numclaim")
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}
}
```