---
base_model: fastino/gliner2-large-v1
library_name: peft
tags:
- base_model:adapter:fastino/gliner2-large-v1
- lora
- transformers
- token-classification
- named-entity-recognition
- finance
- wsb
- r/wallstreetbets
- reddit
- wallstreetbets
- financial-nar
- finance-ner
license: mit
language:
- en
datasets:
- StephanAkkerman/wallstreetbets-ner
metrics:
- f1
- precision
- recall
---

# stock-recognizer-model

LoRA adapter for financial named entity recognition using GLiNER2 Large. Extracts stock ticker symbols and company names from social media and financial text.

## Model Details

- **Base Model:** fastino/gliner2-large-v1
- **Adapter Type:** LoRA (r=32, α=64)
- **Task:** Named Entity Recognition (Token Classification)
- **Training Data:** 3,500+ annotated Reddit posts
- **Framework:** PEFT 0.19.1

## Overview

This adapter fine-tunes GLiNER2 Large to recognize two entity types in financial social media text:

- **ticker**: Stock market symbols ($AAPL, TSLA, gme, etc.)
- **company**: Corporate names (Apple Inc., Microsoft, Goldman Sachs, etc.)

Trained on Reddit posts (primarily r/wallstreetbets) and optimized for informal, casual financial discussions. Serves as the NER backbone for the [stock-recognizer](https://github.com/StephanAkkerman/stock-recognizer) resolution engine.

## Intended Use

Extract stock market entities from:
- Financial social media (Twitter/X, Reddit, StockTwits)
- Forum discussions and comments
- User-generated financial content
- News comments and reader discussions

The model handles ticker symbols in multiple forms: cashtags ($GME), uppercase (AMC), and informal lowercase (amc).

## Training Details

### Data

- **3,500+** manually annotated documents from r/wallstreetbets and related communities
- Labeled in [Label Studio](https://labelstud.io/)
- Train/validation split: 90/10 (stratified by task ID)
- Chunking: 150-word windows with 40-word overlap

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| LoRA Rank (r) | 32 |
| LoRA Alpha (α) | 64 |
| LoRA Dropout | 0.1 |
| Epochs | 10 (with early stopping) |
| Batch Size | 4 (gradient accumulation: 2) |
| Max Seq Length | 256 |
| Encoder LR | 2e-5 |
| Task LR | 5e-4 |
| Precision | bfloat16 |
| Target Modules | key_proj, value_proj, query_proj, dense |

## Benchmark Results

Evaluated on held-out test set (500+ documents) using set-based, deduplicated scoring:

| Metric | Score |
|--------|-------|
| Precision | 82% |
| Recall | 78% |
| F1 | 80% |

**Scoring note:** Set-based evaluation counts each entity type as "found" once per document, regardless of mention frequency. This reflects the engine's public API, which returns deduplicated sets of entities.

## Usage

### Load with GLiNER2

```python
from gliner2 import GLiNER2

# Load base model
model = GLiNER2.from_pretrained("fastino/gliner2-large-v1")

# Load the LoRA adapter
model.load_adapter("StephanAkkerman/stock-recognizer-model", revision="v18")

# Inference
text = "$GME is mooning but Apple Inc. might crash tomorrow"
entities = model.predict_entities(text, ["ticker", "company"])

for entity in entities:
    print(f"{entity['text']}: {entity['label']} (score: {entity['score']:.2f})")
```

## Engine Integration
This adapter is automatically loaded by stock-recognizer when calling recognize_ai(). The engine handles entity extraction, resolution, and deduplication.

## Known Limitations
Social media bias: Trained on Reddit; performance on news, research, or formal text may differ
Boundary mismatches: Occasional off-by-one errors on multi-word entities
Rare tickers: Low-frequency emerging companies may be missed
Out-of-vocabulary names: Unseen company names may be mislabeled
No resolution: Extracts entities but does not resolve ambiguous symbols (e.g., AA → Alcoa or American Airlines)

## License
Refer to the base model (fastino/gliner2-large-v1) for licensing terms. Training data subject to Reddit's terms of service.

## Citation
```bibtex
@misc{stock_recognizer_v18,
  author = {Akkerman, Stephan},
  title = {Stock Recognizer Model: LoRA Adapter for Financial NER},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/StephanAkkerman/stock-recognizer-model}},
  note = {Revision v18}
}
```

## Repositories
Adapter Training: [stock-recognizer-model](https://github.com/StephanAkkerman/stock-recognizer-model)
Engine / Inference: [stock-recognizer](https://github.com/StephanAkkerman/stock-recognizer)