| --- |
| 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) |
