Update README.md
Browse files
README.md
CHANGED
|
@@ -20,9 +20,11 @@ base_model:
|
|
| 20 |
- ProsusAI/finbert
|
| 21 |
---
|
| 22 |
# FinBERT-GICS Sector Classifier v3
|
| 23 |
-
[](https://medium.com/@ajmm030407/ner-prefixed-transformers-a-new-approach-to-financial-sector-classification-at-scale-1abe8fa8a65e)
|
| 24 |
|
| 25 |
|
|
|
|
|
|
|
|
|
|
| 26 |
## Model Summary
|
| 27 |
|
| 28 |
**finbert-gics-sector-classifier-v3** is the strongest performer in a family of GICS sector classification models developed to categorize financial news headlines into their corresponding Global Industry Classification Standard (GICS) sectors. While based on the financial language understanding of **ProsusAI/finbert**, this variant introduces a richer input representation by **prefixing each headline with Named Entity Recognition (NER) tags**, rather than embedding them inside the text.
|
|
@@ -31,9 +33,6 @@ By adding prefix tokens such as `[ORG]` (organizations), `[LOC]` (locations), `[
|
|
| 31 |
|
| 32 |
Among all variants, **finbert-gics-sector-classifier-v3 consistently delivers the most context-aware and reliable predictions**, making it well-suited for automated dataset labeling, financial research workflows, sentiment pipelines, and sector-aware market analysis applications.
|
| 33 |
|
| 34 |
-
## Medium Article
|
| 35 |
-
<https://medium.com/@ajmm030407/ner-prefixed-transformers-a-new-approach-to-financial-sector-classification-at-scale-1abe8fa8a65e>
|
| 36 |
-
|
| 37 |
## Intended Use
|
| 38 |
|
| 39 |
This model is designed for **classifying financial news headlines into GICS sectors** to support downstream analytics, dataset preparation, and real-time financial NLP applications. It is especially useful when building time-series models, sector sentiment indicators, or large-scale automated labeling pipelines.
|
|
|
|
| 20 |
- ProsusAI/finbert
|
| 21 |
---
|
| 22 |
# FinBERT-GICS Sector Classifier v3
|
|
|
|
| 23 |
|
| 24 |
|
| 25 |
+
[](https://medium.com/@ajmm030407/ner-prefixed-transformers-a-new-approach-to-financial-sector-classification-at-scale-1abe8fa8a65e)
|
| 26 |
+
Click the Image to read the full Medium article
|
| 27 |
+
|
| 28 |
## Model Summary
|
| 29 |
|
| 30 |
**finbert-gics-sector-classifier-v3** is the strongest performer in a family of GICS sector classification models developed to categorize financial news headlines into their corresponding Global Industry Classification Standard (GICS) sectors. While based on the financial language understanding of **ProsusAI/finbert**, this variant introduces a richer input representation by **prefixing each headline with Named Entity Recognition (NER) tags**, rather than embedding them inside the text.
|
|
|
|
| 33 |
|
| 34 |
Among all variants, **finbert-gics-sector-classifier-v3 consistently delivers the most context-aware and reliable predictions**, making it well-suited for automated dataset labeling, financial research workflows, sentiment pipelines, and sector-aware market analysis applications.
|
| 35 |
|
|
|
|
|
|
|
|
|
|
| 36 |
## Intended Use
|
| 37 |
|
| 38 |
This model is designed for **classifying financial news headlines into GICS sectors** to support downstream analytics, dataset preparation, and real-time financial NLP applications. It is especially useful when building time-series models, sector sentiment indicators, or large-scale automated labeling pipelines.
|