license: apache-2.0
language:
- en
pipeline_tag: text-classification
base_model: convaiinnovations/laya
base_model_relation: adapter
datasets:
- zeroshot/twitter-financial-news-topic
tags:
- ariadne
- laya
- adapter
- custom-code
Ariadne-Laya-FinanceTopics
Classifies financial-news text into 20 topics. This 4.2 MB specialist interface runs on a shared frozen Laya base. Switching between compatible Ariadne specialists replaces about 1 million parameters, while the 421 million parameter base stays in memory. Each specialist uses its own small interface.
Use
Install the included Python wheel from this downloaded model folder:
pip install ./ariadne_specialists-0.2.0a1-py3-none-any.whl
import ariadne
from ariadne.specialists import Finance
model = ariadne.load_specialist(Finance, model=".")
result = model("The central bank raised interest rates.")
print(result.label, result.score)
The base downloads automatically and is cached. Pass device="cpu" or device="cuda:1" to choose a device. A list of texts returns a list of predictions. Scores have not been recalibrated for this task.
Load from Hugging Face
After installing the included wheel, you can load this repository directly:
import ariadne
from ariadne.specialists import Finance
model = ariadne.load_specialist(Finance, model="GoatHerder/Ariadne-Laya-FinanceTopics")
Use the explicit model= argument with this preview wheel. The interface and pinned base are downloaded automatically and cached. Pass revision="<commit hash>" to pin a particular interface version.
Interface
The interface is a 1,024 × 1,024 linear projection plus a 1,024-element bias: 1,049,600 trainable parameters. It sits after the base's native embeddings and before encoder block 0. It starts as the identity; training updates only this projection. The shared base has 421,293,827 parameters. Compatible specialists share one resident base in the same Python process and on the same device.
Results
All local results below use the same 4,117 requests and label task.
| Model | Accuracy | Macro-F1 |
|---|---|---|
| Base Laya | 25.97% | 31.09% |
| Ariadne Finance | 84.31% | 82.56% |
| leonas5555/finnews-topic-single-classify | 90.82% | 89.99% |
| TF-IDF + logistic regression | 77.80% | 72.45% |
The public fine-tune's training overlap with these cases is unknown, so this table does not establish a common unseen-test ranking. Full results and existing-task retention are in metrics.json.
Training and scope
Trained on zeroshot/twitter-financial-news-topic, revision acbc8af2a35ccf0916124efcbe9e6cf25f191012. One seed (0); epoch 2 selected by validation loss. LR 1e-4; only the 1,049,600 interface parameters were trained. The base embeddings, 28 encoder blocks and decision heads stayed frozen.
English, single-topic inputs and the labels in task.json. Results cover this financial-news corpus; performance on later news and different sources remains untested. Unsupported or ambiguous requests still receive a label. Source datasets retain their own licences.
Base revision: 55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851. Independent adaptation; no affiliation with the original Laya authors.