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language:
- tr
license: apache-2.0
library_name: transformers
base_model: ytu-ce-cosmos/modernbert-tr-base
base_model_relation: finetune
pipeline_tag: token-classification
datasets:
- mrbesher/massive-tr
metrics:
- f1
tags:
- modernbert
- massive
- slot-filling
- slu
- onnx
- encoderfile
---
<p align="center">
<img src="assets/logo.webp" width="20%" alt="ModernBERT-TR MASSIVE slots" />
</p>
<h1 align="center">ModernBERT-TR MASSIVE Slot Filling</h1>
A 150M-parameter Turkish token classifier with `O` plus BIO labels for the 55 slot types in MASSIVE 1.1.
## Results
Our model scores 75.27 +/- 0.31% seqeval entity-level F1 on the MASSIVE 1.1 `tr-TR` test set. The released checkpoint scores 75.30%.
The quantized int8 version scores 75.42%.
## Usage
```python
from transformers import pipeline
fill_slots = pipeline(
"token-classification",
model="ytu-ce-cosmos/modernbert-tr-massive-slot",
aggregation_strategy="first",
)
fill_slots("önümüzdeki cuma Ankara'ya bilet bul")
```
You can call the tokenizer with `is_split_into_words=True` to allow the tokenizer to split the sentence into words, and keep the first WordPiece label for each word.
## Training
We finetune [`ytu-ce-cosmos/modernbert-tr-base`](https://huggingface.co/ytu-ce-cosmos/modernbert-tr-base) jointly with a 60-way intent head and a 111-way slot head; this repository contains the exported slot head. The human-localized MASSIVE 1.1 Turkish split has 11,514 training, 2,033 validation, and 2,974 test utterances. We use encoder learning rate `5e-5`, head learning rate `1e-4`, batch size 64, 15 epochs, linear warmup and decay, weight decay 0.01, plain slot cross-entropy, first-subword alignment, and five seeds.
Standalone binaries are available in the [encoderfile repo](https://huggingface.co/ytu-ce-cosmos/modernbert-tr-massive-slot-encoderfile).
## License
Apache-2.0. The MASSIVE dataset is distributed under CC BY 4.0.
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