MWirelabs
/

nyishibert

@@ -1,199 +1,224 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+language:
+- njz
+license: cc-by-4.0
+tags:
+- fill-mask
+- masked-lm
+- nyishi
+- low-resource
+- northeast-india
+- sino-tibetan
+datasets:
+- wmt25
+metrics:
+- perplexity
 library_name: transformers
+pipeline_tag: fill-mask
 ---
+[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-yellow)](https://huggingface.co/MWireLabs/nyishibert)
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey)](https://creativecommons.org/licenses/by/4.0/)
+![Language](https://img.shields.io/badge/Language-Nyishi%20(njz)-blue)
+![Architecture](https://img.shields.io/badge/Architecture-ModernBERT--Base-purple)
+![Task](https://img.shields.io/badge/Task-Masked%20Language%20Modeling-green)
+![Low Resource](https://img.shields.io/badge/NLP-Low--Resource-orange)
+![Region](https://img.shields.io/badge/Region-Northeast%20India-brightgreen)
+# NyishiBERT
+NyishiBERT is a monolingual masked language model for Nyishi (njz-Latn), a Sino-Tibetan language spoken in Northeast India. A transformer-based language model for the Nyishi language.
 ## Model Details
 ### Model Description
+- **Developed by:** MWire Labs
+- **Model type:** Masked Language Model (MLM)
+- **Language:** Nyishi (ISO 639-3: njz, Roman script)
+- **License:** CC-BY-4.0
+- **Base architecture:** ModernBERT-Base
+- **Parameters:** 149M
+- **Training data:** 55,870 sentences from WMT EMNLP 2025 (WMT25)
+### Model Architecture
+```
+Architecture: ModernBERT-Base
+- Parameters: 149M
+- Layers: 22
+- Hidden size: 768
+- Attention heads: 12
+- Context window: 1024 tokens
+- Positional embeddings: RoPE (Rotary Position Embeddings)
+- Normalization: Pre-LayerNorm
+```
+### Training Details
+**Training Data:**
+- Source: WMT EMNLP 2025 (Tenth Conference on Machine Translation)
+- Total sentences: 55,870
+- Training split: 44,696 sentences (80%)
+- Validation split: 5,587 sentences (10%)
+- Test split: 5,587 sentences (10%)
+- Script: Roman (njz-Latn)
+**Training Configuration:**
+- Objective: Masked Language Modeling (15% masking probability)
+- Optimizer: AdamW
+- Learning rate: 2e-5 (linear warmup + decay)
+- Warmup ratio: 10%
+- Batch size: 16 (effective)
+- Training epochs: 10
+- Total steps: 27,940
+- Precision: bfloat16
+- Hardware: 1× NVIDIA A40 (48GB)
+- Training time: ~1.7 hours
+**Tokenization:**
+- Tokenizer: SentencePiece Unigram tokenizer shared with NE-BERT
+- Vocabulary size: 50,368
+- Shared with [MWireLabs/ne-bert](https://huggingface.co/MWireLabs/ne-bert)
+## Performance
+### Intrinsic Evaluation
+Evaluated on held-out test set (5,587 sentences):
+| Metric | Score |
+|--------|-------|
+| **Test Loss** | 3.03 |
+| **Perplexity** | 20.78 |
+## Usage
+### Direct Usage
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("MWireLabs/nyishibert")
+model = AutoModelForMaskedLM.from_pretrained("MWireLabs/nyishibert")
+# Example: Fill mask
+text = "Ngulug [MASK] nyilakuma"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+# Get predictions
+masked_index = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
+logits = outputs.logits[0, masked_index, :]
+predicted_token_id = logits.argmax(axis=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+print(f"Predicted word: {predicted_token}")
+```
+### Pipeline Usage
+```python
+from transformers import pipeline
+# Create fill-mask pipeline
+unmasker = pipeline('fill-mask', model='MWireLabs/nyishibert')
+# Predict masked tokens
+result = unmasker("Ngulug [MASK] nyilakuma")
+print(result)
+```
+### Fine-tuning
+This model can be fine-tuned for downstream tasks such as:
+- Text classification
+- Named entity recognition
+- Part-of-speech tagging
+- Dependency parsing
+```python
+from transformers import AutoModelForSequenceClassification
+# Load for sequence classification
+model = AutoModelForSequenceClassification.from_pretrained(
+    "MWireLabs/nyishibert",
+    num_labels=2
+)
+# ... add your fine-tuning code
+```
+## Limitations and Bias
+### Known Limitations
+1. **Script:** Trained exclusively on Roman script (njz-Latn). The model will not work with other scripts.
+2. **Orthographic variation:** Nyishi lacks standardized orthography. The model reflects spelling conventions present in the WMT25 training data, which may vary from other writing practices.
+3. **Domain coverage:** Training data comes from mixed domains in WMT25. Performance may vary on specialized domains not represented in the training corpus.
+4. **Data size:** Trained on 55,870 sentences. While sufficient for meaningful language modeling, larger corpora would likely improve performance.
+5. **Vocabulary coverage:** Uses NE-BERT's shared tokenizer. Some Nyishi-specific terms may be suboptimally tokenized.
+### Potential Biases
+- The model may reflect biases present in the WMT25 training corpus
+- Performance may be better on domains well-represented in training data
+- Spelling variations common in digital Nyishi text may not all be equally represented
+## Ethical Considerations
+### Language and Community
+- **Community engagement:** This model is intended to support Nyishi language technology and preservation efforts.
+- **Data sovereignty:** All training data is from publicly available WMT25 resources.
+- **Orthography:** Use of Roman script reflects current digital practice but does not constitute endorsement of any particular writing system.
+### Responsible Use
+- This model is a research tool for Nyishi language technology development
+- Users should be aware of the model's limitations when deploying in production
+- Community feedback on model behavior and outputs is welcomed
+## Citation
+If you use NyishiBERT in your research, please cite:
+```bibtex
+@misc{nyishibert2026,
+  author = {MWire Labs},
+  title = {NyishiBERT: A Monolingual Language Model for Nyishi},
+  year = {2026},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/MWireLabs/nyishibert}},
+}
+```
+**Training data citation:**
+```bibtex
+@inproceedings{wmt25,
+  title = {Findings of the 2025 Conference on Machine Translation (WMT25)},
+  booktitle = {Proceedings of the Tenth Conference on Machine Translation},
+  year = {2025},
+  address = {Suzhou, China},
+  month = {November},
+  publisher = {Association for Computational Linguistics}
+}
+```
+## Model Card Contact
+For questions, feedback, or issues regarding this model:
+- Organization: [MWire Labs](https://huggingface.co/MWireLabs)
+- Issues: Please open an issue on the model repository
+## Acknowledgments
+- Training data: WMT EMNLP 2025 (Tenth Conference on Machine Translation)
+- Tokenizer: Shared with [NE-BERT](https://huggingface.co/MWireLabs/ne-bert)
+- Architecture: [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)
+---