Add VEXMLM-Tigrinya-NER: five verified fine-tuned checkpoints (seeds 42-46)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+seed-42/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+seed-43/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+seed-44/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+seed-45/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+seed-46/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- ti
+license: apache-2.0
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- ner
+- token-classification
+- xlm-roberta
+- vexmlm
+- geez
+- low-resource
+base_model: Hailay/VEXMLM
+---
+
+# VEXMLM — Tigrinya NER
+
+Tigrinya **token classification** fine-tuned from
+[`Hailay/VEXMLM`](https://huggingface.co/Hailay/VEXMLM), the vocabulary-extended
+XLM-R for Ge'ez-script languages.
+
+Official implementation: **https://github.com/hailaykidu/VEXMLM**
+
+| | |
+|---|---|
+| Task | token-classification |
+| Dataset | Tigrinya NER |
+| Language | Tigrinya |
+| Architecture | `XLMRobertaForTokenClassification` |
+| Base model | `Hailay/VEXMLM` |
+| Vocabulary | 280,002 |
+| Labels | 11 |
+| Seeds published | 42, 43, 44, 45, 46 |
+
+Labels cover PER, ORG, LOC, DATE and MISC in BIO format (11 classes).
+
+## Five-seed benchmark evaluation
+
+Fine-tuned independently under seeds 42–46 with one configuration (hash
+`ce27cc194946`) on an A100-PCIE-40GB. Reported as mean ± standard deviation over
+the five runs, on the dataset's **test** split.
+
+| Metric | Score |
+|---|---|
+| Entity-F1 | **72.82 ± 0.79** |
+| Macro-F1 | 82.19 ± 0.69 |
+| Accuracy | 95.15 ± 0.05 |
+
+These are the paper's verified results. They come from the five-seed evaluation
+described above — **not** from interactive use.
+
+### Interactive inference vs. benchmark
+
+**Benchmark evaluation** is the five-seed measurement on the held-out test split,
+shown in the table above.
+
+**Interactive inference** is what the usage example below performs: Enter arbitrary Tigrinya text and inspect the predicted entity spans.
+Predictions on arbitrary user input are demonstrations only and do not produce or
+reproduce the benchmark score.
+
+## Repository layout
+
+Five independently fine-tuned checkpoints, one per seed. The reported benchmark
+score is the mean ± standard deviation over all five; **no single seed is the
+"five-seed model."**
+
+```
+seed-42/  seed-43/  seed-44/  seed-45/  seed-46/
+```
+
+Load a specific seed with the `subfolder` argument, as in the example below.
+
+## Fine-tuning
+
+Fine-tuned from [`Hailay/VEXMLM`](https://huggingface.co/Hailay/VEXMLM), a
+vocabulary-extended XLM-R (280,002 subwords, 30,000 Ge'ez tokens merged into the
+SentencePiece model) after continued MLM pretraining.
+
+| Hyperparameter | Value |
+|---|---|
+| Max sequence length | 256 |
+| Batch size | 32 |
+| Epochs | 4 |
+| Learning rate | 2e-5 |
+| LR schedule | Linear decay, 10% warmup |
+| Weight decay | 0.01 |
+| Gradient clipping | 1.0 |
+| Optimizer | AdamW (β₁ 0.9, β₂ 0.999, ε 1e-8) |
+| Precision | bf16 |
+| Trainable parameters | All |
+| Hardware | 1× NVIDIA A100 |
+
+Runs are bit-reproducible: `enable_full_determinism`,
+`CUBLAS_WORKSPACE_CONFIG=:4096:8`, `dataloader_num_workers=0`.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+repo = "Hailay/VEXMLM-Tigrinya-NER"
+tokenizer = AutoTokenizer.from_pretrained(repo, subfolder="seed-42")
+model = AutoModelForTokenClassification.from_pretrained(repo, subfolder="seed-42")
+model.eval()
+
+words = "ኤርትራ ኣብ ቀርኒ አፍሪቃ እትርከብ ሃገር እያ።".split()
+enc = tokenizer(words, is_split_into_words=True, return_tensors="pt", truncation=True)
+
+with torch.no_grad():
+    pred = model(**enc).logits.argmax(-1)[0].tolist()
+
+seen = set()
+for p, w in zip(pred, enc.word_ids(0)):
+    if w is None or w in seen:
+        continue
+    seen.add(w)
+    print(words[w], "->", model.config.id2label[p])
+```
+
+## Limitations
+
+- Fine-tuned for Tigrinya on Tigrinya NER only; performance on other
+  languages, domains or label schemes is not characterised.
+- The base model covers Amharic and Tigrinya; other Ge'ez-script languages were
+  not part of pretraining.
+- Corpora are drawn largely from religious and news domains, and the model may
+  reflect those distributions and any biases in them.
+- Single-configuration study: no hyperparameter search was performed, and
+  baseline comparisons in the paper are single-seed.
+
+## Reproducibility
+
+The fine-tuning launcher, evaluation code and per-run result records are in the
+official repository: **https://github.com/hailaykidu/VEXMLM**
+
+```bash
+sbatch scripts/slurm_stage2_spm_seeds.sh    # 6 tasks × 5 seeds
+python3 evaluation/export_spm_results.py    # regenerates the metrics table
+```
+
+## Citation
+
+```bibtex
+@inproceedings{teklehaymanot2026vexmlm,
+  title     = {Expanding the Lexicon of Ge'ez Based African Languages:
+               A Comparative Study of Amharic and Tigrinya},
+  author    = {Teklehaymanot, Hailay Kidu and Yadeta, Gebregziabihier and
+               Nejdl, Wolfgang},
+  booktitle = {Proceedings of the Workshop on Language Models for
+               Underserved Communities (LM4UC) at IJCAI},
+  year      = {2026}
+}
+```
+
+Accepted at the LM4UC Workshop, IJCAI 2026.
+
+## License
+
+Apache 2.0, following `xlm-roberta-base`.

seed-42/config.json

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