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+Without-Context-Finegrain/xlm-roberta-base/customized-mwt-ner/customized-mwt-ner.tagger.mdl filter=lfs diff=lfs merge=lfs -text

LICENSE

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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+END OF TERMS AND CONDITIONS

README.md

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+---
+language: multilingual
+tags:
+  - xlm-roberta
+  - adapter
+  - NER
+  - POS
+  - dependency-parsing
+  - universal-dependencies
+  - multi-task
+license: apache-2.0
+
+datasets:
+  - universal_dependencies
+---
+
+# XLM-R Adapter-Based Multitask Model (`customized-mwt-ner`)
+
+This repository contains a fine-tuned [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model enhanced with task-specific **adapter layers** for multilingual NLP tasks including:
+
+- Part-of-Speech tagging (`UPOS`, `XPOS`)
+- Morphological features tagging (`FEATS`)
+- Dependency parsing (`DEPREL`, `HEAD`)
+- Named Entity Recognition (NER)
+
+Trained using **adapter-based finetuning** and multi-task supervision on Universal Dependencies and custom NER data.
+
+---
+
+## Files
+
+- `customized-mwt-ner.tagger.mdl`  
+  → PyTorch state dict containing task adapter weights and classification heads.
+
+- `customized-mwt-ner.vocabs.json`  
+  → Token and label vocabulary mappings (used for preprocessing and decoding).
+
+---
+
+## Model Architecture
+
+- Base: `xlm-roberta-base` (frozen)
+- Adapter configuration: [`Pfeiffer`](https://adapterhub.ml/adapters/pfeiffer/)
+- Injected adapter layer per Transformer block
+- Task-specific heads for:
+  - POS (`upos`, `xpos`)
+  - Morphological features (`feats`)
+  - Dependency parsing (`unlabeled`, `deprel`)
+  - Optional NER layer (if enabled)
+
+---
+
+## Usage (PyTorch)
+
+You’ll need to:
+1. Load `xlm-roberta-base` from Hugging Face
+2. Load adapter weights from `customized-mwt-ner.tagger.mdl`
+3. Use the vocab file for decoding predictions
+
+> NOTE: This model is **not directly plug-and-play** with `transformers` unless you recreate the original architecture and adapter insertion logic.
+
+Refer to the [original paper](http://arxiv.org/abs/2310.09501) for complete documentation.
+
+---
+
+## Variants
+
+This model comes in four variants:
+
+| Context       | Label Granularity | Folder / Repo Name               |
+|---------------|-------------------|----------------------------------|
+| With Context  | Coarse            | `with-context-coarse`            |
+| With Context  | Fine-grain        | `with-context-finegrain`         |
+| Without Context | Coarse          | `without-context-coarse`         |
+| Without Context | Fine-grain      | `without-context-finegrain`      |
+
+---
+
+## License
+
+APACHE 2.0 License. Please cite appropriately if used in research.
+
+---
+
+## Citation
+
+```bibtex
+@misc{sandhan2023depnecti,
+      title={DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit}, 
+      author={Jivnesh Sandhan and Yaswanth Narsupalli and Sreevatsa Muppirala and Sriram Krishnan and Pavankumar Satuluri and Amba Kulkarni and Pawan Goyal},
+      year={2023},
+      eprint={2310.09501},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+Link to original paper => [HERE](http://arxiv.org/abs/2310.09501)  
+Link to DepNeCT Github Repo => [HERE](https://github.com/yaswanth-iitkgp/DepNeCTI)

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