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README.md

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+---
+pipeline_tag: fill-mask
+license: mit
+base_model: microsoft/deberta-base
+library_name: kerasformers
+tags:
+- keras
+- kerasformers
+- deberta
+- deberta-v1
+- fill-mask
+- text-encoder
+- arxiv:2006.03654
+- arxiv:2111.09543
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/kerasformers/deberta-v1-v2-v3-6a6e90bac01e412b478562f3) for all versions of DeBERTa (v1 / v2 / v3).***
+
+# Run DeBERTa with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-DeBERTa-blue)](https://imvision12.github.io/KerasFormers/deberta/) [![Collection](https://img.shields.io/badge/HF-DeBERTa%20collection-yellow)](https://huggingface.co/collections/kerasformers/deberta-v1-v2-v3-6a6e90bac01e412b478562f3)
+
+# kerasformers/deberta_base
+
+Papers: [DeBERTa: Decoding-enhanced BERT with Disentangled Attention (arXiv:2006.03654)](https://arxiv.org/abs/2006.03654) · [DeBERTaV3 (arXiv:2111.09543)](https://arxiv.org/abs/2111.09543) · [HF Papers](https://huggingface.co/papers/2006.03654)
+
+DeBERTa is Microsoft's disentangled-attention text encoder (content + relative position). v1 uses byte-level BPE; v2/v3 use SentencePiece. v3 adds ELECTRA-style pretraining with gradient-disentangled embedding sharing. Import from `deberta` / `deberta_v2` / `deberta_v3` to match the generation.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/microsoft/deberta-base).
+
+Pure-**Keras 3** conversion of [`microsoft/deberta-base`](https://huggingface.co/microsoft/deberta-base) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **fill-mask / encoder** checkpoint (`DebertaMaskedLM`, v1 base). Task heads (sequence/token classify, QA, …) load via `hf:` fine-tunes.
+
+## ✨ Quick start (fill-mask)
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.deberta import (
+    DebertaMaskedLM,
+    DebertaTokenizer,
+)
+
+mlm = DebertaMaskedLM.from_weights("kerasformers/deberta_base")
+tokenizer = DebertaTokenizer.from_weights("kerasformers/deberta_base")
+
+inputs = tokenizer("The capital of France is [MASK].")
+logits = mlm(inputs)  # (1, L, vocab_size)
+mask = int((inputs["input_ids"][0] == tokenizer.mask_token_id).argmax())
+print(tokenizer.decode([int(logits[0, mask].argmax())]))
+```
+
+Load any DeBERTa variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Generation |
+|---|---|---|
+| `deberta_base` | [`kerasformers/deberta_base`](https://huggingface.co/kerasformers/deberta_base) | v1 |
+| `deberta_large` | [`kerasformers/deberta_large`](https://huggingface.co/kerasformers/deberta_large) | v1 |
+| `deberta_v2_xlarge` | [`kerasformers/deberta_v2_xlarge`](https://huggingface.co/kerasformers/deberta_v2_xlarge) | v2 |
+| `deberta_v2_xxlarge` | [`kerasformers/deberta_v2_xxlarge`](https://huggingface.co/kerasformers/deberta_v2_xxlarge) | v2 |
+| `deberta_v3_xsmall` | [`kerasformers/deberta_v3_xsmall`](https://huggingface.co/kerasformers/deberta_v3_xsmall) | v3 |
+| `deberta_v3_small` | [`kerasformers/deberta_v3_small`](https://huggingface.co/kerasformers/deberta_v3_small) | v3 |
+| `deberta_v3_base` | [`kerasformers/deberta_v3_base`](https://huggingface.co/kerasformers/deberta_v3_base) | v3 |
+| `deberta_v3_large` | [`kerasformers/deberta_v3_large`](https://huggingface.co/kerasformers/deberta_v3_large) | v3 |
+
+## Available classes
+
+Load any of these from this repo with `from_weights("kerasformers/deberta_base")` (or on the fly via the `hf:` prefix). The pretrained backbone is shared; task heads not stored in this checkpoint start randomly initialized, ready for fine-tuning (or load a `hf:` fine-tune).
+
+| Class | Task |
+|---|---|
+| `DebertaModel` | Encoder backbone |
+| `DebertaMaskedLM` | Masked language modeling (fill-mask) |
+| `DebertaSequenceClassify` | Sequence classification |
+| `DebertaTokenClassify` | Token classification (NER / POS) |
+| `DebertaQnA` | Extractive question answering |
+
+```python
+from kerasformers.models.deberta import DebertaSequenceClassify
+model = DebertaSequenceClassify.from_weights("kerasformers/deberta_base")
+```
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `Tokenizer.from_weights(...)` so vocab and mask token match.
+- Do not mix packages across generations (v1 ≠ v2 ≠ v3).
+- See [DeBERTa docs](https://imvision12.github.io/KerasFormers/deberta/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `DebertaMaskedLM.from_weights("hf:microsoft/deberta-base")`.
+
+## Special Thanks
+
+A huge thank you to the Microsoft DeBERTa authors for creating and releasing these models.
+
+License: MIT.

kf_config.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.1",
+  "model_module": "kerasformers.models.deberta",
+  "model_class": "DebertaModel",
+  "variant": "deberta_base",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "deberta",
+  "text_config": {
+    "vocab_size": 50265,
+    "embed_dim": 768,
+    "num_layers": 12,
+    "num_heads": 12,
+    "mlp_dim": 3072,
+    "max_position_embeddings": 512,
+    "max_relative_positions": 512,
+    "pos_att_type": [
+      "c2p",
+      "p2c"
+    ],
+    "hidden_act": "gelu",
+    "layer_norm_eps": 1e-07,
+    "pad_token_id": 0
+  }
+}

model.weights.h5

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