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README.md

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+---
+pipeline_tag: fill-mask
+license: apache-2.0
+base_model: answerdotai/ModernBERT-base
+library_name: kerasformers
+tags:
+- keras
+- kerasformers
+- modernbert
+- fill-mask
+- text-encoder
+- arxiv:2412.13663
+- pytorch
+- jax
+- tf
+---
+
+# Run ModernBERT 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-ModernBERT-blue)](https://imvision12.github.io/KerasFormers/modernbert/)
+
+# kerasformers/modernbert_base
+
+Paper: [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder (arXiv:2412.13663)](https://arxiv.org/abs/2412.13663) · [HF Papers](https://huggingface.co/papers/2412.13663)
+
+ModernBERT is Answer.AI / LightOn's modernized bidirectional transformer text encoder: rotary position embeddings, attention that alternates between a global (full) layer and local sliding-window layers, GeGLU feed-forwards, and pre-LayerNorm, with an 8192-token context. Byte-level BPE tokenizer; mask token `[MASK]`. No token-type ids.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/answerdotai/ModernBERT-base).
+
+Pure-**Keras 3** conversion of [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **fill-mask / encoder** checkpoint (`ModernBertMaskedLM`, modernbert_base). Task heads (sequence / token classify, QA, multiple choice) load via `hf:` fine-tunes.
+
+## ✨ Quick start (fill-mask)
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.modernbert import ModernBertMaskedLM, ModernBertTokenizer
+
+mlm = ModernBertMaskedLM.from_weights("kerasformers/modernbert_base")
+tokenizer = ModernBertTokenizer.from_weights("kerasformers/modernbert_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 ModernBERT variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | layers | embed_dim |
+|---|---|---|---|
+| `modernbert_base` | [`kerasformers/modernbert_base`](https://huggingface.co/kerasformers/modernbert_base) | 22 | 768 |
+| `modernbert_large` | [`kerasformers/modernbert_large`](https://huggingface.co/kerasformers/modernbert_large) | 28 | 1024 |
+
+## Available classes
+
+Load any of these from this repo with `from_weights("kerasformers/modernbert_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 |
+|---|---|
+| `ModernBertModel` | Encoder backbone |
+| `ModernBertMaskedLM` | Masked language modeling (fill-mask) |
+| `ModernBertSequenceClassify` | Sequence classification |
+| `ModernBertTokenClassify` | Token classification (NER / POS) |
+| `ModernBertQnA` | Extractive question answering |
+| `ModernBertMultipleChoice` | Multiple choice |
+
+```python
+from kerasformers.models.modernbert import ModernBertSequenceClassify
+model = ModernBertSequenceClassify.from_weights("kerasformers/modernbert_base")
+```
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `ModernBertTokenizer.from_weights(...)` so tokenization matches.
+- Use `[MASK]` (not `<mask>`).
+- ModernBERT has no token-type ids; the tokenizer emits only `input_ids` / `attention_mask`.
+- See [ModernBERT docs](https://imvision12.github.io/KerasFormers/modernbert/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `ModernBertMaskedLM.from_weights("hf:answerdotai/ModernBERT-base")`.
+
+## Special Thanks
+
+A huge thank you to the Answer.AI and LightOn authors for creating and releasing ModernBERT.
+
+License: Apache 2.0.

kf_config.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.3",
+  "model_module": "kerasformers.models.modernbert",
+  "model_class": "ModernBertModel",
+  "variant": "modernbert_base",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "modernbert",
+  "text_config": {
+    "vocab_size": 50368,
+    "embed_dim": 768,
+    "num_layers": 22,
+    "num_heads": 12,
+    "mlp_dim": 1152,
+    "max_position_embeddings": 8192,
+    "hidden_act": "gelu",
+    "norm_eps": 1e-05,
+    "local_attention": 128,
+    "global_attn_every_n_layers": 3,
+    "global_rope_theta": 160000.0,
+    "local_rope_theta": 10000.0,
+    "pad_token_id": 50283
+  }
+}

model.weights.h5

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