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README.md

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+---
+pipeline_tag: fill-mask
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+library_name: kerasformers
+tags:
+- keras
+- kerasformers
+- bert
+- uncased
+- fill-mask
+- text-encoder
+- arxiv:1810.04805
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/kerasformers/bert-6a6e8ea40d45e759626f2ab3) for all versions of BERT.***
+
+# Run BERT 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-BERT-blue)](https://imvision12.github.io/KerasFormers/bert/) [![Collection](https://img.shields.io/badge/HF-BERT%20collection-yellow)](https://huggingface.co/collections/kerasformers/bert-6a6e8ea40d45e759626f2ab3)
+
+# kerasformers/bert_base_uncased
+
+Paper: [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (arXiv:1810.04805)](https://arxiv.org/abs/1810.04805) · [HF Papers](https://huggingface.co/papers/1810.04805)
+
+BERT is Google's bidirectional transformer text encoder, pretrained with masked LM and next-sentence prediction. WordPiece tokenizer; mask token `[MASK]`. Uncased variants lower-case the input; cased variants preserve case.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/google-bert/bert-base-uncased).
+
+Pure-**Keras 3** conversion of [`google-bert/bert-base-uncased`](https://huggingface.co/google-bert/bert-base-uncased) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **fill-mask / encoder** checkpoint (`BertMaskedLM`, base uncased). Task heads (sequence/token classify, QA, NSP, …) load via `hf:` fine-tunes.
+
+## ✨ Quick start (fill-mask)
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.bert import BertMaskedLM, BertTokenizer
+
+mlm = BertMaskedLM.from_weights("kerasformers/bert_base_uncased")
+tokenizer = BertTokenizer.from_weights("kerasformers/bert_base_uncased")
+
+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.ids_to_tokens[int(logits[0, mask].argmax())])
+```
+
+Load any BERT variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Casing |
+|---|---|---|
+| `bert_base_uncased` | [`kerasformers/bert_base_uncased`](https://huggingface.co/kerasformers/bert_base_uncased) | uncased |
+| `bert_large_uncased` | [`kerasformers/bert_large_uncased`](https://huggingface.co/kerasformers/bert_large_uncased) | uncased |
+| `bert_base_cased` | [`kerasformers/bert_base_cased`](https://huggingface.co/kerasformers/bert_base_cased) | cased |
+| `bert_large_cased` | [`kerasformers/bert_large_cased`](https://huggingface.co/kerasformers/bert_large_cased) | cased |
+
+## Available classes
+
+Load any of these from this repo with `from_weights("kerasformers/bert_base_uncased")` (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 |
+|---|---|
+| `BertModel` | Encoder backbone |
+| `BertMaskedLM` | Masked language modeling (fill-mask) |
+| `BertSequenceClassify` | Sequence classification |
+| `BertTokenClassify` | Token classification (NER / POS) |
+| `BertNextSentencePredict` | Next-sentence prediction |
+| `BertQnA` | Extractive question answering |
+| `BertMultipleChoice` | Multiple choice |
+
+```python
+from kerasformers.models.bert import BertSequenceClassify
+model = BertSequenceClassify.from_weights("kerasformers/bert_base_uncased")
+```
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `BertTokenizer.from_weights(...)` so WordPiece casing matches.
+- Use `[MASK]` (not `<mask>`).
+- See [BERT docs](https://imvision12.github.io/KerasFormers/bert/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `BertMaskedLM.from_weights("hf:google-bert/bert-base-uncased")`.
+
+## Special Thanks
+
+A huge thank you to the Google BERT authors for creating and releasing these models.
+
+License: Apache 2.0.

kf_config.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.1",
+  "model_module": "kerasformers.models.bert",
+  "model_class": "BertModel",
+  "variant": "bert_base_uncased",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "bert",
+  "text_config": {
+    "vocab_size": 30522,
+    "embed_dim": 768,
+    "num_layers": 12,
+    "num_heads": 12,
+    "mlp_dim": 3072,
+    "max_position_embeddings": 512,
+    "type_vocab_size": 2,
+    "hidden_act": "gelu",
+    "layer_norm_eps": 1e-12,
+    "pad_token_id": 0
+  }
+}

model.weights.h5

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