Add zeromodels Keras 3 conversion of microsoft/mpnet-base

README.md

@@ -0,0 +1,92 @@
+---
+pipeline_tag: fill-mask
+license: mit
+base_model: microsoft/mpnet-base
+library_name: zeromodels
+language:
+- en
+tags:
+- keras
+- zeromodels
+- mpnet
+- fill-mask
+- text-encoder
+- arxiv:2004.09297
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/zeromodels/mpnet-6ab0702ad43a267bae54ded1) for all versions of MPNet.***
+
+# Run MPNet with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-MPNet-blue)](https://imvision12.github.io/ZeroModels/mpnet/) [![Collection](https://img.shields.io/badge/HF-MPNet%20collection-yellow)](https://huggingface.co/collections/zeromodels/mpnet-6ab0702ad43a267bae54ded1)
+
+# zeromodels/mpnet_base
+
+Paper: [MPNet: Masked and Permuted Pre-training for Language Understanding (arXiv:2004.09297)](https://arxiv.org/abs/2004.09297) · [HF Papers](https://huggingface.co/papers/2004.09297)
+
+MPNet is a bidirectional encoder pre-trained with **masked and permuted** language modeling, unifying BERT's masked-LM objective with XLNet's permuted one. Unlike BERT it has no token-type embeddings, offsets position ids past the padding id, and adds a shared relative position bias to every attention layer.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/microsoft/mpnet-base).
+
+Pure-**Keras 3** conversion of [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **fill-mask / encoder** checkpoint (`MPNetMaskedLM`, 12 layers / 768 dim). Task heads load via `hf:` fine-tunes.
+
+## ✨ Quick start (fill-mask)
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from zeromodels.models.mpnet import MPNetMaskedLM, MPNetTokenizer
+
+mlm = MPNetMaskedLM.from_weights("zeromodels/mpnet_base")
+tokenizer = MPNetTokenizer.from_weights("zeromodels/mpnet_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 MPNet variant the same way with `from_weights("zeromodels/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `mpnet_base` | [`zeromodels/mpnet_base`](https://huggingface.co/zeromodels/mpnet_base) |
+
+## Available classes
+
+Load any of these from this repo with `from_weights("zeromodels/mpnet_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 |
+|---|---|
+| `MPNetModel` | Encoder backbone |
+| `MPNetMaskedLM` | Masked language modeling (fill-mask) |
+| `MPNetSequenceClassify` | Sequence classification |
+| `MPNetTokenClassify` | Token classification (NER / POS) |
+| `MPNetQnA` | Extractive question answering |
+| `MPNetMultipleChoice` | Multiple choice |
+
+```python
+from zeromodels.models.mpnet import MPNetSequenceClassify
+model = MPNetSequenceClassify.from_weights("zeromodels/mpnet_base")
+```
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
+- Prefer `MPNetTokenizer.from_weights(...)` so the WordPiece vocab matches.
+- Use `<mask>` (not `[MASK]`); MPNet pairs RoBERTa-style special tokens with a WordPiece vocabulary.
+- MPNet takes `input_ids` + `attention_mask` only — there are no `token_type_ids`.
+- See [MPNet docs](https://imvision12.github.io/ZeroModels/mpnet/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `MPNetMaskedLM.from_weights("hf:microsoft/mpnet-base")`.
+
+## Special Thanks
+
+A huge thank you to the Microsoft MPNet authors for creating and releasing these models.
+
+License: MIT.

model.weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f0108355e7303311ab0a3c8372e815314b714004dbf34f5456fa89b52f6e1fa
+size 534681124

zm_config.json

@@ -0,0 +1,23 @@
+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.3.0",
+  "model_module": "zeromodels.models.mpnet",
+  "model_class": "MPNetModel",
+  "variant": "mpnet_base",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "mpnet",
+  "text_config": {
+    "vocab_size": 30527,
+    "embed_dim": 768,
+    "num_layers": 12,
+    "num_heads": 12,
+    "mlp_dim": 3072,
+    "max_position_embeddings": 514,
+    "relative_attention_num_buckets": 32,
+    "hidden_act": "gelu",
+    "layer_norm_eps": 1e-05,
+    "pad_token_id": 1
+  }
+}