Instructions to use zeromodels/deberta_v3_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zeromodels/deberta_v3_large with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/deberta_v3_large") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +30 -0
- model.weights.h5 +3 -0
- tokenizer.json +0 -0
README.md
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---
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license: mit
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library_name: keras
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pipeline_tag: feature-extraction
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tags:
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- keras
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- kerasformers
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- tf
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- jax
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- pytorch
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- backend-agnostic
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- deberta_v3
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- feature-extraction
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base_model:
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- microsoft/deberta-v3-large
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---
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# DeBERTa-v3 (deberta_v3_large)
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A pure [Keras 3](https://keras.io) port of **DeBERTa-v3**, converted from [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) (Hugging Face Transformers).
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The weights are **backend-agnostic**: the same checkpoint loads and runs identically under the JAX, TensorFlow, or PyTorch Keras backend (`KERAS_BACKEND=jax|tensorflow|torch`), via [kerasformers](https://github.com/IMvision12/KerasFormers).
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## Usage
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```python
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from kerasformers.models.deberta_v3 import DebertaV3Model
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model = DebertaV3Model.from_weights("kerasformers/deberta_v3_large")
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```
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model.weights.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2a163ab193f0306cb6a5ebde9193764e776a676868aa84d39fa2ace751a94da
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size 1736943056
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tokenizer.json
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