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pipeline_tag: feature-extraction
license: apache-2.0
base_model: google/electra-base-discriminator
library_name: kerasformers
tags:
- keras
- kerasformers
- electra
- discriminator
- text-encoder
- feature-extraction
- arxiv:2003.10555
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/kerasformers/electra-6a8540d1f5831e07dc89d8d1) for all versions of ELECTRA.***
# Run ELECTRA with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/KerasFormers) [](https://imvision12.github.io/KerasFormers/electra/) [](https://huggingface.co/collections/kerasformers/electra-6a8540d1f5831e07dc89d8d1)
# kerasformers/electra_base_discriminator
Paper: [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (arXiv:2003.10555)](https://arxiv.org/abs/2003.10555) · [HF Papers](https://huggingface.co/papers/2003.10555)
ELECTRA is Google's BERT-style bidirectional text encoder, pre-trained as a replaced-token **discriminator** (with a smaller **generator** producing the corrupted tokens). This repo is the **encoder / downstream** checkpoint. WordPiece tokenizer; mask token `[MASK]`.
For more details on the model, please go to the upstream [model card](https://huggingface.co/google/electra-base-discriminator).
Pure-**Keras 3** conversion of [`google/electra-base-discriminator`](https://huggingface.co/google/electra-base-discriminator) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
## ✨ Quick start (encoder / downstream)
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from kerasformers.models.electra import ElectraModel, ElectraTokenizer
model = ElectraModel.from_weights("kerasformers/electra_base_discriminator")
tokenizer = ElectraTokenizer.from_weights("kerasformers/electra_base_discriminator")
out = model(tokenizer("The quick brown fox."))["last_hidden_state"] # (1, L, H)
```
The same repo also serves the task heads, loaded the same way: `ElectraSequenceClassify`, `ElectraTokenClassify`, `ElectraQnA`, `ElectraMultipleChoice` (each takes the pretrained encoder and a randomly-initialized head, ready for fine-tuning).
Load any ELECTRA variant the same way with `from_weights("kerasformers/<variant>")`:
| Size | Discriminator (encoder / downstream) | Generator (masked-LM) |
|---|---|---|
| small | [`kerasformers/electra_small_discriminator`](https://huggingface.co/kerasformers/electra_small_discriminator) | [`kerasformers/electra_small_generator`](https://huggingface.co/kerasformers/electra_small_generator) |
| base | [`kerasformers/electra_base_discriminator`](https://huggingface.co/kerasformers/electra_base_discriminator) | [`kerasformers/electra_base_generator`](https://huggingface.co/kerasformers/electra_base_generator) |
| large | [`kerasformers/electra_large_discriminator`](https://huggingface.co/kerasformers/electra_large_discriminator) | [`kerasformers/electra_large_generator`](https://huggingface.co/kerasformers/electra_large_generator) |
## Available classes
Load any of these from this repo with `from_weights("kerasformers/electra_base_discriminator")` (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 |
|---|---|
| `ElectraModel` | Encoder backbone |
| `ElectraSequenceClassify` | Sequence classification |
| `ElectraTokenClassify` | Token classification (NER / POS) |
| `ElectraQnA` | Extractive question answering |
| `ElectraMultipleChoice` | Multiple choice |
```python
from kerasformers.models.electra import ElectraSequenceClassify
model = ElectraSequenceClassify.from_weights("kerasformers/electra_base_discriminator")
```
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
- Prefer `ElectraTokenizer.from_weights(...)` so WordPiece tokenization matches.
- Downstream tasks (classification / QA / NER) use the **discriminator** repos; the **generator** repos are the masked-LM.
- See [ELECTRA docs](https://imvision12.github.io/KerasFormers/electra/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
- Community / upstream safetensors still work via the `hf:` prefix, e.g. `ElectraModel.from_weights("hf:google/electra-base-discriminator")`.
## Special Thanks
A huge thank you to the Google ELECTRA authors for creating and releasing these models.
License: Apache 2.0.
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