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README.md

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+---
+pipeline_tag: translation
+license: apache-2.0
+base_model: google-t5/t5-large
+library_name: kerasformers
+language:
+- en
+- fr
+- de
+- ro
+tags:
+- keras
+- kerasformers
+- t5
+- text2text-generation
+- pytorch
+- jax
+- tf
+---
+
+# T5-large in Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-181717?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-T5-1f6feb)](https://imvision12.github.io/KerasFormers/t5/) [![HuggingFace](https://img.shields.io/badge/HuggingFace-T5-ffd21e?logo=huggingface&logoColor=black)](https://huggingface.co/collections/kerasformers/t5-6a85056935f438653698c56f)
+
+# kerasformers/t5_large
+
+Pure-**Keras 3** conversion of [`google-t5/t5-large`](https://huggingface.co/google-t5/t5-large) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**, bit-exact with the Hugging Face original. T5 is a text-to-text encoder-decoder; this repo hosts the full backbone (`kf_config.json` declares `T5Model`), and every T5 class (`T5ConditionalGenerate`, `T5EncoderModel`, and the classification / QA heads) loads its subset from the one `model.weights.h5`.
+
+For model details, license, and usage terms, see the upstream [model card](https://huggingface.co/google-t5/t5-large).
+
+Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)
+
+## Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.t5 import T5ConditionalGenerate, T5Tokenizer
+
+model = T5ConditionalGenerate.from_weights("kerasformers/t5_large")
+tokenizer = T5Tokenizer.from_weights("kerasformers/t5_large")
+
+inputs = tokenizer("translate English to German: The house is wonderful.")
+output_ids = model.generate(
+    inputs["input_ids"], inputs["attention_mask"], max_new_tokens=40
+)
+print(tokenizer.decode(output_ids[0]))
+```
+
+Load any T5 variant the same way with `from_weights("kerasformers/<variant>")`. Browse them all in the [T5 collection](https://huggingface.co/collections/kerasformers/t5-6a85056935f438653698c56f).
+
+## Available classes
+
+Load any of these from this repo with `from_weights("kerasformers/t5_large")` (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 |
+|---|---|
+| `T5Model` | Encoder backbone |
+| `T5ConditionalGenerate` | Text-to-text generation |
+| `T5EncoderModel` | Encoder-only features |
+| `T5SequenceClassify` | Sequence classification |
+| `T5TokenClassify` | Token classification (NER / POS) |
+| `T5QnA` | Extractive question answering |
+
+```python
+from kerasformers.models.t5 import T5SequenceClassify
+model = T5SequenceClassify.from_weights("kerasformers/t5_large")
+```
+
+## Special Thanks
+
+Thank you to the Google T5 team for creating and releasing the T5 models.

kf_config.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.4",
+  "model_module": "kerasformers.models.t5",
+  "model_class": "T5Model",
+  "variant": "t5_large",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "t5",
+  "text_config": {
+    "vocab_size": 32128,
+    "embed_dim": 1024,
+    "key_value_dim": 64,
+    "mlp_dim": 4096,
+    "num_layers": 24,
+    "num_decoder_layers": 24,
+    "num_heads": 16,
+    "relative_attention_num_buckets": 32,
+    "relative_attention_max_distance": 128,
+    "hidden_act": "relu",
+    "layer_norm_eps": 1e-06,
+    "dropout": 0.1,
+    "tie_word_embeddings": true,
+    "pad_token_id": 0,
+    "eos_token_id": 1,
+    "decoder_start_token_id": 0
+  }
+}

model.weights.h5

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