README.md

---
title: README
emoji: ❤️
colorFrom: red
colorTo: red
sdk: streamlit
app_file: app.py
pinned: false
---

<div class="lg:col-span-3">
	<p class="mb-4">
	Hugging Face makes it easy to collaboratively build and showcase your <a
		href="https://www.sbert.net/">Sentence Transformers</a
	>
	models!<br />
You can collaborate with your organization, upload and showcase your own models in your profile! ❤️
</p>
</div>
<a href="https://www.sbert.net/" class="block overflow-hidden group">
	<div
		class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center bg-[#FA8072]"
	>
		<img alt="" src="https://huggingface.co/spaces/sentence-transformers/README/resolve/main/sbertLogo.png" class="w-40" />
	</div>
	<div class="underline">Documentation</div>
</a>
<a
	href="https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py#L417"
	class="block overflow-hidden group"
>
	<div
		class="w-full h-40 mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start overflow-hidden"
	>
		<img
			alt=""
			src="https://huggingface.co/spaces/sentence-transformers/README/resolve/main/push-to-hub.png"
			class="w-full h-40 object-cover overflow-hidden"
		/>
	</div>
	<div class="underline">Push your Sentence Transformers models to the Hub ❤️ </div>
</a>
<a
	href="https://huggingface.co/models?library=sentence-transformers&sort=downloads"
	class="block overflow-hidden group"
>
	<div
		class="w-full h-40 mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start overflow-hidden"
	>
		<img
			alt=""
			src="https://huggingface.co/spaces/sentence-transformers/README/resolve/main/sbert-hf.png"
			class="w-full h-40 object-cover overflow-hidden"
		/>
	</div>
	<div class="underline">Find all Sentence Transformers models on the 🤗 Hub</div>
</a>
<div class="lg:col-span-3">
	<p class="mb-4">
		To upload your Keras models to the Hub, you can use the <a
			href="https://github.com/huggingface/huggingface_hub/blob/1f83ed230932128fba8bfe2a7f0c78df66e6e3ee/src/huggingface_hub/keras_mixin.py#L60"
			>push_to_hub_keras</a
		>
		function.
	</p>
	<div
		class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
	>
		<pre
			class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
!pip install huggingface-hub
!huggingface-cli login
from huggingface_hub.keras_mixin import push_to_hub_keras
push_to_hub_keras(model = model, repo_url = "https://huggingface.co/your-username/your-awesome-model")
	</pre>
	</div>
</p>
<div class="lg:col-span-3">
	<p class="mb-4">
		To load Keras models from the 🤗Hub, use <a
			href="https://github.com/huggingface/huggingface_hub/blob/d3ba39a69bb5570eb7f31ce76a19b53fdc89728b/src/huggingface_hub/keras_mixin.py#L56"
			>from_pretrained_keras</a
		>
		function.
	</p>
	<div
		class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
	>
		<pre
			class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
!pip install huggingface-hub
!huggingface-cli login
from huggingface_hub.keras_mixin import from_pretrained_keras
from_pretrained_keras("your-username/your-awesome-model)
	</pre>
	</div>
<div class="lg:col-span-1">
	<p class="mb-4">
		If you'd like to upload 🤗Transformers based Keras checkpoints and let us host your metrics interactively in the repo in with TensorBoard, use <a
			href="https://huggingface.co/transformers/v4.12.5/_modules/transformers/keras_callbacks.html#PushToHubCallback"
			>PushToHubCallback</a
		>
		like follows:
	</p>
	<div
		class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
	>
		<pre
			class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
!pip install huggingface-hub
!huggingface-cli login
from transformers.keras_callbacks import PushToHubCallback
from tensorflow.keras.callbacks import TensorBoard
tensorboard_callback = TensorBoard(log_dir = "./logs/tensorboard)
push_to_hub_callback = PushToHubCallback(output_dir="./logs", 
										tokenizer=tokenizer,
										hub_model_id=model_id)

callbacks = [tensorboard_callback, push_to_hub_callback]
model.fit(..., callbacks=callbacks, ...)

</pre>

</div>