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.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

2_Dense/config.json

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+{
+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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3_Dense/config.json

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+{
+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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Prithvi-EO-V2-300M-TL-Sen1Floods11.pt

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Prithvi_EO_V2_300M_BurnScars.pt

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README.md

@@ -0,0 +1,222 @@
+---
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: feature-extraction
+tags:
+- granite
+- embeddings
+- transformers
+- mteb
+---
+
+# Granite-Embedding-English-R2
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**Model Summary:** Granite-embedding-english-r2 is a 149M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. 
+
+The r2 models show strong performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), 
+code retrieval (COIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), 
+table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases.
+
+These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-english-r2 is optimized to ensure strong alignment between query and passage embeddings.
+
+The latest granite embedding r2 release introduces two English embedding models, both based on the ModernBERT architecture:
+- **_granite-embedding-english-r2_** (**149M** parameters): with an output embedding size of _768_, replacing _granite-embedding-125m-english_. 
+- _granite-embedding-small-english-r2_ (**47M** parameters): A _first-of-its-kind_ reduced-size model, with fewer layers and a smaller output embedding size (_384_), replacing _granite-embedding-30m-english_. 
+
+## Model Details
+
+- **Developed by:** Granite Embedding Team, IBM
+- **Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
+- **Paper:** [Granite Embedding R2 Models](https://arxiv.org/abs/2508.21085)
+- **Language(s) (NLP):** English
+- **Release Date**: Aug 15, 2025
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Usage
+
+**Intended Use:** The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
+
+For efficient decoding, these models use Flash Attention 2. Installing it is optional, but can lead to faster inference.
+
+```shell
+pip install flash_attn==2.6.1
+```
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+**Usage with Sentence Transformers:** 
+
+The model is compatible with SentenceTransformer library and is very easy to use:
+
+First, install the sentence transformers library
+```shell
+pip install sentence_transformers
+```
+
+The model can then be used to encode pairs of text and find the similarity between their representations
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+model_path = "ibm-granite/granite-embedding-english-r2"
+# Load the Sentence Transformer model
+model = SentenceTransformer(model_path)
+
+input_queries = [
+    ' Who made the song My achy breaky heart? ',
+    'summit define'
+    ]
+
+input_passages = [
+    "Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
+    "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
+    ]
+
+# encode queries and passages. The model produces unnormalized vectors. If your task requires normalized embeddings pass normalize_embeddings=True to encode as below.
+query_embeddings = model.encode(input_queries)
+passage_embeddings = model.encode(input_passages)
+
+# calculate cosine similarity
+print(util.cos_sim(query_embeddings, passage_embeddings))
+```
+
+**Usage with Huggingface Transformers:** 
+
+This is a simple example of how to use the granite-embedding-english-r2 model with the Transformers library and PyTorch.
+
+First, install the required libraries
+```shell
+pip install transformers torch
+```
+
+The model can then be used to encode pairs of text
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_path = "ibm-granite/granite-embedding-english-r2"
+
+# Load the model and tokenizer
+model = AutoModel.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.eval()
+
+input_queries = [
+    ' Who made the song My achy breaky heart? ',
+    'summit define'
+    ]
+
+# tokenize inputs
+tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
+
+# encode queries
+with torch.no_grad():
+    # Queries
+    model_output = model(**tokenized_queries)
+    # Perform pooling. granite-embedding-278m-multilingual uses CLS Pooling
+    query_embeddings = model_output[0][:, 0]
+
+# normalize the embeddings
+query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
+
+```
+
+## Evaluation Results
+Granite embedding r2 models show a strong performance across tasks diverse tasks. 
+
+Performance of the granite models on MTEB Retrieval (i.e., BEIR), MTEB-v2, code retrieval (CoIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), 
+table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables),  benchmarks is reported in the below tables. 
+
+The r2 models demonstrates speed and efficiency, while mainintaining competitive performance. The average speed to encode documents on a single H100 GPU using a sliding window with 512 context length chunks is also reported. 
+
+| Model                              | Parameters (M) | Embedding Size | BEIR Retrieval (15) | MTEB-v2 (41)| CoIR (10) | MLDR (En) | MTRAG (4) |  Encoding Speed (docs/sec) |
+|------------------------------------|:--------------:|:--------------:|:-------------------:|:-----------:|:---------:|:---------:|:---------:|:-------------------------------:|
+| granite-embedding-125m-english     |      125       |      768       |        52.3         |     62.1   |   50.3    |   35.0    |   49.4   |               149             |
+| granite-embedding-30m-english      |       30       |      384       |        49.1         |     60.2   |   47.0    |   32.6    |   48.6   |               198             |
+| granite-embedding-english-r2       |      149       |      768       |        53.1         |     62.8   |   55.3    |   40.7    |   56.7   |               144              |
+| granite-embedding-small-english-r2 |       47       |      384       |        50.9         |     61.1   |   53.8    |   39.8    |   48.1   |               199             |
+
+
+|Model                              | Parameters (M) | Embedding Size |**AVERAGE**|MTEB-v2 Retrieval (10) | CoIR (10) | MLDR (En) | LongEmbed (6)| Table IR (5)| MTRAG(4) |  Encoding Speed (docs/sec) |
+|-----------------------------------|:--------------:|:--------------:|:---------:|:---------------------:|:---------:|:---------:|:------------:|:-----------:|:--------:|-------------------------------:|
+|e5-base-v2                         |109|768|47.5|49.7|50.3|32.5|41.1|74.09|37.0| 115|
+|bge-base-en-v1.5                   |109|768|46.9|54.8|46.6|33.5|33.9|73.98|38.8| 116|
+|snowflake-arctic-embed-m-v2.0      |305|768|51.4|58.4|52.2|32.4|55.4|80.75|29.2| 106|
+|gte-base-en-v1.5                   |137|768|52.8|55.5|42.4|42.7|59.4|80.52|36.0| 116|
+|gte-modernbert-base                |149|768|57.5|57.0|71.5|46.2|57.0|76.68|36.8| 142|
+|nomic-ai/modernbert-embed-base     |149|768|48.0|48.7|48.8|31.3|56.3|66.69|36.2| 141|
+|||||||||||
+|granite-embedding-english-r2       |149|768|**59.5**|56.4|54.8|41.6|67.8|78.53|57.6| 144|
+|granite-embedding-small-english-r2 | 47|384|55.6|53.9|53.4|40.1|61.9|75.51|48.9|199|
+
+
+### Model Architecture and Key Features
+
+The latest granite embedding r2 release introduces two English embedding models, both based on the ModernBERT architecture:
+- _granite-embedding-english-r2_ (**149M** parameters): with an output embedding size of _768_, replacing _granite-embedding-125m-english_. 
+- _granite-embedding-small-english-r2_ (**47M** parameters): A _first-of-its-kind_ reduced-size model, with fewer layers and a smaller output embedding size (_384_), replacing _granite-embedding-30m-english_. 
+
+The following table shows the structure of the two models:
+
+| Model                     | granite-embedding-small-english-r2 | **granite-embedding-english-r2**   |
+| :---------                | :-------:|:--------:| 
+| Embedding size            | 384      | **768**      | 
+| Number of layers          | 12       | **22**       | 
+| Number of attention heads | 12       | **12**       | 
+| Intermediate size         | 1536     | **1152**     | 
+| Activation Function       | GeGLU    | **GeGLU**    | 
+| Vocabulary Size           | 50368    | **50368**    | 
+| Max. Sequence Length      | 8192     | **8192**     | 
+| # Parameters              | 47M      | **149M**     | 
+
+
+### Training and Optimization
+
+The granite embedding r2 models incorporate key enhancements from the ModernBERT architecture, including: 
+- Alternating attention lengths to accelerate processing 
+- Rotary position embeddings for extended sequence length 
+- A newly trained tokenizer optimized with code and text data 
+- Flash Attention 2.0 for improved efficiency 
+- Streamlined parameters, eliminating unnecessary bias terms
+
+
+## Data Collection
+Granite embedding r2 models are trained using data from four key sources: 
+1. Unsupervised title-body paired data scraped from the web
+2. Publicly available paired with permissive, enterprise-friendly license
+3. IBM-internal paired data targetting specific technical domains
+4. IBM-generated synthetic data
+
+Notably, we _do not use_ the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license (many open-source models use this dataset due to its high quality). 
+
+The underlying encoder models using GneissWeb, an IBM-curated dataset composed exclusively of open, commercial-friendly sources.
+
+For governance, all our data undergoes a data clearance process subject to technical, business, and governance review. This comprehensive process captures critical information about the data, including but not limited to their content description ownership, intended use, data classification, licensing information, usage restrictions, how the data will be acquired, as well as an assessment of sensitive information (i.e, personal information). 
+
+## Infrastructure
+We trained the granite embedding english r2 models using IBM's computing cluster, BlueVela Cluster, which is outfitted with NVIDIA H100 80GB GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
+
+## Ethical Considerations and Limitations
+Granite-embedding-english-r2 leverages both permissively licensed open-source and select proprietary data for enhanced performance. The training data for the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-embedding-english-r2 is trained only for English texts, and has a context length of 8192 tokens (longer texts will be truncated to this size).
+
+- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
+- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
+- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
+
+## Citation
+```
+@misc{awasthy2025graniteembeddingr2models,
+      title={Granite Embedding R2 Models}, 
+      author={Parul Awasthy and Aashka Trivedi and Yulong Li and Meet Doshi and Riyaz Bhat and Vignesh P and Vishwajeet Kumar and Yushu Yang and Bhavani Iyer and Abraham Daniels and Rudra Murthy and Ken Barker and Martin Franz and Madison Lee and Todd Ward and Salim Roukos and David Cox and Luis Lastras and Jaydeep Sen and Radu Florian},
+      year={2025},
+      eprint={2508.21085},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.21085}, 
+}
+```

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

burn_scars_config.yaml

@@ -0,0 +1,104 @@
+# lightning.pytorch==2.4.0
+seed_everything: 2
+trainer:
+  logger: true
+  max_epochs: 100
+  log_every_n_steps: 1
+  callbacks:
+    - class_path: EarlyStopping
+      init_args:
+        monitor: val/loss
+        patience: 15
+    - class_path: LearningRateMonitor
+      init_args:
+        logging_interval: epoch
+  enable_progress_bar: false
+  precision: bf16-mixed
+
+model:
+  class_path: terratorch.tasks.SemanticSegmentationTask
+  init_args:
+    model_factory: EncoderDecoderFactory
+    model_args:
+      backbone: prithvi_eo_v2_300
+      backbone_pretrained: true
+      backbone_bands: ["BLUE", "GREEN", "RED", "NIR_NARROW", "SWIR_1", "SWIR_2"]
+      necks:
+        - name: SelectIndices
+          indices: [5, 11, 17, 23]
+        - name: ReshapeTokensToImage
+        - name: LearnedInterpolateToPyramidal
+      decoder: UNetDecoder
+      decoder_channels: [512, 256, 128, 64]
+      num_classes: 2
+    loss: ce
+    ignore_index: -1
+    freeze_backbone: false
+    plot_on_val: false
+    class_names: [Not burned, Burn scar]
+
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 1.e-4
+lr_scheduler:
+  class_path: ReduceLROnPlateau
+  init_args:
+    monitor: val/loss
+    factor: 0.5
+    patience: 4
+
+data:
+  class_path: GenericNonGeoSegmentationDataModule
+  init_args:
+    batch_size: 8
+    num_workers: 8
+    dataset_bands:  # Dataset bands
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+    output_bands: # Model input bands
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+    rgb_indices:
+      - 2
+      - 1
+      - 0
+    train_data_root: hls_burn_scars/data
+    val_data_root: hls_burn_scars/data
+    test_data_root: hls_burn_scars/data
+    train_split: hls_burn_scars/splits/train.txt
+    val_split: hls_burn_scars/splits/val.txt
+    test_split: hls_burn_scars/splits/test.txt
+    img_grep: "*_merged.tif"
+    label_grep: "*.mask.tif"
+    means:
+      -  0.033349706741586264
+      -  0.05701185520536176
+      -  0.05889748132001316
+      -  0.2323245113436119
+      -  0.1972854853760658
+      -  0.11944914225186566
+    stds:
+      -  0.02269135568823774
+      -  0.026807560223070237
+      -  0.04004109844362779
+      -  0.07791732423672691
+      -  0.08708738838140137
+      -  0.07241979477437814
+    num_classes: 2
+    train_transform:
+      - class_path: albumentations.D4
+      - class_path: ToTensorV2
+    test_transform:
+      - class_path: ToTensorV2
+
+    no_data_replace: 0
+    no_label_replace: -1

config.json

@@ -0,0 +1,48 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 50281,
+  "classifier_activation": "silu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 80000.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_activation": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "vocab_size": 50368
+}

config.yaml

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+# lightning.pytorch==2.4.0
+seed_everything: 0
+trainer:
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  precision: 16-mixed
+  logger: true
+  callbacks:
+  - class_path: lightning.pytorch.callbacks.RichProgressBar
+    init_args:
+      refresh_rate: 1
+      leave: false
+      theme:
+        description: white
+        progress_bar: '#6206E0'
+        progress_bar_finished: '#6206E0'
+        progress_bar_pulse: '#6206E0'
+        batch_progress: white
+        time: grey54
+        processing_speed: grey70
+        metrics: white
+        metrics_text_delimiter: ' '
+        metrics_format: .3f
+  - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+    init_args:
+      logging_interval: epoch
+      log_momentum: false
+      log_weight_decay: false
+  - class_path: lightning.pytorch.callbacks.EarlyStopping
+    init_args:
+      monitor: val/loss
+      min_delta: 0.0
+      patience: 20
+      verbose: false
+      mode: min
+      strict: true
+      check_finite: true
+      log_rank_zero_only: false
+  fast_dev_run: false
+  max_epochs: 50
+  max_steps: -1
+  overfit_batches: 0.0
+  check_val_every_n_epoch: 2
+  log_every_n_steps: 10
+  enable_checkpointing: true
+  accumulate_grad_batches: 1
+  inference_mode: true
+  use_distributed_sampler: true
+  detect_anomaly: false
+  barebones: false
+  sync_batchnorm: false
+  reload_dataloaders_every_n_epochs: 0
+  default_root_dir: /dccstor/geofm-finetuning/benchmark-geo-bench-paolo/
+model:
+  class_path: terratorch.tasks.SemanticSegmentationTask
+  init_args:
+    model_args:
+      backbone_pretrained: true
+      backbone: prithvi_eo_v2_300_tl
+      decoder: UperNetDecoder
+      decoder_channels: 256
+      decoder_scale_modules: true
+      num_classes: 2
+      rescale: true
+      backbone_bands:
+      - BLUE
+      - GREEN
+      - RED
+      - NIR_NARROW
+      - SWIR_1
+      - SWIR_2
+      head_dropout: 0.1
+      necks:
+      - name: SelectIndices
+        indices:
+        - 5
+        - 11
+        - 17
+        - 23
+      - name: ReshapeTokensToImage
+    model_factory: EncoderDecoderFactory
+    loss: ce
+    ignore_index: -1
+    lr: 0.001
+    freeze_backbone: false
+    freeze_decoder: false
+    plot_on_val: 10
+data:
+  class_path: terratorch.datamodules.Sen1Floods11NonGeoDataModule
+  init_args:
+    data_root: /dccstor/geofm-finetuning/datasets/sen1floods11
+    batch_size: 16
+    num_workers: 8
+    bands:
+    - BLUE
+    - GREEN
+    - RED
+    - NIR_NARROW
+    - SWIR_1
+    - SWIR_2
+    train_transform:
+    - class_path: albumentations.RandomCrop
+      init_args:
+        height: 224
+        width: 224
+        p: 1.0
+    - class_path: albumentations.HorizontalFlip
+      init_args:
+        p: 0.5
+    - class_path: albumentations.VerticalFlip
+      init_args:
+        p: 0.5
+    - class_path: albumentations.pytorch.ToTensorV2
+      init_args:
+        transpose_mask: false
+        p: 1.0
+    val_transform:
+    - class_path: albumentations.pytorch.ToTensorV2
+      init_args:
+        transpose_mask: false
+        p: 1.0
+    test_transform:
+    - class_path: albumentations.pytorch.ToTensorV2
+      init_args:
+        transpose_mask: false
+        p: 1.0
+    drop_last: true
+    constant_scale: 0.0001
+    no_data_replace: 0.0
+    no_label_replace: -1
+    use_metadata: false
+out_dtype: int16
+deploy_config_file: true
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 5.0e-05
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+    weight_decay: 0.05
+    amsgrad: false
+    maximize: false
+    capturable: false
+    differentiable: false
+lr_scheduler:
+  class_path: torch.optim.lr_scheduler.CosineAnnealingLR
+  init_args:
+    T_max: 50
+    eta_min: 0
+    last_epoch: -1

config_sentence_transformers.json

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+{
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+  "__version__": {
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+    "transformers": "4.57.0.dev0",
+    "pytorch": "2.8.0+cu128"
+  },
+  "prompts": {
+    "query": "task: search result | query: ",
+    "document": "title: none | text: ",
+    "BitextMining": "task: search result | query: ",
+    "Clustering": "task: clustering | query: ",
+    "Classification": "task: classification | query: ",
+    "InstructionRetrieval": "task: code retrieval | query: ",
+    "MultilabelClassification": "task: classification | query: ",
+    "PairClassification": "task: sentence similarity | query: ",
+    "Reranking": "task: search result | query: ",
+    "Retrieval": "task: search result | query: ",
+    "Retrieval-query": "task: search result | query: ",
+    "Retrieval-document": "title: none | text: ",
+    "STS": "task: sentence similarity | query: ",
+    "Summarization": "task: summarization | query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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generation_config.json

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+{
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.57.0.dev0"
+}

inference.py

@@ -0,0 +1,335 @@
+
+import argparse
+import os
+from typing import List, Union
+import re
+import datetime
+import numpy as np
+import rasterio
+import torch
+import yaml
+from einops import rearrange
+from terratorch.cli_tools import LightningInferenceModel
+
+NO_DATA = -9999
+NO_DATA_FLOAT = 0.0001
+OFFSET = 0
+PERCENTILE = 99
+
+
+def process_channel_group(orig_img, channels):
+    """
+    Args:
+        orig_img: torch.Tensor representing original image (reference) with shape = (bands, H, W).
+        channels: list of indices representing RGB channels.
+
+    Returns:
+        torch.Tensor with shape (num_channels, height, width) for original image
+    """
+
+    orig_img = orig_img[channels, ...]
+    valid_mask = torch.ones_like(orig_img, dtype=torch.bool)
+    valid_mask[orig_img == NO_DATA_FLOAT] = False
+
+
+    # Rescale (enhancing contrast)
+    max_value = max(3000, np.percentile(orig_img[valid_mask], PERCENTILE))
+    min_value = OFFSET
+
+    orig_img = torch.clamp((orig_img - min_value) / (max_value - min_value), 0, 1)
+
+    # No data as zeros
+    orig_img[~valid_mask] = 0
+
+    return orig_img
+
+
+def read_geotiff(file_path: str):
+    """Read all bands from *file_path* and return image + meta info.
+
+    Args:
+        file_path: path to image file.
+
+    Returns:
+        np.ndarray with shape (bands, height, width)
+        meta info dict
+    """
+
+    with rasterio.open(file_path) as src:
+        img = src.read()
+        meta = src.meta
+        try:
+            coords = src.lnglat()
+        except:
+            # Cannot read coords
+            coords = None
+
+    return img, meta, coords
+
+
+def save_geotiff(image, output_path: str, meta: dict):
+    """Save multi-band image in Geotiff file.
+
+    Args:
+        image: np.ndarray with shape (bands, height, width)
+        output_path: path where to save the image
+        meta: dict with meta info.
+    """
+
+    with rasterio.open(output_path, "w", **meta) as dest:
+        for i in range(image.shape[0]):
+            dest.write(image[i, :, :], i + 1)
+
+    return
+
+
+def _convert_np_uint8(float_image: torch.Tensor):
+    image = float_image.numpy() * 255.0
+    image = image.astype(dtype=np.uint8)
+
+    return image
+
+
+def load_example(
+    file_paths: List[str],
+    mean: List[float] = None,
+    std: List[float] = None,
+    indices: Union[list[int], None] = None,
+):
+    """Build an input example by loading images in *file_paths*.
+
+    Args:
+        file_paths: list of file paths .
+        mean: list containing mean values for each band in the images in *file_paths*.
+        std: list containing std values for each band in the images in *file_paths*.
+
+    Returns:
+        np.array containing created example
+        list of meta info for each image in *file_paths*
+    """
+
+    imgs = []
+    metas = []
+    temporal_coords = []
+    location_coords = []
+
+    for file in file_paths:
+        img, meta, coords = read_geotiff(file)
+
+        # Rescaling (don't normalize on nodata)
+        img = np.moveaxis(img, 0, -1)  # channels last for rescaling
+        if indices is not None:
+            img = img[..., indices]
+        if mean is not None and std is not None:
+            img = np.where(img == NO_DATA, NO_DATA_FLOAT, (img - mean) / std)
+
+        imgs.append(img)
+        metas.append(meta)
+        if coords is not None:
+            location_coords.append(coords)
+
+        try:
+            match = re.search(r'(\d{7,8}T\d{6})', file)
+            if match:
+                year = int(match.group(1)[:4])
+                julian_day = match.group(1).split('T')[0][4:]
+                if len(julian_day) == 3:
+                    julian_day = int(julian_day)
+                else:
+                    julian_day = datetime.datetime.strptime(julian_day, '%m%d').timetuple().tm_yday
+                temporal_coords.append([year, julian_day])
+        except Exception as e:
+            print(f'Could not extract timestamp for {file} ({e})')
+
+    imgs = np.stack(imgs, axis=0)  # num_frames, H, W, C
+    imgs = np.moveaxis(imgs, -1, 0).astype("float32")  # C, num_frames, H, W
+    imgs = np.expand_dims(imgs, axis=0)  # add batch di
+
+    return imgs, temporal_coords, location_coords, metas
+
+
+def run_model(input_data, model, datamodule, img_size):
+    # Reflect pad if not divisible by img_size
+    original_h, original_w = input_data.shape[-2:]
+    pad_h = (img_size - (original_h % img_size)) % img_size
+    pad_w = (img_size - (original_w % img_size)) % img_size
+    input_data = np.pad(
+        input_data, ((0, 0), (0, 0), (0, 0), (0, pad_h), (0, pad_w)), mode="reflect"
+    )
+
+    # Build sliding window
+
+    batch_size = 1
+    batch = torch.tensor(input_data, device="cpu")
+    windows = batch.unfold(3, img_size, img_size).unfold(4, img_size, img_size)
+    h1, w1 = windows.shape[3:5]
+    windows = rearrange(
+        windows, "b c t h1 w1 h w -> (b h1 w1) c t h w", h=img_size, w=img_size
+    )
+
+    # Split into batches if number of windows > batch_size
+    num_batches = windows.shape[0] // batch_size if windows.shape[0] > batch_size else 1
+    windows = torch.tensor_split(windows, num_batches, dim=0)
+
+    # Run model
+    pred_imgs = []
+    for x in windows:
+        # Apply standardization
+        x = datamodule.test_transform(image=x.squeeze().numpy().transpose(1,2,0))
+        x['image'] = x['image'].unsqueeze(0)
+        x = datamodule.aug(x)['image']
+
+        with torch.no_grad():
+            x = x.to(model.device)
+            pred = model(x)
+            pred = pred.output.detach().cpu()
+
+        y_hat = pred.argmax(dim=1)
+
+        y_hat = torch.nn.functional.interpolate(y_hat.unsqueeze(1).float(), size=img_size, mode="nearest")
+
+        pred_imgs.append(y_hat)
+
+    pred_imgs = torch.concat(pred_imgs, dim=0)
+
+    # Build images from patches
+    pred_imgs = rearrange(
+        pred_imgs,
+        "(b h1 w1) c h w -> b c (h1 h) (w1 w)",
+        h=img_size,
+        w=img_size,
+        b=1,
+        c=1,
+        h1=h1,
+        w1=w1,
+    )
+
+    # Cut padded area back to original size
+    pred_imgs = pred_imgs[..., :original_h, :original_w]
+
+    # Squeeze (batch size 1)
+    pred_imgs = pred_imgs[0]
+
+    return pred_imgs
+
+
+def main(
+    data_file: str,
+    config: str,
+    checkpoint: str,
+    output_dir: str,
+    rgb_outputs: bool,
+    input_indices: list[int] = None,
+):
+    os.makedirs(output_dir, exist_ok=True)
+
+    with open(config, "r") as f:
+        config_dict = yaml.safe_load(f)
+
+    # Load model ---------------------------------------------------------------------------------
+
+    lightning_model = LightningInferenceModel.from_config(config, checkpoint)
+    img_size = 512  # Size of BurnScars
+
+    # Loading data ---------------------------------------------------------------------------------
+
+    input_data, temporal_coords, location_coords, meta_data = load_example(
+        file_paths=[data_file], indices=input_indices,
+    )
+
+    meta_data = meta_data[0]  # only one image
+
+    if input_data.mean() > 1:
+        input_data = input_data / 10000  # Convert to range 0-1
+
+    # Running model --------------------------------------------------------------------------------
+
+    lightning_model.model.eval()
+
+    channels = config_dict['data']['init_args']['rgb_indices']
+
+    pred = run_model(input_data, lightning_model.model, lightning_model.datamodule, img_size)
+
+    # Save pred
+    meta_data.update(count=1, dtype="uint8", compress="lzw", nodata=0)
+    pred_file = os.path.join(output_dir, f"pred_{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+    save_geotiff(_convert_np_uint8(pred), pred_file, meta_data)
+
+    # Save image + pred
+    meta_data.update(count=3, dtype="uint8", compress="lzw", nodata=0)
+
+    if input_data.mean() < 1:
+        input_data = input_data * 10000  # Scale to 0-10000
+
+    rgb_orig = process_channel_group(
+        orig_img=torch.Tensor(input_data[0, :, 0, ...]),
+        channels=channels,
+    )
+
+    pred[pred == 0.] = np.nan
+    img_pred = rgb_orig * 0.7 + pred * 0.3
+    img_pred[img_pred.isnan()] = rgb_orig[img_pred.isnan()]
+
+    img_pred_file = os.path.join(output_dir, f"rgb_pred_{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+    save_geotiff(
+        image=_convert_np_uint8(img_pred),
+        output_path=img_pred_file,
+        meta=meta_data,
+    )
+
+    # Save image rgb
+    if rgb_outputs:
+        rgb_file = os.path.join(output_dir, f"original_rgb_{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+        save_geotiff(
+            image=_convert_np_uint8(rgb_orig),
+            output_path=rgb_file,
+            meta=meta_data,
+        )
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("run inference", add_help=False)
+
+    parser.add_argument(
+        "--data_file",
+        type=str,
+        default="examples/subsetted_512x512_HLS.S30.T10SEH.2018190.v1.4_merged.tif",
+        help="Path to the file.",
+    )
+    parser.add_argument(
+        "--config",
+        "-c",
+        type=str,
+        default="burn_scars_config.yaml",
+        help="Path to yaml file containing model parameters.",
+    )
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default="Prithvi_EO_V2_300M_BurnScars.pt",
+        help="Path to a checkpoint file to load from.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="Path to the directory where to save outputs.",
+    )
+    parser.add_argument(
+        "--input_indices",
+        default=[0,1,2,3,4,5],
+        type=int,
+        nargs="+",
+        help="0-based indices of the six Prithvi channels to be selected from the input. By default selects [0,1,2,3,4,5] for filtered HLS data.",
+    )
+    parser.add_argument(
+        "--rgb_outputs",
+        action="store_true",
+        help="If present, output files will only contain RGB channels. "
+        "Otherwise, all bands will be saved.",
+    )
+    args = parser.parse_args()
+
+    main(**vars(args))

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23bd9625b32680f180703408ed9d8d7b3a9c1efe4c950111e3ae9d9695b21b29
+size 298041696

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

notebook.ipynb

@@ -0,0 +1,656 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-u7xRR3DeFXz"
+      },
+      "source": [
+        "##### Copyright 2025 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "oed1Dh9SeIlD"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UpJl85mfqdUB"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.ipynb\"\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.ipynb\"><img src=\"https://www.kaggle.com/static/images/logos/kaggle-logo-transparent-300.png\" height=\"32\" width=\"70\"/>Run in Kaggle</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/google/generative-ai-docs/main/site/en/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.ipynb\"><img src=\"https://ai.google.dev/images/cloud-icon.svg\" width=\"40\" />Open in Vertex AI</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sq3lJyEiqqD-"
+      },
+      "source": [
+        "# Generate Embeddings with Sentence Transformers\n",
+        "\n",
+        "EmbeddingGemma is a lightweight, open embedding model designed for fast, high-quality retrieval on everyday devices like mobile phones. At only 308 million parameters, it's efficient enough to run advanced AI techniques, such as Retrieval Augmented Generation (RAG), directly on your local machine with no internet connection required.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "Before starting this tutorial, complete the following steps:\n",
+        "\n",
+        "* Get access to Gemma by logging into [Hugging Face](https://huggingface.co/google/embeddinggemma-300M) and selecting **Acknowledge license** for a Gemma model.\n",
+        "* Generate a Hugging Face [Access Token](https://huggingface.co/docs/hub/en/security-tokens#how-to-manage-user-access-token) and use it to login from Colab.\n",
+        "\n",
+        "This notebook will run on either CPU or GPU."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R3TOEqprq-X3"
+      },
+      "source": [
+        "### Install Python packages\n",
+        "\n",
+        "Install the libraries required for running the EmbeddingGemma model and generating embeddings. Sentence Transformers is a Python framework for text and image embeddings. For more information, see the [Sentence Transformers](https://www.sbert.net/) documentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jZFuhT3nrHEK"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U sentence-transformers git+https://github.com/huggingface/transformers@v4.56.0-Embedding-Gemma-preview"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O3ttIyfSA0Lj"
+      },
+      "source": [
+        "After you have accepted the license, you need a valid Hugging Face Token to access the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WXK1Ev1Sq2iY"
+      },
+      "outputs": [],
+      "source": [
+        "# Login into Hugging Face Hub\n",
+        "from huggingface_hub import login\n",
+        "login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NUydcaDBrXDi"
+      },
+      "source": [
+        "### Load Model\n",
+        "\n",
+        "Use the `sentence-transformers` libraries to create an instance of a model class with EmbeddingGemma."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mkpmqlU_rcOd",
+        "outputId": "f8458e59-9a6e-4a89-af83-ffdf391c323a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Device: cuda:0\n",
+            "SentenceTransformer(\n",
+            "  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})\n",
+            "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
+            "  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
+            "  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
+            "  (4): Normalize()\n",
+            ")\n",
+            "Total number of parameters in the model: 307581696\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from sentence_transformers import SentenceTransformer\n",
+        "\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "\n",
+        "model_id = \"google/embeddinggemma-300M\"\n",
+        "model = SentenceTransformer(model_id).to(device=device)\n",
+        "\n",
+        "print(f\"Device: {model.device}\")\n",
+        "print(model)\n",
+        "print(\"Total number of parameters in the model:\", sum([p.numel() for _, p in model.named_parameters()]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JxrZ8na0A7Hv"
+      },
+      "source": [
+        "## Generating Embedding\n",
+        "\n",
+        "An embedding is a numerical representation of text, like a word or sentence, that captures its semantic meaning. Essentially, it's a list of numbers (a vector) that allows computers to understand the relationships and context of words.\n",
+        "\n",
+        "Let's see how EmbeddingGemma would process three different words `[\"apple\", \"banana\", \"car\"]`.\n",
+        "\n",
+        "EmbeddingGemma has been trained on vast amounts of text and has learned the relationships between words and concepts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o0UK8UVAA9b7",
+        "outputId": "37c91847-57de-4a47-9c1a-0adffacd1867"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[-0.18476306  0.00167681  0.03773484 ... -0.07996225 -0.02348064\n",
+            "   0.00976741]\n",
+            " [-0.21189538 -0.02657359  0.02513712 ... -0.08042689 -0.01999852\n",
+            "   0.00512146]\n",
+            " [-0.18924113 -0.02551468  0.04486253 ... -0.06377774 -0.03699806\n",
+            "   0.03973572]]\n",
+            "Embedding 1: (768,)\n",
+            "Embedding 2: (768,)\n",
+            "Embedding 3: (768,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "words = [\"apple\", \"banana\", \"car\"]\n",
+        "\n",
+        "# Calculate embeddings by calling model.encode()\n",
+        "embeddings = model.encode(words)\n",
+        "\n",
+        "print(embeddings)\n",
+        "for idx, embedding in enumerate(embeddings):\n",
+        "    print(f\"Embedding {idx+1} (shape): {embedding.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "inuWOAuMBAR7"
+      },
+      "source": [
+        "The model outpus a numerical vector for each sentence. The actual vectors are very long (768), but for simplicity, those are presented with a few dimensions.\n",
+        "\n",
+        "The key isn't the individual numbers themselves, but **the distance between the vectors**. If we were to plot these vectors in a multi-dimensional space, The vectors for `apple` and `banana` would be very close to each other. And the vector for `car` would be far away from the other two."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2oCpMMJUr4RT"
+      },
+      "source": [
+        "## Determining Similarity\n",
+        "\n",
+        "In this section, we use embeddings to determine how sementically similar different sentences are. Here we show examples with high, medieum, and low similarity scores.\n",
+        "\n",
+        "- High Similarity:\n",
+        "  - Sentence A: \"The chef prepared a delicious meal for the guests.\"\n",
+        "  - Sentence B: \"A tasty dinner was cooked by the chef for the visitors.\"\n",
+        "  - Reasoning: Both sentences describe the same event using different words and grammatical structures (active vs. passive voice). They convey the same core meaning.\n",
+        "\n",
+        "- Medium Similarity:\n",
+        "  - Sentence A: \"She is an expert in machine learning.\"\n",
+        "  - Sentence B: \"He has a deep interest in artificial intelligence.\"\n",
+        "  - Reasoning: The sentences are related as machine learning is a subfield of artificial intelligence. However, they talk about different people with different levels of engagement (expert vs. interest).\n",
+        "\n",
+        "- Low Similarity:\n",
+        "  - Sentence A: \"The weather in Tokyo is sunny today.\"\n",
+        "  - Sentence B: \"I need to buy groceries for the week.\"\n",
+        "  - Reasoning: The two sentences are on completely unrelated topics and share no semantic overlap."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VeTEvnTyslyq",
+        "outputId": "b387529f-aad8-4150-e4f1-daef4f30cfc0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🙋‍♂️\n",
+            "['The chef prepared a delicious meal for the guests.', 'A tasty dinner was cooked by the chef for the visitors.']\n",
+            "`-> 🤖 score:  0.8002148\n",
+            "🙋‍♂️\n",
+            "['She is an expert in machine learning.', 'He has a deep interest in artificial intelligence.']\n",
+            "`-> 🤖 score:  0.45417833\n",
+            "🙋‍♂️\n",
+            "['The weather in Tokyo is sunny today.', 'I need to buy groceries for the week.']\n",
+            "`-> 🤖 score:  0.22262995\n"
+          ]
+        }
+      ],
+      "source": [
+        "# The sentences to encode\n",
+        "sentence_high = [\n",
+        "    \"The chef prepared a delicious meal for the guests.\",\n",
+        "    \"A tasty dinner was cooked by the chef for the visitors.\"\n",
+        "]\n",
+        "sentence_medium = [\n",
+        "    \"She is an expert in machine learning.\",\n",
+        "    \"He has a deep interest in artificial intelligence.\"\n",
+        "]\n",
+        "sentence_low = [\n",
+        "    \"The weather in Tokyo is sunny today.\",\n",
+        "    \"I need to buy groceries for the week.\"\n",
+        "]\n",
+        "\n",
+        "for sentence in [sentence_high, sentence_medium, sentence_low]:\n",
+        "    print(\"🙋‍♂️\")\n",
+        "    print(sentence)\n",
+        "    embeddings = model.encode(sentence)\n",
+        "    similarities = model.similarity(embeddings[0], embeddings[1])\n",
+        "    print(\"`-> 🤖 score: \", similarities.numpy()[0][0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "obfUiizULZE0"
+      },
+      "source": [
+        "### Using Prompts with EmbeddingGemma\n",
+        "\n",
+        "To generate the best embeddings with EmbeddingGemma, you should add an \"instructional prompt\" or \"task\" to the beginning of your input text. These prompts optimize the embeddings for specific tasks, such as document retrieval or question answering, and help the model distinguish between different input types, like a search query versus a document.\n",
+        "\n",
+        "#### How to Apply Prompts\n",
+        "\n",
+        "You can apply a prompt during inference in three ways.\n",
+        "\n",
+        "1.  **Using the `prompt` argument**<br>\n",
+        "    Pass the full prompt string directly to the `encode` method. This gives you precise control.\n",
+        "    ```python\n",
+        "    embeddings = model.encode(\n",
+        "        sentence,\n",
+        "        prompt=\"task: sentence similarity | query: \"\n",
+        "    )\n",
+        "    ```\n",
+        "2.  **Using the `prompt_name` argument**<br>\n",
+        "    Select a predefined prompt by its name. These prompts are loaded from the model's configuration or during its initialization.\n",
+        "    ```python\n",
+        "    embeddings = model.encode(sentence, prompt_name=\"STS\")\n",
+        "    ```\n",
+        "3.  **Using the Default Prompt**<br>\n",
+        "    If you don't specify either `prompt` or `prompt_name`, the system will automatically use the prompt set as `default_prompt_name`, if no default is set, then no prompt is applied.\n",
+        "    ```python\n",
+        "    embeddings = model.encode(sentence)\n",
+        "    ```\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0p3qe3WDJV-I",
+        "outputId": "5fa2638e-e67b-479b-fba4-ca89a22cd10e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Available tasks:\n",
+            " query: \"task: search result | query: \"\n",
+            " document: \"title: none | text: \"\n",
+            " BitextMining: \"task: search result | query: \"\n",
+            " Clustering: \"task: clustering | query: \"\n",
+            " Classification: \"task: classification | query: \"\n",
+            " InstructionRetrieval: \"task: code retrieval | query: \"\n",
+            " MultilabelClassification: \"task: classification | query: \"\n",
+            " PairClassification: \"task: sentence similarity | query: \"\n",
+            " Reranking: \"task: search result | query: \"\n",
+            " Retrieval: \"task: search result | query: \"\n",
+            " Retrieval-query: \"task: search result | query: \"\n",
+            " Retrieval-document: \"title: none | text: \"\n",
+            " STS: \"task: sentence similarity | query: \"\n",
+            " Summarization: \"task: summarization | query: \"\n",
+            "--------------------------------------------------------------------------------\n",
+            "🙋‍♂️\n",
+            "['The chef prepared a delicious meal for the guests.', 'A tasty dinner was cooked by the chef for the visitors.']\n",
+            "`-> 🤖 score:  0.9363755\n",
+            "🙋‍♂️\n",
+            "['She is an expert in machine learning.', 'He has a deep interest in artificial intelligence.']\n",
+            "`-> 🤖 score:  0.6425841\n",
+            "🙋‍♂️\n",
+            "['The weather in Tokyo is sunny today.', 'I need to buy groceries for the week.']\n",
+            "`-> 🤖 score:  0.38587403\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Available tasks:\")\n",
+        "for name, prefix in model.prompts.items():\n",
+        "    print(f\" {name}: \\\"{prefix}\\\"\")\n",
+        "print(\"-\"*80)\n",
+        "\n",
+        "for sentence in [sentence_high, sentence_medium, sentence_low]:\n",
+        "    print(\"🙋‍♂️\")\n",
+        "    print(sentence)\n",
+        "    embeddings = model.encode(sentence, prompt_name=\"STS\")\n",
+        "    similarities = model.similarity(embeddings[0], embeddings[1])\n",
+        "    print(\"`-> 🤖 score: \", similarities.numpy()[0][0])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2YAqPXDctw2w"
+      },
+      "source": [
+        "#### Use Case: Retrieval-Augmented Generation (RAG)\n",
+        "\n",
+        "For RAG systems, use the following `prompt_name` values to create specialized embeddings for your queries and documents:\n",
+        "\n",
+        "* **For Queries:** Use `prompt_name=\"Retrieval-query\"`.<br>\n",
+        "    ```python\n",
+        "    query_embedding = model.encode(\n",
+        "        \"How do I use prompts with this model?\",\n",
+        "        prompt_name=\"Retrieval-query\"\n",
+        "    )\n",
+        "    ```\n",
+        "\n",
+        "* **For Documents:** Use `prompt_name=\"Retrieval-document\"`. To further improve document embeddings, you can also include a title by using the `prompt` argument directly:<br>\n",
+        "  * **With a title:**<br>\n",
+        "    ```python\n",
+        "    doc_embedding = model.encode(\n",
+        "        \"The document text...\",\n",
+        "        prompt=\"title: Using Prompts in RAG | text: \"\n",
+        "    )\n",
+        "    ```\n",
+        "  * **Without a title:**<br>\n",
+        "    ```python\n",
+        "    doc_embedding = model.encode(\n",
+        "        \"The document text...\",\n",
+        "        prompt=\"title: none | text: \"\n",
+        "    )\n",
+        "    ```\n",
+        "\n",
+        "#### Further Reading\n",
+        "\n",
+        "* For details on all available EmbeddingGemma prompts, see the [model card](http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions).\n",
+        "* For general information on prompt templates, see the [Sentence Transformer documentation](https://sbert.net/examples/sentence_transformer/applications/computing-embeddings/README.html#prompt-templates).\n",
+        "* For a demo of RAG, see the [Simple RAG example](https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb) in the Gemma Cookbook.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aQh-QFAPsswb"
+      },
+      "source": [
+        "## Classification\n",
+        "\n",
+        "Classification is the task of assigning a piece of text to one or more predefined categories or labels. It's one of the most fundamental tasks in Natural Language Processing (NLP).\n",
+        "\n",
+        "A practical application of text classification is customer support ticket routing. This process automatically directs customer queries to the correct department, saving time and reducing manual work."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C2Ufawl-tXvr",
+        "outputId": "347bd68c-dfee-470d-eef7-e3af5d096e91"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[0.4673, 0.5145, 0.3604],\n",
+            "        [0.4191, 0.5010, 0.5966]])\n",
+            "tensor([1, 2])\n",
+            "🙋‍♂️ Excuse me, the app freezes on the login screen. It won't work even when I try to reset my password. -> 🤖 Technical Support\n",
+            "🙋‍♂️ I would like to inquire about your enterprise plan pricing and features for a team of 50 people. -> 🤖 Sales Inquiry\n"
+          ]
+        }
+      ],
+      "source": [
+        "labels = [\"Billing Issue\", \"Technical Support\", \"Sales Inquiry\"]\n",
+        "\n",
+        "sentence = [\n",
+        "    \"Excuse me, the app freezes on the login screen. It won't work even when I try to reset my password.\",\n",
+        "    \"I would like to inquire about your enterprise plan pricing and features for a team of 50 people.\",\n",
+        "]\n",
+        "\n",
+        "# Calculate embeddings by calling model.encode()\n",
+        "label_embeddings = model.encode(labels, prompt_name=\"Classification\")\n",
+        "embeddings = model.encode(sentence, prompt_name=\"Classification\")\n",
+        "\n",
+        "# Calculate the embedding similarities\n",
+        "similarities = model.similarity(embeddings, label_embeddings)\n",
+        "print(similarities)\n",
+        "\n",
+        "idx = similarities.argmax(1)\n",
+        "print(idx)\n",
+        "\n",
+        "for example in sentence:\n",
+        "    print(\"🙋‍♂️\", example, \"-> 🤖\", labels[idx[sentence.index(example)]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IRUU2EIDPSmW"
+      },
+      "source": [
+        "## Matryoshka Representation Learning (MRL)\n",
+        "\n",
+        "EmbeddingGemma leverages MRL to provide multiple embedding sizes from one model. It's a clever training method that creates a single, high-quality embedding where the most important information is concentrated at the beginning of the vector.\n",
+        "\n",
+        "This means you can get a smaller but still very useful embedding by simply taking the first `N` dimensions of the full embedding. Using smaller, truncated embeddings is significantly cheaper to store and faster to process, but this efficiency comes at the cost of potential lower quality of embeddings. MRL gives you the power to choose the optimal balance between this speed and accuracy for your application's specific needs.\n",
+        "\n",
+        "Let's use three words `[\"apple\", \"banana\", \"car\"]` and create simplified embeddings to see how MRL works."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B1q1F9I5PYSq",
+        "outputId": "a5b28e04-4783-4d79-ae82-3fac7e554a7a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "similarity function:  cosine\n",
+            "tensor([[0.7510, 0.6685]])\n",
+            "🙋‍♂️ apple vs. banana -> 🤖 score:  0.75102395\n",
+            "🙋‍♂️ apple vs. car -> 🤖 score:  0.6684626\n"
+          ]
+        }
+      ],
+      "source": [
+        "def check_word_similarities():\n",
+        "    # Calculate the embedding similarities\n",
+        "    print(\"similarity function: \", model.similarity_fn_name)\n",
+        "    similarities = model.similarity(embeddings[0], embeddings[1:])\n",
+        "    print(similarities)\n",
+        "\n",
+        "    for idx, word in enumerate(words[1:]):\n",
+        "        print(\"🙋‍♂️ apple vs.\", word, \"-> 🤖 score: \", similarities.numpy()[0][idx])\n",
+        "\n",
+        "# Calculate embeddings by calling model.encode()\n",
+        "embeddings = model.encode(words, prompt_name=\"STS\")\n",
+        "\n",
+        "check_word_similarities()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_iv1xG0TPxkm"
+      },
+      "source": [
+        "Now, for a faster application, you don't need a new model. Simply **truncate** the full embeddings to the first **512 dimensions**. For optimal results, it is also recommended to set `normalize_embeddings=True`, which scales the vectors to a unit length of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9Ue4aWh8PzdL",
+        "outputId": "176dabd4-9d9c-4ce9-c7e5-472ba47ed55f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Embedding 1: (512,)\n",
+            "Embedding 2: (512,)\n",
+            "Embedding 3: (512,)\n",
+            "--------------------------------------------------------------------------------\n",
+            "similarity function:  cosine\n",
+            "tensor([[0.7674, 0.7041]])\n",
+            "🙋‍♂️ apple vs. banana -> 🤖 score:  0.767427\n",
+            "🙋‍♂️ apple vs. car -> 🤖 score:  0.7040509\n"
+          ]
+        }
+      ],
+      "source": [
+        "embeddings = model.encode(words, truncate_dim=512, normalize_embeddings=True)\n",
+        "\n",
+        "for idx, embedding in enumerate(embeddings):\n",
+        "    print(f\"Embedding {idx+1}: {embedding.shape}\")\n",
+        "\n",
+        "print(\"-\"*80)\n",
+        "check_word_similarities()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lgkmgzfVP24M"
+      },
+      "source": [
+        "In extremely constrained environments, you can further shorten the embeddings to just **256 dimensions**. You can also use the more efficient **dot-product** for similarity calculations instead of the standard **cosine** similarity."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gi4NlPv-P4RS",
+        "outputId": "656d8d6a-1e79-41be-f17a-cab136bf27ea"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Embedding 1: (256,)\n",
+            "Embedding 2: (256,)\n",
+            "Embedding 3: (256,)\n",
+            "--------------------------------------------------------------------------------\n",
+            "similarity function:  dot\n",
+            "tensor([[0.7855, 0.7382]])\n",
+            "🙋‍♂️ apple vs. banana -> 🤖 score:  0.7854644\n",
+            "🙋‍♂️ apple vs. car -> 🤖 score:  0.7382126\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = SentenceTransformer(model_id, truncate_dim=256, similarity_fn_name=\"dot\").to(device=device)\n",
+        "embeddings = model.encode(words, prompt_name=\"STS\", normalize_embeddings=True)\n",
+        "\n",
+        "for idx, embedding in enumerate(embeddings):\n",
+        "    print(f\"Embedding {idx+1}: {embedding.shape}\")\n",
+        "\n",
+        "print(\"-\"*80)\n",
+        "check_word_similarities()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RYr9uSI_t3fm"
+      },
+      "source": [
+        "## Summary and next steps\n",
+        "\n",
+        "You are now equipped to generate high-quality text embeddings using EmbeddingGemma and the Sentence Transformers library. Apply these skills to build powerful features like semantic similarity, text classification, and Retrieval-Augmented Generation (RAG) systems, and continue exploring what's possible with Gemma models.\n",
+        "\n",
+        "Check out the following docs next:\n",
+        "\n",
+        "* [Fine-tune EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers)\n",
+        "* [Simple RAG example](https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb) in the Gemma Cookbook\n"
+      ]
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tf_model.h5

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