This is a sentence-transformers model finetuned from google/embeddinggemma-300m. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: google/embeddinggemma-300m
Maximum Sequence Length: 2048 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yasserrmd/geo-gemma-300m-emb")
# Run inference
queries = [
    "Based on the Brine Shrimp Lethality Test (BSLT), what are the toxicity levels of liquid smoke from cocoa pod skin at various pyrolysis temperatures and water contents?",
]
documents = [
    'The Brine Shrimp Lethality Test (BSLT) was used to determine the toxicity levels of liquid smoke from cocoa pod skin at various pyrolysis temperatures and water contents. The results showed that the LC50 values (the concentration required to kill 50% of the test organisms) were as follows: at 200°C and 10% water content, 11,858.58 ppm; at 200°C and 15% water content, 13,094.23 ppm; at 200°C and 20% water content, 13,373.94 ppm; at 200°C and 25% water content, 15,703.52 ppm. At 300°C and 10% water content, 11,604.26 ppm; at 300°C and 15% water content, 11,673.05 ppm; at 300°C and 20% water content, 13,373.94 ppm; at 300°C and 25% water content, 13,373.94 ppm. At 400°C and 10% water content, 9,213.73 ppm; at 400°C and 15% water content, 13,094.237 ppm; at 400°C and 20% water content, 13,373.94 ppm; at 400°C and 25% water content, 12,493.63 ppm. All the results indicate that the liquid smoke from cocoa pod skin at different pyrolysis temperatures and water contents is classified as non-toxic.',
    'The estimated annual flood damage for agriculture and built-up areas in the Tajan watershed, northern Iran, is projected to surge from USD 162 million to USD 376 million and USD 91 million to USD 220 million, respectively, by 2040, considering the land use change scenarios from 2021 to 2040.',
    'The distribution of PM2.5 in Santa Ana, CA, tends to be higher in socioeconomically disadvantaged communities compared to other areas, highlighting environmental health inequities that persist in urban areas. This can inform policy decisions related to health equity and community access to resources.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5805, 0.0253, 0.0709]])

Training Details

Training Dataset

Unnamed Dataset

Size: 41,432 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 12 tokens
mean: 27.1 tokens
max: 71 tokens
min: 17 tokens
mean: 119.32 tokens
max: 413 tokens

Samples:

sentence_0	sentence_1
`How does plastic debris from land-based sources impact the ocean, particularly in the context of First Long Beach, China?`	Plastic debris from land-based sources can significantly impact the ocean, as seen in the study conducted at First Long Beach (FLB), China. The study found that plastic debris amounts ranged from 2 to 82 particles per square meter on this marine sand beach. The most common size of plastics was 0.5–2.5 cm (44.4%), and the most common color was white (60.9%). The most abundant shape of plastic debris was fragments (76.2%). The amount of plastic debris varied significantly between different transects along the land-based source input zone due to the impacts of wind, ocean currents, and waves. Land-based wastewater discharge was identified as a major source of plastic debris on FLB, influenced by coastal water tide variations. Reduction strategies should focus on tracing and managing these land-based sources to mitigate the impact of plastic debris on the ocean.
`How does the concentration of SO2 in urban areas of Nanjing correlate with the normalized difference vegetation index (NDVI), and what does this imply for public health?`	The concentration of SO2 in urban areas of Nanjing exhibits a strong correlation (coefficient of determination, R2 > 0.5) with the normalized difference vegetation index (NDVI) within a radial distance of 2 km from the air pollutant monitoring sites. This indicates that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants such as SO2. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations, which is crucial for public health interventions and policies.
`How has the rise of user-generated geodata impacted the role of traditional map producers?`	The rise of user-generated geodata has transformed ordinary citizens into neogeographers, blurring the boundaries between traditional map producers, such as national mapping agencies and local authorities, and citizens as consumers of this information. Citizens now actively participate in mapping different types of features on the Earth’s surface as volunteers, either by providing observations on the ground or tracing data from other sources, such as aerial photographs or satellite imagery. This has resulted in a significant increase in the availability of rich spatial datasets, which are often openly accessible through platforms like OpenStreetMap (OSM) and Ushahidi.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0965	500	0.012
0.1931	1000	0.006
0.2896	1500	0.0057
0.3862	2000	0.0045
0.4827	2500	0.0024
0.5793	3000	0.0013
0.6758	3500	0.0025
0.7723	4000	0.0029
0.8689	4500	0.0012
0.9654	5000	0.0004

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 27.1 tokens max: 71 tokens	min: 17 tokens mean: 119.32 tokens max: 413 tokens