Upload embeddinggemma-2-lean sentence-transformers model (finetuned on openai_math 377K dep pairs)

46607a6 verified 25 days ago

17.1 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:346930
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: U card
    sentences:
      - region
      - clique
      - tokens
  - source_sentence: branch
    sentences:
      - block Half
      - bool Code
      - schedule
  - source_sentence: pair le
    sentences:
      - trans E
      - endpoint
      - selected
  - source_sentence: loop Trace
    sentences:
      - blocks
      - potential
      - Is Blowup
  - source_sentence: hit weight
    sentences:
      - machine
      - config
      - append
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: val ir
          type: val_ir
        metrics:
          - type: cosine_accuracy@1
            value: 0.2305
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3385
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.386
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.426
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2305
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11283333333333331
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.07719999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0426
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2305
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3385
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.386
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.426
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3258681126672316
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2940464285714284
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3016717487640095
            name: Cosine Map@100

SentenceTransformer

This is a sentence-transformers model trained. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Maximum Sequence Length: 1000000000000000019884624838656 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Supported Modalities: Text, Image, Audio, Video, Message

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'audio': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'architecture': 'EmbeddingGemma2Model'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    'resolution Cokernel Map comp',
]
documents = [
    'resolution Cokernel Map',
    'potential Of',
    'canonical exposure supported',
]
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([[0.8557, 0.3572, 0.2455]])

Evaluation

Metrics

Information Retrieval

Dataset: val_ir
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.2305
cosine_accuracy@3	0.3385
cosine_accuracy@5	0.386
cosine_accuracy@10	0.426
cosine_precision@1	0.2305
cosine_precision@3	0.1128
cosine_precision@5	0.0772
cosine_precision@10	0.0426
cosine_recall@1	0.2305
cosine_recall@3	0.3385
cosine_recall@5	0.386
cosine_recall@10	0.426
cosine_ndcg@10	0.3259
cosine_mrr@10	0.294
cosine_map@100	0.3017

Training Details

Training Dataset

Unnamed Dataset

Size: 346,930 training samples
Columns: anchor and positive
Approximate statistics based on the first 100 samples:
anchor positive
type string string

modality text text
details
min: 3 tokens
mean: 6.48 tokens
max: 11 tokens

min: 3 tokens
mean: 5.38 tokens
max: 11 tokens
Samples:

anchor positive

resolution Cokernel Map comp resolution Cokernel Map

potential Of continuous potential Of

sum fully exposed code success probability le canonical exposure supported

	anchor	positive
type	string	string
modality	text	text
details	min: 3 tokens mean: 6.48 tokens max: 11 tokens	min: 3 tokens mean: 5.38 tokens max: 11 tokens

anchor	positive
`resolution Cokernel Map comp`	`resolution Cokernel Map`
`potential Of continuous`	`potential Of`
`sum fully exposed code success probability le`	`canonical exposure supported`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 256
num_train_epochs: 2
warmup_steps: 200
bf16: True

All Hyperparameters

Click to expand

per_device_train_batch_size: 256
num_train_epochs: 2
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 200
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
dataloader_multiprocessing_context: None
dataloader_in_order: True
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
warmup_ratio: None

Training Logs

Epoch	Step	Training Loss	val_ir_cosine_ndcg@10
0.0737	100	3.5315	-
0.1475	200	2.8227	-
0.2212	300	2.6758	-
0.2950	400	2.5389	-
0.3687	500	2.4665	-
0.4425	600	2.3844	-
0.5162	700	2.3230	-
0.5900	800	2.2864	-
0.6637	900	2.2415	-
0.7375	1000	2.1594	0.3263
0.8112	1100	2.1646	-
0.8850	1200	2.1441	-
0.9587	1300	2.1340	-
1.0324	1400	1.9848	-
1.1062	1500	1.8459	-
1.1799	1600	1.8273	-
1.2537	1700	1.8094	-
1.3274	1800	1.7973	-
1.4012	1900	1.8163	-
1.4749	2000	1.7998	0.3268
1.5487	2100	1.8070	-
1.6224	2200	1.8098	-
1.6962	2300	1.7844	-
1.7699	2400	1.7933	-
1.8437	2500	1.7804	-
1.9174	2600	1.7810	-
1.9912	2700	1.7693	-
2.0	2712	-	0.3259

Training Time

Training: 43.3 minutes
Evaluation: 2.0 minutes
Total: 45.3 minutes

Framework Versions

Python: 3.13.2
Sentence Transformers: 6.1.0
Transformers: 5.19.0
PyTorch: 2.11.0+cu128
Accelerate: 1.15.0
Datasets: 5.1.0
Tokenizers: 0.23.3

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX

Sentence Transformers

@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

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}