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samanvitha7/semeval2026-bge_large-bge-large-all-bge_base-checkpoints

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Model Card

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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: BAAI/bge-base-en-v1.5
—Maximum Sequence Length: 512 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': 512, 'do_lower_case': True, 'architecture': 'BertModel'})
  (1): Pooling({'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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7884, 0.3899],
#         [0.7884, 1.0000, 0.3584],
#         [0.3899, 0.3584, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 2,733 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | sentence_2 | |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 13 tokens</li><li>mean: 166.26 tokens</li><li>max: 466 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 172.93 tokens</li><li>max: 366 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 181.3 tokens</li><li>max: 512 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | sentence_2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>A war veteran returns to his small hometown after years of service overseas, seeking solace and normalcy. Struggling with post-traumatic stress disorder, he isolates himself, spending his days repairing old motorcycles in his garage. When a local teenager asks for his help fixing her late father's bike, he reluctantly agrees, forming an unexpected bond. Their friendship helps him confront his memories of the war, while she learns to cope with her own grief. As tensions rise between the veteran and a neighbor with a history of resentment, the teenager intervenes, diffusing the situation and encouraging reconciliation. By the film's end, the veteran begins to reintegrate into the community, finding a renewed sense of purpose.</code> | <code>A former firefighter returns to his quiet coastal village after years of battling wildfires across the country, hoping to find peace and routine. Haunted by memories of his dangerous past, he retreats into solitude, spending his days restoring old fishing boats in a small shed by the harbor. When a young boy from the village asks for help repairing his late grandfather's dinghy, he reluctantly agrees, forging an unexpected friendship. Their connection allows him to confront his guilt and fears, while the boy learns to process his own loss. When tensions escalate between the firefighter and a fisherman with a longstanding grudge, the boy steps in to mediate, fostering a fragile truce. By the end, the firefighter begins to reconnect with the villagers, discovering a renewed sense of belonging.</code> | <code>A reclusive artist lives alone in a dilapidated lighthouse on a rocky coastline, painting abstract depictions of storms. Haunted by the tragic shipwreck that claimed his family years earlier, he avoids contact with the nearby fishing village. One day, a curious young boy sneaks into the lighthouse, fascinated by the artist's turbulent canvases. Initially hostile, the artist reluctantly allows the boy to observe his work, and the two develop an uneasy camaraderie. When a fierce storm threatens the village, the boy urges the artist to help rally the townsfolk to secure their boats and homes. Reluctantly, the artist steps outside for the first time in years, using his knowledge of the tides to guide them through the crisis. Afterward, he begins painting calmer seas, finally letting go of his grief and guilt.</code> | | <code>Lila and Ben were playing outside with their toys. They liked to make sand castles and pretend they were kings and queens. They had a big bucket of salt that they used to make the sand stick together. But then the sky became dark and gray. Lila heard a loud noise. It was thunder. She was scared of thunder. She grabbed her toys and ran to the house. "Ben, come on! It\</code> | <code>Maya and Alex were playing outside with their toys. They liked to build forts out of cardboard boxes and pretend they were explorers. They had a big bag of flour that they used to make the dough stick together. But then the sky turned dark and gray. Maya heard a loud noise. It was thunder. She was scared of thunder. She grabbed her toys and ran to the house. "Alex, come on! It\</code> | <code>Elena and Marco were preparing a banquet in the bustling kitchen of the city hotel. They liked to create elaborate desserts and pretend they were culinary artists. They had a large bowl of sugar that they used to glaze the pastries. But then the sky turned dark and gray. Elena heard a loud noise. It was thunder. She was nervous about the storm. She grabbed her apron and ran to the pantry. "Marco, come on! It\</code> | | <code>A brilliant but reckless scientist working for a government research facility accidentally exposes herself to an experimental quantum energy field during a late-night laboratory accident. Initially appearing unharmed, she soon discovers she can phase through solid objects at will, though she struggles to control this newfound ability. When her former colleague steals the research data and sells it to a criminal organization intent on weaponizing the technology, she must learn to master her powers to stop them. Despite her scientific background, she finds that understanding her abilities requires intuition rather than logic, leading to a series of failed attempts to infiltrate the criminals' headquarters. With the help of a veteran security guard who becomes her mentor, she finally gains control over her phasing ability and successfully prevents the technology from being mass-produced. The film concludes with her decision to use her powers to protect others, adopting a new identity as a...</code> | <code>A gifted but impulsive engineer employed at a corporate tech laboratory accidentally absorbs radiation from a prototype teleportation device during an unauthorized midnight experiment. Though initially showing no symptoms, she gradually realizes she can become invisible at will, but lacks precise control over when the ability activates or deactivates. Her situation becomes urgent when a trusted research partner betrays the team by delivering classified blueprints to a terrorist cell planning to militarize the invisibility technology. Despite her technical expertise, she discovers that mastering her condition depends more on emotional discipline than scientific knowledge, resulting in multiple botched reconnaissance missions at the terrorists' warehouse compound. Under the guidance of an experienced night watchman who becomes her unlikely coach, she eventually achieves mastery over her invisibility powers and thwarts the mass distribution of the dangerous technology. She ultimately choo...</code> | <code>A veteran park ranger discovers an ancient cave system beneath Yellowstone during a routine geological survey, inadvertently awakening a colony of bioluminescent organisms that have remained dormant for centuries. The organisms begin rapidly spreading throughout the park's underground water systems, causing geysers to emit an eerie blue glow that attracts massive crowds of tourists and scientific researchers. As media attention intensifies, the ranger realizes the organisms are actually dying from exposure to surface contaminants, and their luminescence is a distress signal rather than a natural phenomenon. Working against park officials who want to exploit the discovery for tourism revenue, she collaborates with a retired mycologist to develop a protective barrier around the cave system. Their efforts to relocate the organisms to a deeper, more pristine environment initially fail when the creatures reject the artificial habitat. Through patient observation rather than scientific inter...</code> |
—Loss: <code>TripletLoss</code> with these parameters:

json

  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.35
  }

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—num_train_epochs: 6
—fp16: True
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—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: 6
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_ratio: None
—warmup_steps: 0
—log_level: passive
—log_level_replica: warning
—log_on_each_node: True
—logging_nan_inf_filter: True
—enable_jit_checkpoint: False
—save_on_each_node: False
—save_only_model: False
—restore_callback_states_from_checkpoint: False
—use_cpu: False
—seed: 42
—data_seed: None
—bf16: False
—fp16: True
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—local_rank: -1
—ddp_backend: None
—debug: []
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_prefetch_factor: None
—disable_tqdm: False
—remove_unused_columns: True
—label_names: None
—load_best_model_at_end: False
—ignore_data_skip: False
—fsdp: []
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamw_torch
—optim_args: None
—group_by_length: False
—length_column_name: length
—project: huggingface
—trackio_space_id: trackio
—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
—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_for_metrics: []
—eval_do_concat_batches: True
—auto_find_batch_size: False
—full_determinism: False
—ddp_timeout: 1800
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—include_num_input_tokens_seen: no
—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: True
—use_cache: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss
2.9240	500	0.0192
5.8480	1000	0.0003

Framework Versions

—Python: 3.11.7
—Sentence Transformers: 5.2.2
—Transformers: 5.0.0
—PyTorch: 2.5.1+cu121
—Accelerate: 1.10.1
—Datasets: 4.2.0
—Tokenizers: 0.22.2

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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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