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sentence-transformers-stsb

dekshitha-k / sentence-transformers-stsb

Run on huggingface.co
huggingface.co
Total runs: 111
24-hour runs: -14
7-day runs: 13
30-day runs: 26
Model's Last Updated: November 13 2024
sentence-similarity

Introduction of sentence-transformers-stsb

Model Details of sentence-transformers-stsb

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 . It maps sentences & paragraphs to a 384-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 Sources
Full Model Architecture 
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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): 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. 

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("dekshitha-k/sentence-transformers-stsb")
# Run inference
sentences = [
    'Egypt imposes state of emergency after 95 people killed',
    'Egypt announces one-month state of emergency nationwide',
    "The arrests came just days after Israeli troops shot and killed Abdullah Kawasme, the militant group's leader in Hebron.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Dataset
Unnamed Dataset
  • Size: 5,749 training samples
  • Columns: sentence_0 , sentence_1 , and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 6 tokens
    • mean: 14.49 tokens
    • max: 70 tokens
    • min: 6 tokens
    • mean: 14.45 tokens
    • max: 63 tokens
    • min: 0.0
    • mean: 0.55
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    Dozens dead in Central African Republic fighting 98 dead in Central African Republic after clashes 0.68
    Dean told reporters traveling on his 10-city "Sleepless Summer" tour that he considered campaigning in Texas a challenge. Today, Dean ends his four-day, 10-city "Sleepless Summer" tour in Chicago and New York. 0.52
    The WiFi potties were to be unveiled this summer, at music festivals in Britain. The world's first portal potty was soon to be rolled out at summer festivals in Great Britain. 0.8
  • Loss: CosineSimilarityLoss with these parameters: 
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}
Training Hyperparameters
Non-Default Hyperparameters
  • per_device_train_batch_size : 16
  • per_device_eval_batch_size : 16
  • num_train_epochs : 20
  • 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 : 16
  • per_device_eval_batch_size : 16
  • 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 : 20
  • 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}
  • deepspeed : None
  • label_smoothing_factor : 0.0
  • optim : adamw_torch
  • 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 : False
  • hub_always_push : False
  • 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
  • dispatch_batches : None
  • split_batches : 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
  • eval_use_gather_object : False
  • average_tokens_across_devices : False
  • prompts : None
  • batch_sampler : batch_sampler
  • multi_dataset_batch_sampler : round_robin
Training Logs
Epoch Step Training Loss
1.3889 500 0.0293
2.7778 1000 0.0242
4.1667 1500 0.0217
5.5556 2000 0.0194
6.9444 2500 0.0176
8.3333 3000 0.0154
9.7222 3500 0.0136
11.1111 4000 0.012
12.5 4500 0.0101
13.8889 5000 0.0087
15.2778 5500 0.0076
16.6667 6000 0.0064
18.0556 6500 0.0056
19.4444 7000 0.0049
Framework Versions
  • Python: 3.10.12
  • Sentence Transformers: 3.3.0
  • Transformers: 4.46.2
  • PyTorch: 2.5.0+cu121
  • Accelerate: 1.1.1
  • Datasets: 3.1.0
  • Tokenizers: 0.20.3
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",
}

Runs of dekshitha-k sentence-transformers-stsb on huggingface.co

111
Total runs
-14
24-hour runs
6
3-day runs
13
7-day runs
26
30-day runs

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