SentenceTransformer based on intfloat/multilingual-e5-large
This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the json dataset. It maps sentences & paragraphs to a 1024-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: intfloat/multilingual-e5-large
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
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': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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("luka023/proba")
# Run inference
sentences = [
"'Super Jenki' sistem uključuje pet događaja i 26 pojedinačnih opklada, takođe poznat kao kanadski sistem.",
"Šta je 'Super Jenki' sistem klađenja?",
'Kako klađenje uživo funkcioniše u tenisu?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.8261 |
cosine_accuracy@3 | 0.9565 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.8261 |
cosine_precision@3 | 0.3188 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.8261 |
cosine_recall@3 | 0.9565 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9271 |
cosine_mrr@10 | 0.9022 |
cosine_map@100 | 0.9022 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.8696 |
cosine_accuracy@3 | 1.0 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.8696 |
cosine_precision@3 | 0.3333 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.8696 |
cosine_recall@3 | 1.0 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9462 |
cosine_mrr@10 | 0.9275 |
cosine_map@100 | 0.9275 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.8261 |
cosine_accuracy@3 | 1.0 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.8261 |
cosine_precision@3 | 0.3333 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.8261 |
cosine_recall@3 | 1.0 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9301 |
cosine_mrr@10 | 0.9058 |
cosine_map@100 | 0.9058 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7826 |
cosine_accuracy@3 | 0.9565 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.7826 |
cosine_precision@3 | 0.3188 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.7826 |
cosine_recall@3 | 0.9565 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9092 |
cosine_mrr@10 | 0.8783 |
cosine_map@100 | 0.8783 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.8261 |
cosine_accuracy@3 | 0.9565 |
cosine_accuracy@5 | 0.9565 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.8261 |
cosine_precision@3 | 0.3188 |
cosine_precision@5 | 0.1913 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.8261 |
cosine_recall@3 | 0.9565 |
cosine_recall@5 | 0.9565 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9164 |
cosine_mrr@10 | 0.8895 |
cosine_map@100 | 0.8895 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 198 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 198 samples:
positive anchor type string string details - min: 19 tokens
- mean: 33.76 tokens
- max: 53 tokens
- min: 6 tokens
- mean: 12.87 tokens
- max: 21 tokens
- Samples:
positive anchor Klađenje na ukupan broj poena timova podrazumeva predviđanje da li će jedan tim postići više ili manje poena od postavljene granice, nezavisno od konačnog ishoda.
Kako funkcioniše klađenje na ukupan broj poena timova?
Konačan ishod podrazumeva klađenje na to ko će pobediti u utakmici, pri čemu postoje tri mogućnosti: pobeda domaćina, pobeda gosta ili nerešeno.
Šta znači klađenje na konačan ishod?
Patent opklada uključuje tri događaja sa ukupno sedam pojedinačnih opklada: tri singl, tri dubl i jedna trostruka opklada.
Šta je patent opklada?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 4lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Falseload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
---|---|---|---|---|---|---|
1.0 | 1 | 0.6717 | 0.7663 | 0.8229 | 0.5755 | 0.8242 |
2.0 | 2 | 0.7779 | 0.8457 | 0.8638 | 0.7833 | 0.8635 |
3.0 | 4 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 |
1.0 | 1 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 |
2.0 | 2 | 0.8845 | 0.8732 | 0.9022 | 0.858 | 0.9022 |
3.0 | 4 | 0.8783 | 0.9058 | 0.9275 | 0.8895 | 0.9022 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 3.0.0
- Tokenizers: 0.19.1
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",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
MultipleNegativesRankingLoss
@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}
}
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Model tree for luka023/proba
Base model
intfloat/multilingual-e5-largeEvaluation results
- Cosine Accuracy@1 on dim 768self-reported0.826
- Cosine Accuracy@3 on dim 768self-reported0.957
- Cosine Accuracy@5 on dim 768self-reported1.000
- Cosine Accuracy@10 on dim 768self-reported1.000
- Cosine Precision@1 on dim 768self-reported0.826
- Cosine Precision@3 on dim 768self-reported0.319
- Cosine Precision@5 on dim 768self-reported0.200
- Cosine Precision@10 on dim 768self-reported0.100
- Cosine Recall@1 on dim 768self-reported0.826
- Cosine Recall@3 on dim 768self-reported0.957