metadata
base_model: srikarvar/multilingual-e5-small-pairclass-contrastive
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:246
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How to tie a tie?
sentences:
- How to identify diabetes?
- How to reset a password
- Instructions for tying a tie
- source_sentence: What are the benefits of meditation?
sentences:
- First President of the USA
- Advantages of meditation
- Name the capital of Canada
- source_sentence: How to improve English vocabulary?
sentences:
- Methods to improve English vocabulary
- Methods for saving money efficiently
- Current Prime Minister of the United Kingdom
- source_sentence: What are the symptoms of COVID-19?
sentences:
- COVID-19 symptoms
- Current population of India
- Tesla's Chief Executive Officer
- source_sentence: What time does the event start?
sentences:
- When does the event begin?
- Japan's capital city
- Tips for efficient time management
model-index:
- name: e5 cogcache small refined
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: e5 cogcache small refined
type: e5-cogcache-small-refined
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8571428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8571428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 1
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7634769642911022
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6845238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6845238095238094
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.5
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8571428571428571
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.2857142857142857
name: Dot Precision@3
- type: dot_precision@5
value: 0.20000000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.10000000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.5
name: Dot Recall@1
- type: dot_recall@3
value: 0.8571428571428571
name: Dot Recall@3
- type: dot_recall@5
value: 1
name: Dot Recall@5
- type: dot_recall@10
value: 1
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7634769642911022
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6845238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.6845238095238094
name: Dot Map@100
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8571428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8571428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 1
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7634769642911022
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6845238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6845238095238094
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.5
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8571428571428571
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.2857142857142857
name: Dot Precision@3
- type: dot_precision@5
value: 0.20000000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.10000000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.5
name: Dot Recall@1
- type: dot_recall@3
value: 0.8571428571428571
name: Dot Recall@3
- type: dot_recall@5
value: 1
name: Dot Recall@5
- type: dot_recall@10
value: 1
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7634769642911022
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6845238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.6845238095238094
name: Dot Map@100
e5 cogcache small refined
This is a sentence-transformers model finetuned from srikarvar/multilingual-e5-small-pairclass-contrastive. 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 Type: Sentence Transformer
- Base model: srikarvar/multilingual-e5-small-pairclass-contrastive
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Language: en
- License: apache-2.0
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: 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("srikarvar/fine_tuned_model_3")
# Run inference
sentences = [
'What time does the event start?',
'When does the event begin?',
"Japan's capital city",
]
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]
Evaluation
Metrics
Information Retrieval
- Dataset:
e5-cogcache-small-refined
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.5 |
cosine_accuracy@3 | 0.8571 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.5 |
cosine_precision@3 | 0.2857 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.5 |
cosine_recall@3 | 0.8571 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.7635 |
cosine_mrr@10 | 0.6845 |
cosine_map@100 | 0.6845 |
dot_accuracy@1 | 0.5 |
dot_accuracy@3 | 0.8571 |
dot_accuracy@5 | 1.0 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 0.5 |
dot_precision@3 | 0.2857 |
dot_precision@5 | 0.2 |
dot_precision@10 | 0.1 |
dot_recall@1 | 0.5 |
dot_recall@3 | 0.8571 |
dot_recall@5 | 1.0 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 0.7635 |
dot_mrr@10 | 0.6845 |
dot_map@100 | 0.6845 |
Information Retrieval
- Dataset:
e5-cogcache-small-refined
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.5 |
cosine_accuracy@3 | 0.8571 |
cosine_accuracy@5 | 1.0 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.5 |
cosine_precision@3 | 0.2857 |
cosine_precision@5 | 0.2 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.5 |
cosine_recall@3 | 0.8571 |
cosine_recall@5 | 1.0 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.7635 |
cosine_mrr@10 | 0.6845 |
cosine_map@100 | 0.6845 |
dot_accuracy@1 | 0.5 |
dot_accuracy@3 | 0.8571 |
dot_accuracy@5 | 1.0 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 0.5 |
dot_precision@3 | 0.2857 |
dot_precision@5 | 0.2 |
dot_precision@10 | 0.1 |
dot_recall@1 | 0.5 |
dot_recall@3 | 0.8571 |
dot_recall@5 | 1.0 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 0.7635 |
dot_mrr@10 | 0.6845 |
dot_map@100 | 0.6845 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 246 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 6 tokens
- mean: 9.6 tokens
- max: 14 tokens
- min: 4 tokens
- mean: 8.28 tokens
- max: 13 tokens
- Samples:
anchor positive How to speak confidently?
Tips for confident speaking
How to manage time effectively?
Tips for efficient time management
Where can I find a good restaurant?
Suggestions for a good restaurant
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 1e-05num_train_epochs
: 2warmup_ratio
: 0.1batch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: linearlr_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
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_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
: Falseignore_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_torchoptim_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | e5-cogcache-small-refined_cosine_map@100 |
---|---|---|---|
0 | 0 | - | 0.7024 |
0.625 | 10 | 0.0252 | - |
1.0 | 16 | - | 0.6845 |
1.25 | 20 | 0.0119 | - |
1.875 | 30 | 0.0035 | - |
2.0 | 32 | - | 0.6845 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- Datasets: 2.19.1
- 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",
}
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}
}