SentenceTransformer based on intfloat/multilingual-e5-small
This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- 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': 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_4")
# Run inference
sentences = [
'Who is the President of the United States?',
'Who is the current US President?',
'What is the velocity of sound?',
]
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
Binary Classification
- Dataset:
pair-class-dev
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.6207 |
cosine_accuracy_threshold | 0.9036 |
cosine_f1 | 0.7193 |
cosine_f1_threshold | 0.9036 |
cosine_precision | 0.5827 |
cosine_recall | 0.9394 |
cosine_ap | 0.6366 |
dot_accuracy | 0.6207 |
dot_accuracy_threshold | 0.9036 |
dot_f1 | 0.7193 |
dot_f1_threshold | 0.9036 |
dot_precision | 0.5827 |
dot_recall | 0.9394 |
dot_ap | 0.6366 |
manhattan_accuracy | 0.6176 |
manhattan_accuracy_threshold | 6.5018 |
manhattan_f1 | 0.7232 |
manhattan_f1_threshold | 7.1429 |
manhattan_precision | 0.5724 |
manhattan_recall | 0.9818 |
manhattan_ap | 0.6414 |
euclidean_accuracy | 0.6207 |
euclidean_accuracy_threshold | 0.4391 |
euclidean_f1 | 0.7193 |
euclidean_f1_threshold | 0.4391 |
euclidean_precision | 0.5827 |
euclidean_recall | 0.9394 |
euclidean_ap | 0.6366 |
max_accuracy | 0.6207 |
max_accuracy_threshold | 6.5018 |
max_f1 | 0.7232 |
max_f1_threshold | 7.1429 |
max_precision | 0.5827 |
max_recall | 0.9818 |
max_ap | 0.6414 |
Binary Classification
- Dataset:
pair-class-test
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.8934 |
cosine_accuracy_threshold | 0.777 |
cosine_f1 | 0.9034 |
cosine_f1_threshold | 0.775 |
cosine_precision | 0.8503 |
cosine_recall | 0.9636 |
cosine_ap | 0.9467 |
dot_accuracy | 0.8934 |
dot_accuracy_threshold | 0.777 |
dot_f1 | 0.9034 |
dot_f1_threshold | 0.775 |
dot_precision | 0.8503 |
dot_recall | 0.9636 |
dot_ap | 0.9467 |
manhattan_accuracy | 0.8903 |
manhattan_accuracy_threshold | 9.9086 |
manhattan_f1 | 0.9003 |
manhattan_f1_threshold | 10.4374 |
manhattan_precision | 0.8495 |
manhattan_recall | 0.9576 |
manhattan_ap | 0.9452 |
euclidean_accuracy | 0.8934 |
euclidean_accuracy_threshold | 0.6678 |
euclidean_f1 | 0.9034 |
euclidean_f1_threshold | 0.6708 |
euclidean_precision | 0.8503 |
euclidean_recall | 0.9636 |
euclidean_ap | 0.9467 |
max_accuracy | 0.8934 |
max_accuracy_threshold | 9.9086 |
max_f1 | 0.9034 |
max_f1_threshold | 10.4374 |
max_precision | 0.8503 |
max_recall | 0.9636 |
max_ap | 0.9467 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 1,273 training samples
- Columns:
sentence1
,label
, andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 label sentence2 type string int string details - min: 6 tokens
- mean: 10.93 tokens
- max: 28 tokens
- 0: ~48.90%
- 1: ~51.10%
- min: 5 tokens
- mean: 10.29 tokens
- max: 22 tokens
- Samples:
sentence1 label sentence2 What are the main ingredients in a traditional pizza Margherita?
1
What ingredients are used in a classic pizza Margherita?
Release date of the iPhone 14
0
Release date of the iPhone 13
Who won the first Nobel Prize in Literature?
0
Who won the first Nobel Prize in Peace?
- Loss:
OnlineContrastiveLoss
Evaluation Dataset
Unnamed Dataset
- Size: 319 evaluation samples
- Columns:
sentence1
,label
, andsentence2
- Approximate statistics based on the first 1000 samples:
sentence1 label sentence2 type string int string details - min: 6 tokens
- mean: 11.12 tokens
- max: 22 tokens
- 0: ~48.28%
- 1: ~51.72%
- min: 4 tokens
- mean: 10.52 tokens
- max: 21 tokens
- Samples:
sentence1 label sentence2 How many bones are in the human body?
1
Total bones in an adult human
What is the price of an iPhone 12?
0
What is the price of an iPhone 11?
What are the different types of renewable energy?
1
What are the various forms of renewable energy?
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 32gradient_accumulation_steps
: 2num_train_epochs
: 4warmup_ratio
: 0.1load_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
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 2eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_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
: 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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap |
---|---|---|---|---|---|
0 | 0 | - | - | 0.6414 | - |
0.5 | 10 | 1.9407 | - | - | - |
1.0 | 20 | 0.9729 | 0.6810 | - | - |
1.475 | 30 | 0.4822 | - | - | - |
1.975 | 40 | 0.4062 | - | - | - |
2.025 | 41 | - | 0.5953 | - | - |
2.45 | 50 | 0.2894 | - | - | - |
2.95 | 60 | 0.1977 | - | - | - |
3.0 | 61 | - | 0.5318 | - | - |
3.425 | 70 | 0.1999 | - | - | - |
3.925 | 80 | 0.1491 | 0.5159 | - | 0.9467 |
- The bold row denotes the saved checkpoint.
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",
}
- Downloads last month
- 15
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for srikarvar/fine_tuned_model_4
Base model
intfloat/multilingual-e5-smallEvaluation results
- Cosine Accuracy on pair class devself-reported0.621
- Cosine Accuracy Threshold on pair class devself-reported0.904
- Cosine F1 on pair class devself-reported0.719
- Cosine F1 Threshold on pair class devself-reported0.904
- Cosine Precision on pair class devself-reported0.583
- Cosine Recall on pair class devself-reported0.939
- Cosine Ap on pair class devself-reported0.637
- Dot Accuracy on pair class devself-reported0.621
- Dot Accuracy Threshold on pair class devself-reported0.904
- Dot F1 on pair class devself-reported0.719