metadata

base_model: intfloat/multilingual-e5-small
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1030
  - loss:ContrastiveLoss
widget:
  - source_sentence: First climber to reach the summit of Everest
    sentences:
      - How to create a podcast?
      - How to cook sushi rice?
      - Who was the first person to climb Mount Everest?
  - source_sentence: What methods are used to measure a nation's GDP?
    sentences:
      - How is the GDP of a country measured?
      - How do I sign out of my email account?
      - How does digital marketing differ from traditional marketing?
  - source_sentence: Steps to sign up for a new account
    sentences:
      - How to grow tomatoes in a garden?
      - What is the process for creating a new account?
      - What is the GDP of India?
  - source_sentence: Name of the tallest building in New York
    sentences:
      - What are the symptoms of anxiety?
      - What is the tallest building in New York?
      - Who was the first female Prime Minister of the UK?
  - source_sentence: How do you make a paper boat?
    sentences:
      - What are the benefits of using solar energy?
      - Where can I buy a new phone?
      - How do you make a paper airplane?
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-small
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: pair class dev
          type: pair-class-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9478260869565217
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.6633322238922119
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9558823529411764
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.6633322238922119
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9154929577464789
            name: Cosine Precision
          - type: cosine_recall
            value: 1
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9777355464218691
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9478260869565217
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.6633322238922119
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9558823529411764
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.6633322238922119
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9154929577464789
            name: Dot Precision
          - type: dot_recall
            value: 1
            name: Dot Recall
          - type: dot_ap
            value: 0.9777355464218691
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9391304347826087
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 9.603110313415527
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9489051094890512
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 12.660685539245605
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9027777777777778
            name: Manhattan Precision
          - type: manhattan_recall
            value: 1
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.975614621691024
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9478260869565217
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.8205450773239136
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9558823529411764
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.8205450773239136
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9154929577464789
            name: Euclidean Precision
          - type: euclidean_recall
            value: 1
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9777355464218691
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9478260869565217
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 9.603110313415527
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9558823529411764
            name: Max F1
          - type: max_f1_threshold
            value: 12.660685539245605
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9154929577464789
            name: Max Precision
          - type: max_recall
            value: 1
            name: Max Recall
          - type: max_ap
            value: 0.9777355464218691
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: pair class test
          type: pair-class-test
        metrics:
          - type: cosine_accuracy
            value: 0.9478260869565217
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7873066663742065
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9558823529411764
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.6542514562606812
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9154929577464789
            name: Cosine Precision
          - type: cosine_recall
            value: 1
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9776721343444097
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9478260869565217
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.7873067259788513
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9558823529411764
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.6542515158653259
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9154929577464789
            name: Dot Precision
          - type: dot_recall
            value: 1
            name: Dot Recall
          - type: dot_ap
            value: 0.9776721343444097
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9478260869565217
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 11.123205184936523
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9558823529411764
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 12.862250328063965
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9154929577464789
            name: Manhattan Precision
          - type: manhattan_recall
            value: 1
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9774497925836063
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9478260869565217
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.652188777923584
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9558823529411764
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.8315430879592896
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9154929577464789
            name: Euclidean Precision
          - type: euclidean_recall
            value: 1
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9776721343444097
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9478260869565217
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 11.123205184936523
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9558823529411764
            name: Max F1
          - type: max_f1_threshold
            value: 12.862250328063965
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9154929577464789
            name: Max Precision
          - type: max_recall
            value: 1
            name: Max Recall
          - type: max_ap
            value: 0.9776721343444097
            name: Max Ap

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_2")
# Run inference
sentences = [
    'How do you make a paper boat?',
    'How do you make a paper airplane?',
    'What are the benefits of using solar energy?',
]
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	0.9478
cosine_accuracy_threshold	0.6633
cosine_f1	0.9559
cosine_f1_threshold	0.6633
cosine_precision	0.9155
cosine_recall	1.0
cosine_ap	0.9777
dot_accuracy	0.9478
dot_accuracy_threshold	0.6633
dot_f1	0.9559
dot_f1_threshold	0.6633
dot_precision	0.9155
dot_recall	1.0
dot_ap	0.9777
manhattan_accuracy	0.9391
manhattan_accuracy_threshold	9.6031
manhattan_f1	0.9489
manhattan_f1_threshold	12.6607
manhattan_precision	0.9028
manhattan_recall	1.0
manhattan_ap	0.9756
euclidean_accuracy	0.9478
euclidean_accuracy_threshold	0.8205
euclidean_f1	0.9559
euclidean_f1_threshold	0.8205
euclidean_precision	0.9155
euclidean_recall	1.0
euclidean_ap	0.9777
max_accuracy	0.9478
max_accuracy_threshold	9.6031
max_f1	0.9559
max_f1_threshold	12.6607
max_precision	0.9155
max_recall	1.0
max_ap	0.9777

Binary Classification

Dataset: pair-class-test
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9478
cosine_accuracy_threshold	0.7873
cosine_f1	0.9559
cosine_f1_threshold	0.6543
cosine_precision	0.9155
cosine_recall	1.0
cosine_ap	0.9777
dot_accuracy	0.9478
dot_accuracy_threshold	0.7873
dot_f1	0.9559
dot_f1_threshold	0.6543
dot_precision	0.9155
dot_recall	1.0
dot_ap	0.9777
manhattan_accuracy	0.9478
manhattan_accuracy_threshold	11.1232
manhattan_f1	0.9559
manhattan_f1_threshold	12.8623
manhattan_precision	0.9155
manhattan_recall	1.0
manhattan_ap	0.9774
euclidean_accuracy	0.9478
euclidean_accuracy_threshold	0.6522
euclidean_f1	0.9559
euclidean_f1_threshold	0.8315
euclidean_precision	0.9155
euclidean_recall	1.0
euclidean_ap	0.9777
max_accuracy	0.9478
max_accuracy_threshold	11.1232
max_f1	0.9559
max_f1_threshold	12.8623
max_precision	0.9155
max_recall	1.0
max_ap	0.9777

Training Details

Training Dataset

Unnamed Dataset

Size: 1,030 training samples
Columns: label, sentence2, and sentence1
Approximate statistics based on the first 1000 samples:
label sentence2 sentence1
type int string string
details
0: ~49.60%
1: ~50.40%

min: 4 tokens
mean: 10.27 tokens
max: 22 tokens

min: 6 tokens
mean: 10.9 tokens
max: 22 tokens

	label	sentence2	sentence1
type	int	string	string
details	0: ~49.60% 1: ~50.40%	min: 4 tokens mean: 10.27 tokens max: 22 tokens	min: 6 tokens mean: 10.9 tokens max: 22 tokens

Samples:

label	sentence2	sentence1
`1`	`Speed of sound in air`	`What is the speed of sound?`
`1`	`World's most popular tourist destination`	`What is the most visited tourist attraction in the world?`
`1`	`How do I write a resume?`	`How do I create a resume?`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.6,
    "size_average": true
}

Evaluation Dataset

Unnamed Dataset

Size: 115 evaluation samples
Columns: label, sentence2, and sentence1
Approximate statistics based on the first 1000 samples:
label sentence2 sentence1
type int string string
details
0: ~43.48%
1: ~56.52%

min: 5 tokens
mean: 10.04 tokens
max: 15 tokens

min: 6 tokens
mean: 10.81 tokens
max: 20 tokens

	label	sentence2	sentence1
type	int	string	string
details	0: ~43.48% 1: ~56.52%	min: 5 tokens mean: 10.04 tokens max: 15 tokens	min: 6 tokens mean: 10.81 tokens max: 20 tokens

Samples:

label	sentence2	sentence1
`0`	`What methods are used to measure a nation's GDP?`	`How is the GDP of a country measured?`
`0`	`What is the currency of Japan?`	`What is the currency of China?`
`1`	`Steps to cultivate tomatoes at home`	`How to grow tomatoes in a garden?`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.6,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 2
weight_decay: 0.01
num_train_epochs: 8
lr_scheduler_type: reduce_lr_on_plateau
warmup_ratio: 0.1
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
learning_rate: 5e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 8
max_steps: -1
lr_scheduler_type: reduce_lr_on_plateau
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
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_fused
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
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	pair-class-dev_max_ap	pair-class-test_max_ap
0	0	-	-	0.7625	-
0.6061	10	0.0417	-	-	-
0.9697	16	-	0.0119	0.9695	-
1.2121	20	0.0189	-	-	-
1.8182	30	0.0148	-	-	-
2.0	33	-	0.0102	0.9741	-
2.4242	40	0.0114	-	-	-
2.9697	49	-	0.0098	0.9752	-
3.0303	50	0.009	-	-	-
3.6364	60	0.008	-	-	-
4.0	66	-	0.0095	0.9778	-
4.2424	70	0.0065	-	-	-
4.8485	80	0.0056	-	-	-
4.9697	82	-	0.0092	0.9749	-
5.4545	90	0.0056	-	-	-
6.0	99	-	0.0088	0.9766	-
6.0606	100	0.0045	-	-	-
6.6667	110	0.0044	-	-	-
6.9697	115	-	0.0087	0.9777	-
7.2727	120	0.0038	-	-	-
7.7576	128	-	0.0090	0.9777	0.9777

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}