metadata

base_model: intfloat/multilingual-e5-small
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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:2000
  - loss:OnlineContrastiveLoss
widget:
  - source_sentence: How do I sign up for a new account?
    sentences:
      - How do I book a flight online?
      - Can I withdraw money from my bank?
      - What is the process for creating a new account?
  - source_sentence: How can I enhance my English skills?
    sentences:
      - What are the ingredients of a pizza?
      - How can I improve my English?
      - What are the ingredients of a pizza?
  - source_sentence: Where can I buy a new bicycle?
    sentences:
      - What is the importance of a balanced diet?
      - How do I update my address?
      - Where can I buy a new laptop?
  - source_sentence: What steps do I need to follow to log into the company's internal network?
    sentences:
      - Who wrote the book "To Kill a Mockingbird"?
      - How do I reset my password?
      - How do I access the company's intranet?
  - source_sentence: How can I improve my Spanish?
    sentences:
      - How can I lose weight?
      - How can I improve my English?
      - What is the most effective way to lose weight?
model-index:
  - name: e5 cogcache small
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: base
          type: base
        metrics:
          - type: cosine_accuracy
            value: 0.8923076923076924
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8427294492721558
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9166666666666666
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8427294492721558
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9166666666666666
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9166666666666666
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9540451716910969
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.8923076923076924
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.842729389667511
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9166666666666666
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.842729389667511
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9166666666666666
            name: Dot Precision
          - type: dot_recall
            value: 0.9166666666666666
            name: Dot Recall
          - type: dot_ap
            value: 0.9540451716910969
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.8846153846153846
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 10.00046157836914
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9142857142857143
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 10.00046157836914
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.8791208791208791
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9523809523809523
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9533842444122883
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.8923076923076924
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.5608394742012024
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9166666666666666
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.5608394742012024
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9166666666666666
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9166666666666666
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9540451716910969
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.8923076923076924
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 10.00046157836914
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9166666666666666
            name: Max F1
          - type: max_f1_threshold
            value: 10.00046157836914
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9166666666666666
            name: Max Precision
          - type: max_recall
            value: 0.9523809523809523
            name: Max Recall
          - type: max_ap
            value: 0.9540451716910969
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: tuned
          type: tuned
        metrics:
          - type: cosine_accuracy
            value: 0.8923076923076924
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8427294492721558
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9166666666666666
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8427294492721558
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9166666666666666
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9166666666666666
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9540451716910969
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.8923076923076924
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.842729389667511
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9166666666666666
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.842729389667511
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9166666666666666
            name: Dot Precision
          - type: dot_recall
            value: 0.9166666666666666
            name: Dot Recall
          - type: dot_ap
            value: 0.9540451716910969
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.8846153846153846
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 10.00046157836914
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9142857142857143
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 10.00046157836914
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.8791208791208791
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9523809523809523
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9533842444122883
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.8923076923076924
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.5608394742012024
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9166666666666666
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.5608394742012024
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9166666666666666
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9166666666666666
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9540451716910969
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.8923076923076924
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 10.00046157836914
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9166666666666666
            name: Max F1
          - type: max_f1_threshold
            value: 10.00046157836914
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9166666666666666
            name: Max Precision
          - type: max_recall
            value: 0.9523809523809523
            name: Max Recall
          - type: max_ap
            value: 0.9540451716910969
            name: Max Ap

e5 cogcache 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
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/e5-small-cogcachedata")
# Run inference
sentences = [
    'How can I improve my Spanish?',
    'How can I improve my English?',
    'How can I lose weight?',
]
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: base
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.8923
cosine_accuracy_threshold	0.8427
cosine_f1	0.9167
cosine_f1_threshold	0.8427
cosine_precision	0.9167
cosine_recall	0.9167
cosine_ap	0.954
dot_accuracy	0.8923
dot_accuracy_threshold	0.8427
dot_f1	0.9167
dot_f1_threshold	0.8427
dot_precision	0.9167
dot_recall	0.9167
dot_ap	0.954
manhattan_accuracy	0.8846
manhattan_accuracy_threshold	10.0005
manhattan_f1	0.9143
manhattan_f1_threshold	10.0005
manhattan_precision	0.8791
manhattan_recall	0.9524
manhattan_ap	0.9534
euclidean_accuracy	0.8923
euclidean_accuracy_threshold	0.5608
euclidean_f1	0.9167
euclidean_f1_threshold	0.5608
euclidean_precision	0.9167
euclidean_recall	0.9167
euclidean_ap	0.954
max_accuracy	0.8923
max_accuracy_threshold	10.0005
max_f1	0.9167
max_f1_threshold	10.0005
max_precision	0.9167
max_recall	0.9524
max_ap	0.954

Binary Classification

Dataset: tuned
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.8923
cosine_accuracy_threshold	0.8427
cosine_f1	0.9167
cosine_f1_threshold	0.8427
cosine_precision	0.9167
cosine_recall	0.9167
cosine_ap	0.954
dot_accuracy	0.8923
dot_accuracy_threshold	0.8427
dot_f1	0.9167
dot_f1_threshold	0.8427
dot_precision	0.9167
dot_recall	0.9167
dot_ap	0.954
manhattan_accuracy	0.8846
manhattan_accuracy_threshold	10.0005
manhattan_f1	0.9143
manhattan_f1_threshold	10.0005
manhattan_precision	0.8791
manhattan_recall	0.9524
manhattan_ap	0.9534
euclidean_accuracy	0.8923
euclidean_accuracy_threshold	0.5608
euclidean_f1	0.9167
euclidean_f1_threshold	0.5608
euclidean_precision	0.9167
euclidean_recall	0.9167
euclidean_ap	0.954
max_accuracy	0.8923
max_accuracy_threshold	10.0005
max_f1	0.9167
max_f1_threshold	10.0005
max_precision	0.9167
max_recall	0.9524
max_ap	0.954

Training Details

Training Dataset

Unnamed Dataset

Size: 2,000 training samples
Columns: sentence2, sentence1, and label
Approximate statistics based on the first 1000 samples:
sentence2 sentence1 label
type string string int
details
min: 4 tokens
mean: 13.29 tokens
max: 55 tokens

min: 6 tokens
mean: 13.24 tokens
max: 66 tokens

0: ~55.10%
1: ~44.90%

	sentence2	sentence1	label
type	string	string	int
details	min: 4 tokens mean: 13.29 tokens max: 55 tokens	min: 6 tokens mean: 13.24 tokens max: 66 tokens	0: ~55.10% 1: ~44.90%

Samples:

sentence2	sentence1	label
`What are the ingredients of a pizza`	`What are the ingredients of a pizza?`	`1`
`What are the ingredients of pizza`	`What are the ingredients of a pizza?`	`1`
`What are ingredients of pizza`	`What are the ingredients of a pizza?`	`1`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 130 evaluation samples
Columns: sentence2, sentence1, and label
Approximate statistics based on the first 1000 samples:
sentence2 sentence1 label
type string string int
details
min: 5 tokens
mean: 11.48 tokens
max: 22 tokens

min: 6 tokens
mean: 10.85 tokens
max: 20 tokens

0: ~35.38%
1: ~64.62%

	sentence2	sentence1	label
type	string	string	int
details	min: 5 tokens mean: 11.48 tokens max: 22 tokens	min: 6 tokens mean: 10.85 tokens max: 20 tokens	0: ~35.38% 1: ~64.62%

Samples:

sentence2	sentence1	label
`What are the ingredients of a pizza`	`What are the ingredients of a pizza?`	`1`
`What are the ingredients of pizza`	`What are the ingredients of a pizza?`	`1`
`What are ingredients of pizza`	`What are the ingredients of a pizza?`	`1`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 6
warmup_ratio: 0.1
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: 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
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 6
max_steps: -1
lr_scheduler_type: linear
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: 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
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	base_max_ap	tuned_max_ap
0	0	-	-	0.7430	-
1.0	125	-	0.5464	0.7914	-
2.0	250	-	0.2451	0.9018	-
3.0	375	-	0.1717	0.9460	-
4.0	500	0.24	0.1490	0.9532	-
5.0	625	-	0.1598	0.9523	-
6.0	750	-	0.1382	0.9540	0.9540

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",
}