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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_7")
# Run inference
sentences = [
    'Top literature about World War II',
    'Best books on World War II',
    'What is the price of an iPhone 12?',
]
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	0.9
cosine_accuracy_threshold	0.7847
cosine_f1	0.9266
cosine_f1_threshold	0.7847
cosine_precision	0.8938
cosine_recall	0.9619
cosine_ap	0.9549
dot_accuracy	0.9
dot_accuracy_threshold	0.7847
dot_f1	0.9266
dot_f1_threshold	0.7847
dot_precision	0.8938
dot_recall	0.9619
dot_ap	0.9549
manhattan_accuracy	0.8969
manhattan_accuracy_threshold	9.909
manhattan_f1	0.9241
manhattan_f1_threshold	10.1367
manhattan_precision	0.8933
manhattan_recall	0.9571
manhattan_ap	0.955
euclidean_accuracy	0.9
euclidean_accuracy_threshold	0.6562
euclidean_f1	0.9266
euclidean_f1_threshold	0.6562
euclidean_precision	0.8938
euclidean_recall	0.9619
euclidean_ap	0.9549
max_accuracy	0.9
max_accuracy_threshold	9.909
max_f1	0.9266
max_f1_threshold	10.1367
max_precision	0.8938
max_recall	0.9619
max_ap	0.955

Binary Classification

Dataset: pair-class-test
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9062
cosine_accuracy_threshold	0.8142
cosine_f1	0.9292
cosine_f1_threshold	0.8142
cosine_precision	0.9206
cosine_recall	0.9381
cosine_ap	0.9556
dot_accuracy	0.9062
dot_accuracy_threshold	0.8142
dot_f1	0.9292
dot_f1_threshold	0.8142
dot_precision	0.9206
dot_recall	0.9381
dot_ap	0.9556
manhattan_accuracy	0.9031
manhattan_accuracy_threshold	9.5768
manhattan_f1	0.9271
manhattan_f1_threshold	9.5768
manhattan_precision	0.9163
manhattan_recall	0.9381
manhattan_ap	0.9558
euclidean_accuracy	0.9062
euclidean_accuracy_threshold	0.6095
euclidean_f1	0.9292
euclidean_f1_threshold	0.6095
euclidean_precision	0.9206
euclidean_recall	0.9381
euclidean_ap	0.9556
max_accuracy	0.9062
max_accuracy_threshold	9.5768
max_f1	0.9292
max_f1_threshold	9.5768
max_precision	0.9206
max_recall	0.9381
max_ap	0.9558

Training Details

Training Dataset

Unnamed Dataset

Size: 2,871 training 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: 20.57 tokens
max: 177 tokens
min: 6 tokens
mean: 20.74 tokens
max: 176 tokens
0: ~34.00%
1: ~66.00%

Samples:

sentence2	sentence1	label
`How do I do to get fuller face?`	`How can one get a fuller face?`	`1`
`The DatasetInfo holds the data of a dataset, which may include its description, characteristics, and size.`	`A dataset's information is stored inside DatasetInfo and can include information such as the dataset description, features, and dataset size.`	`1`
`How do I write a resume?`	`How do I create a resume?`	`1`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 320 evaluation samples
Columns: sentence2, sentence1, and label
Approximate statistics based on the first 320 samples:
sentence2 sentence1 label
type string string int
details
min: 4 tokens
mean: 19.57 tokens
max: 135 tokens
min: 6 tokens
mean: 19.55 tokens
max: 136 tokens
0: ~34.38%
1: ~65.62%

Samples:

sentence2	sentence1	label
`Steps to erase internet history`	`How do I delete my browsing history?`	`1`
`How important is it to be the first person to wish someone a happy birthday?`	`What is the right etiquette for wishing a Jehovah Witness happy birthday?`	`0`
`Who directed 'Gone with the Wind'?`	`Who directed 'Citizen Kane'?`	`0`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 2
num_train_epochs: 4
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: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
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: 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	-	-	0.8735	-
0.2222	10	1.3298	-	-	-
0.4444	20	0.8218	-	-	-
0.6667	30	0.642	-	-	-
0.8889	40	0.571	-	-	-
1.0	45	-	0.5321	0.9499	-
1.1111	50	0.4828	-	-	-
1.3333	60	0.3003	-	-	-
1.5556	70	0.3331	-	-	-
1.7778	80	0.203	-	-	-
2.0	90	0.3539	0.5118	0.9558	-
2.2222	100	0.1357	-	-	-
2.4444	110	0.1562	-	-	-
2.6667	120	0.0703	-	-	-
2.8889	130	0.0806	-	-	-
3.0	135	-	0.5266	0.9548	-
3.1111	140	0.1721	-	-	-
3.3333	150	0.1063	-	-	-
3.5556	160	0.0909	-	-	-
3.7778	170	0.0358	-	-	-
4.0	180	0.1021	0.5256	0.9550	0.9558

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.0
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.34.2
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

	sentence2	sentence1	label
type	string	string	int
details	min: 5 tokens mean: 20.57 tokens max: 177 tokens	min: 6 tokens mean: 20.74 tokens max: 176 tokens	0: ~34.00% 1: ~66.00%