This is a sentence-transformers model finetuned from l3cube-pune/indic-sentence-similarity-sbert. It maps sentences & paragraphs to a 768-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: l3cube-pune/indic-sentence-similarity-sbert
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Language: en

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': 768, '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})
)

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("ammumadhu/Indic_Bert-8-layers")
# Run inference
sentences = [
    'Men are outdoors.',
    'A man is outside.',
    'A Little girl is enjoying cake outside.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6061
spearman_cosine	0.6316
pearson_manhattan	0.4868
spearman_manhattan	0.5132
pearson_euclidean	0.506
spearman_euclidean	0.5306
pearson_dot	0.2198
spearman_dot	0.2098
pearson_max	0.6061
spearman_max	0.6316

Knowledge Distillation

Evaluated with MSEEvaluator

Metric	Value
negative_mse	-3.0273

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7909
spearman_cosine	0.7965
pearson_manhattan	0.776
spearman_manhattan	0.773
pearson_euclidean	0.7764
spearman_euclidean	0.7736
pearson_dot	0.6959
spearman_dot	0.6843
pearson_max	0.7909
spearman_max	0.7965

Training Details

Training Dataset

Unnamed Dataset

Size: 1,147,385 training samples
Columns: sentence and label
Approximate statistics based on the first 1000 samples:
sentence label
type string list
details
min: 4 tokens
mean: 12.59 tokens
max: 52 tokens
size: 768 elements

Samples:

sentence	label
`A person on a horse jumps over a broken down airplane.`	`[-0.0009042086312547326, 0.02319158799946308, 0.016657305881381035, -0.004571350757032633, -0.008184989914298058, ...]`
`Children smiling and waving at camera`	`[-0.020024249330163002, -0.0005705401999875903, 0.025419672951102257, -0.014105383306741714, 0.009407470934092999, ...]`
`A boy is jumping on skateboard in the middle of a red bridge.`	`[-0.01713346689939499, -2.3264645278686658e-05, -0.0005397812929004431, 0.002506087301298976, 0.027286207303404808, ...]`

Loss: MSELoss

Evaluation Dataset

sentence-transformers/wikipedia-en-sentences

Dataset: sentence-transformers/wikipedia-en-sentences at 4a0972d
Size: 10,000 evaluation samples
Columns: sentence and label
Approximate statistics based on the first 1000 samples:
sentence label
type string list
details
min: 5 tokens
mean: 13.53 tokens
max: 61 tokens
size: 768 elements

Samples:

sentence	label
`Two women are embracing while holding to go packages.`	`[-0.000599742284975946, 0.0042074089869856834, 0.0013686479069292545, -0.0009170330595225096, -0.010106148198246956, ...]`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`[0.003711540251970291, -0.005768307950347662, -0.03475787863135338, 0.010626137256622314, -0.0023863380774855614, ...]`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`[-0.014246350154280663, 0.015385480597615242, 0.0016394935082644224, -0.013386472128331661, -0.015061145648360252, ...]`

Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 0.0001
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
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: True
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	negative_mse	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	-3.0273	0.6316	-
0.2231	1000	0.0015	-	-	-
0.4462	2000	0.0001	-	-	-
0.6693	3000	0.0001	-	-	-
0.8925	4000	0.0001	-	-	-
1.0	4482	-	-	-	0.7965

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.1.0
Accelerate: 0.30.1
Datasets: 2.19.2
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",
}

MSELoss

bibtex

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

	sentence	label
type	string	list
details	min: 4 tokens mean: 12.59 tokens max: 52 tokens	size: 768 elements