This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
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': 256, 'do_lower_case': False, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The entire country of India receives more than 50% of its annual rainfall during the winter season',
    'prepared in the last week of December 2018. North India consisting of seven subdivisions (East U.P., West U.P., Uttaranchal, Haryana, Punjab, Himachal Pradesh, Jammu & Kashmir) receives about 17% of its annual rainfall during the winter season (January to March). The Jammu & Kashmir in particular receives about 30% of its annual rainfall during this period. The winter rainfall is very crucial for ',
    'take care of their crops and livestock. The information would be updated twice a week on every Tuesday and Friday. The app would provide information in the form of images, maps and pictures to help the farmers for decision making based on weather information and agromet advisories. Fig. 15. Megdoot Mobile App (ii) IMD in collaboration with WOTR, Pune is developing Decision Support System software ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.0534, -0.2588],
#         [ 0.0534,  1.0000,  0.1284],
#         [-0.2588,  0.1284,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 800 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 800 samples:
sentence_0 sentence_1 label
type string string float
details
min: 6 tokens
mean: 19.42 tokens
max: 51 tokens
min: 61 tokens
mean: 98.17 tokens
max: 252 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence_0	sentence_1	label
`The meeting on Disaster Preparedness resulted in a 20% reduction in climate-related disasters in the region`	`Centre (SRLDC), Bengaluru Familiarization of web portal exclusively created by IMD, New Delhi for Power System Operation Co- operation (POSOCO) use and interpretation of weather data on 12th July, 2017 Presentation on Disaster Preparedness Shri Bikram Singh, Sc. ‘E’ during the presentation Shri Bikram Singh, Sc. ‘E’ attended a meeting on 14th July, 2017 on Disaster Preparedness and gave a presenta`	`0.0`
`The installation of AWOS systems was funded by a private climate organization based in the United States`	`Govt. of India on 29.09.2024. On this occasion Shri Bikram Singh, Head/Scientist-F, Meteorological Center, Dehradun, Officials from MoES & senior officials of the Govt. of Uttarakhand were also present. H-AWOS installation at Kedarnath ji H-AWOS installation at Kedarnath ji H-AWOS installation at Kedarnath ji The three new Automatic Weather Observing Systems (AWOS) have been installed at Runway- 2`	`0.0`
`The centre has launched a mobile application for tracking climate changes`	`users. 3. Web based service has been started by the centre by creation of electronic versions of 14 language editions of Rashtriya Panchang which can be accessed by the users through the PAC Kolkata website. 4. A pocket-type, card-size calendar containing brief information on important celestial events during the year 2017 has been published in for benefit of users. 5. The centre has prepared mont`	`0.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
10.0	500	0.0628

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.3.0
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 6 tokens mean: 19.42 tokens max: 51 tokens	min: 61 tokens mean: 98.17 tokens max: 252 tokens	min: 0.0 mean: 0.5 max: 1.0

sooriyajs/imd-sbert-finetuned

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2