This is a sentence-transformers model trained. 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

Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 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("rossieRuby/nyayadrishti-bert-v2")
# Run inference
sentences = [
    'What entry is mentioned in relation to tax on land?',
    'In that connection, the following observations lay down the guide lines: "It is well settled that the entries in the three legislative lists have to be interpreted in their widest amplitude and there fore if a tax can reasonably be held to be a tax on land it will come within entry 49. Further it is equally well settled that tax on land may be based on the annual value of the land and would still be a tax on land and would not be beyond the com petence of the State legislature on the ground that it is a tax on income: (See Ralla Ram vs The province of East Punjab: It follows therefore that the use to which the land is put can be taken into account in imposing a tax (1) ; (2)Quoted in Liberty cinema: P. 484. 339 on it within the meaning of entry 49 of List II, for the annual value of land which can certainly be taken into account in imposing a tax for the purpose of this entry would necessarily depend upon the use to which the land is put." (p. 49).',
    'Now it is true that the so called will of 1864 does not make provision for the grandsons, nor does it expressly confer an absolute estate on the legatees, but the witness is illiterate and had to depend on what he was told about the contents and meaning of the document, and what we have to test is the truth of his assertion that the plaintiff Mukand Ram and Kanhaiya Lal, and other members of the family, told him that Mst. Pato had given the property to her daughters and grandsons. If they told him this, as he says they did, then it operates as an admission against Mukand Ram and shifts the burden of proof to him because he was one of the persons who made the statement. The statements made by the others are not relevant except in so far as they prove the conduct of the family. The plaintiff (P.W. 11 in C.A. 91/50) admits that Mst. Pato divided the estate but says that it was only for convenience of management and that neither she nor her daughters had, or pretended to have, anything more than a life estate. He denies that there was any gift or family arrangement. But he had to admit that the grandsons also got properties at the same time. His explanation is that it was for the purposes of "shradh" and pilgrimage to Gaya and he says that though they were given possession they were not the "owners".',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 29,992 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 7 tokens
mean: 15.09 tokens
max: 45 tokens
min: 10 tokens
mean: 153.59 tokens
max: 512 tokens
min: 0.0
mean: 0.48
max: 1.0

Samples:

sentence_0	sentence_1	label
`Why did the respondents dispute the demurrage charges?`	`The respondents disputed the right of the Port Trust to charge any demurrage for the period during which the goods were detained by the Customs authorities for analytical test. as well as for the Import Trade Control formalities.`	`1.0`
`What was the paid up capital of the subsidiary Company?`	`The paid up capital of the subsidiary Company was Rs. 7,91,100 divided into 7,911 shares of Rs. 100 each.`	`1.0`
`How many parts does the schedule attached to the Act have?`	`The schedule attached to the Act specifies, under two parts, the employments in respect of which the minimum wages of the employees can be fixed; and section 27 authorises the "appropriate Government", after giving three months ' notice of its intention to do so, to add to either part of the schedule, any other employment, in respect of which it is of the opinion that minimum rates of wages should be fixed under the Act.`	`1.0`

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: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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
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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.2667	500	0.2532
0.5333	1000	0.2527
0.8	1500	0.2524

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 2.14.4
Tokenizers: 0.21.1

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 7 tokens mean: 15.09 tokens max: 45 tokens	min: 10 tokens mean: 153.59 tokens max: 512 tokens	min: 0.0 mean: 0.48 max: 1.0