You can deploy this model and start earning money today!

ashwinpatti/
finetuned_arctic_naive_ft-legal-ft-v0

Name: finetuned_arctic_naive_ft-legal-ft-v0
Availability: InStock

Views

No views yet

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-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: Snowflake/snowflake-arctic-embed-l
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("ashwinpatti/finetuned_arctic_naive_ft-legal-ft-v0")
# Run inference
sentences = [
    'How has the approach to run chases in the IPL changed from 2019 to 2024?',
    'Three slips and a gullySubscribeSign inShare this postThree slips and a gullyWhat makes a successful run chase in the IPLCopy linkFacebookEmailNotesMoreWhat makes a successful run chase in the IPLA look at the way teams have been chasing targets in the IPL since 2019, and how 2024 was just a tad bit different in the way teams approach run chases.Divyansh PeswaniJan 09, 20254Share this postThree slips and a gullyWhat makes a successful run chase in the IPLCopy linkFacebookEmailNotesMore1ShareT20 batting has two sides to it; the calculations of putting up a first-innings total that could be considered above par for the given conditions, and the complexities of structuring the second innings chase across the innings to bag a win safely',
    'batters by bowling line-length combinations they’re the most conservative against.Thanks for reading Three slips and a gully! This post is public so feel free to share it.ShareSuryakumar Yadav is an absolute beast in T20 cricket. Although in a lean patch right now, he is potentially the only cricketer that will go down as an all-time great because of his brilliance in only one format, the 20 over game. He, like most Indian batters, struggles a bit against SLA, but still fares better than most of his contemporaries. He’s conservative against the straight-on SLAOs, bowled at the stumps from a good length. As the bowler drifts his line away from the stumps, he finds himself to have more room, and his striking ability improves as the ball gets',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6786
cosine_accuracy@3	0.8571
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6786
cosine_precision@3	0.2857
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6786
cosine_recall@3	0.8571
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8465
cosine_mrr@10	0.7958
cosine_map@100	0.7958

Training Details

Training Dataset

Unnamed Dataset

Size: 56 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 56 samples:
sentence_0 sentence_1
type string string
details
min: 12 tokens
mean: 22.36 tokens
max: 40 tokens
min: 12 tokens
mean: 149.39 tokens
max: 187 tokens

Samples:

sentence_0	sentence_1
`What do the lines and lengths represent in the context provided?`	`The lines and lengths are trying to tell us something`
`How might the lines and lengths convey a message or meaning?`	`The lines and lengths are trying to tell us something`
`What is the main focus of the analysis regarding line-length combinations used against different batters?`	Three slips and a gullySubscribeSign inShare this postThree slips and a gullyThe lines and lengths are trying to tell us somethingCopy linkFacebookEmailNotesMoreThe lines and lengths are trying to tell us somethingTaking a closer at line-length combinations used against different batters to see if there's more than what meets the eyeDivyansh PeswaniFeb 02, 202510Share this postThree slips and a gullyThe lines and lengths are trying to tell us somethingCopy linkFacebookEmailNotesMore2ShareMatchups across all forms of cricket are predominant. They take different forms, and are incorporated within gameday strategy differently, but the thought process behind a bowling line-up is to bowl deliveries least suitable to a batter’s playing style.

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
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: 10
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}
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
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
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	cosine_ndcg@10
1.0	6	0.7848
2.0	12	0.8365
3.0	18	0.8539
4.0	24	0.8539
5.0	30	0.8680
6.0	36	0.8655
7.0	42	0.8727
8.0	48	0.8727
8.3333	50	0.8727
9.0	54	0.8727
10.0	60	0.8727
1.0	6	0.8738
2.0	12	0.8550
3.0	18	0.8550
4.0	24	0.8440
5.0	30	0.8465
6.0	36	0.8465
7.0	42	0.8465
8.0	48	0.8465
8.3333	50	0.8465
9.0	54	0.8465
10.0	60	0.8465

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.1
Tokenizers: 0.21.0

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 22.36 tokens max: 40 tokens	min: 12 tokens mean: 149.39 tokens max: 187 tokens