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nomic-embed-text-v1.5

Name: nomic-embed-text-finetuned
Availability: InStock

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5 on the json dataset. 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: nomic-ai/nomic-embed-text-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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("Thejina/nomic-embed-text-finetuned")
# Run inference
sentences = [
    'such an argument, and she does not offer any case law, cites to secondary sources, dictionaries \nor grammatical texts, arguments by analogy, or other citations, except for the mere assertion \nthat defendant failed to move in a timely fashion after he was “on notice” of the ex parte order. \nA reviewing court is entitled to have issues clearly defined with relevant authority cited.',
    'What mere assertion does she make?',
    "What page is Cross-MJAR's emphasis mentioned on?",
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy@1	0.5487
cosine_accuracy@3	0.5966
cosine_accuracy@5	0.7017
cosine_accuracy@10	0.7697
cosine_precision@1	0.5487
cosine_precision@3	0.524
cosine_precision@5	0.4099
cosine_precision@10	0.2414
cosine_recall@1	0.1905
cosine_recall@3	0.5102
cosine_recall@5	0.6503
cosine_recall@10	0.7595
cosine_ndcg@10	0.6615
cosine_mrr@10	0.6004
cosine_map@100	0.6428

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy@1	0.541
cosine_accuracy@3	0.5889
cosine_accuracy@5	0.6924
cosine_accuracy@10	0.7743
cosine_precision@1	0.541
cosine_precision@3	0.5173
cosine_precision@5	0.4034
cosine_precision@10	0.2419
cosine_recall@1	0.1874
cosine_recall@3	0.5054
cosine_recall@5	0.6412
cosine_recall@10	0.7622
cosine_ndcg@10	0.6576
cosine_mrr@10	0.5934
cosine_map@100	0.6355

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy@1	0.5085
cosine_accuracy@3	0.5564
cosine_accuracy@5	0.6708
cosine_accuracy@10	0.745
cosine_precision@1	0.5085
cosine_precision@3	0.4874
cosine_precision@5	0.3864
cosine_precision@10	0.2312
cosine_recall@1	0.1767
cosine_recall@3	0.4771
cosine_recall@5	0.6141
cosine_recall@10	0.7258
cosine_ndcg@10	0.6258
cosine_mrr@10	0.563
cosine_map@100	0.6092

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	Value
cosine_accuracy@1	0.4513
cosine_accuracy@3	0.5054
cosine_accuracy@5	0.5889
cosine_accuracy@10	0.6862
cosine_precision@1	0.4513
cosine_precision@3	0.4374
cosine_precision@5	0.3416
cosine_precision@10	0.213
cosine_recall@1	0.157
cosine_recall@3	0.4283
cosine_recall@5	0.5426
cosine_recall@10	0.6721
cosine_ndcg@10	0.568
cosine_mrr@10	0.5039
cosine_map@100	0.5512

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	Value
cosine_accuracy@1	0.3524
cosine_accuracy@3	0.3895
cosine_accuracy@5	0.473
cosine_accuracy@10	0.5641
cosine_precision@1	0.3524
cosine_precision@3	0.339
cosine_precision@5	0.2696
cosine_precision@10	0.1723
cosine_recall@1	0.1217
cosine_recall@3	0.3322
cosine_recall@5	0.4311
cosine_recall@10	0.5447
cosine_ndcg@10	0.452
cosine_mrr@10	0.3966
cosine_map@100	0.4461

Training Details

Training Dataset

json

Dataset: json
Size: 5,822 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 46 tokens
mean: 91.09 tokens
max: 324 tokens
min: 7 tokens
mean: 16.89 tokens
max: 43 tokens

Samples:

positive	anchor
`functional test, too. Id. at 89–90. Still, the Court made clear that this functional test was “not [object Object]relevant.” Id. at 90. So, just as in Energy Research, its application of the functional test was [object Object]dicta. And because this discussion relied on the dicta from Energy Research, this was dicta [object Object]upon dicta. [object Object] [object Object] The Government is thus imprecise when it asserts as the “law of the case” that the`	`What page is the functional test mentioned as 'not relevant'?`
`authenticated through his testimony under Maryland Rule 5-901(b)(1) as a witness with [object Object]personal knowledge of the events. [object Object]- 6 - [object Object]The part of the video depicting the shooting was properly authenticated through [object Object]circumstantial evidence under Maryland Rule 5-901(b)(4), as there was sufficient [object Object]circumstantial evidence from which a reasonable juror could have inferred that the video`	`Which part of the video was authenticated?`
`KLAN202300916 [object Object] [object Object] [object Object] [object Object] [object Object]9[object Object]Los derechos morales, a su vez, están fundamentalmente [object Object]protegidos por la legislación estatal. Esta reconoce los derechos de [object Object]los autores como exclusivos de estos y los protege no solo en [object Object]beneficio propio, sino también de la sociedad por la contribución [object Object]social y cultural que históricamente se le ha reconocido a la`	`¿En beneficio de quién se protegen los derechos de los autores?`

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: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: cosine
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: True
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}
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_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: 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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.8791	10	69.7578	-	-	-	-	-
1.0	12	-	0.6178	0.6069	0.5742	0.5088	0.4115
1.7033	20	28.4334	-	-	-	-	-
2.0	24	-	0.6589	0.6509	0.6268	0.5616	0.4494
2.5275	30	20.1123	-	-	-	-	-
3.0	36	-	0.6621	0.6573	0.6263	0.5677	0.4508
3.3516	40	16.5444	-	-	-	-	-
3.7033	44	-	0.6615	0.6576	0.6258	0.568	0.452

The bold row denotes the saved checkpoint.

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: 3.6.0
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",
}

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}
}

	positive	anchor
type	string	string
details	min: 46 tokens mean: 91.09 tokens max: 324 tokens	min: 7 tokens mean: 16.89 tokens max: 43 tokens