Add new SentenceTransformer model

22d3177 verified 3 months ago

26.7 kB

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:21470
  - loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-small
widget:
  - source_sentence: >-
      This positive resistance model is a different way of analyzing feedback
      oscillator operation.
    sentences:
      - >-
        This positive resistance model is a different way of analyzing feedback
        oscillator operation.
      - >-
        This negative resistance model is an alternate way of analyzing feedback
        oscillator operation.
      - >-
        I am BE 8th sem. CSE student. Which path should I choose as a career or
        which course I should do to get a good job in future within my country?
  - source_sentence: >-
      Danny Danny Kortchmar played guitar , Charles Larkey played bass and
      Gordon played drums producing with Lou Adler .
    sentences:
      - What is the main reason for all the problems within India?
      - >-
        Gordon played guitar , Danny Kortchmar played bass and Lou Adler played
        drums with Charles Larkey producing .
      - >-
        Danny Danny Kortchmar played guitar , Charles Larkey played bass and
        Gordon played drums producing with Lou Adler .
  - source_sentence: The Ngage isn't still lacking in earbuds.
    sentences:
      - >-
        What is Queen's University's acceptance rate for international students
        on campus?
      - The Ngage is still lacking in earbuds.
      - The Ngage isn't still lacking in earbuds.
  - source_sentence: Previously reported figures were consistently revised down.
    sentences:
      - Previously reported figures were consistently revised down.
      - >-
        What are the side effects for using Proactiv on the face? How are the
        side effects treated?
      - Previously reported numbers were infrequently revised down.
  - source_sentence: What is the fastest way to get a PAN card within India?
    sentences:
      - >-
        He has also used the OpenMusic software (designed at IRCAM ) to create
        computer-generated music.
      - What is the fastest way to get a PAN card outside India?
      - What is the fastest way to get a PAN card within India?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on thenlper/gte-small
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.48
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.52
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.58
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15999999999999998
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10400000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.057999999999999996
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.28
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.48
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.52
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.58
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4281391945817123
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3795238095238095
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.39018847344323304
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.32
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.66
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.132
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07400000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.55
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.61
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5108521344166539
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4791904761904762
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.452598225251627
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.30000000000000004
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.54
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5900000000000001
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6599999999999999
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.30000000000000004
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11800000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.066
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.29000000000000004
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.515
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.565
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.63
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4694956644991831
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4293571428571429
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.42139334934743
            name: Cosine Map@100

SentenceTransformer based on thenlper/gte-small

This is a sentence-transformers model finetuned from thenlper/gte-small. 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: thenlper/gte-small
Maximum Sequence Length: 128 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': 128, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("redis/unified-negatives")
# Run inference
sentences = [
    'What is the fastest way to get a PAN card within India?',
    'What is the fastest way to get a PAN card within India?',
    'What is the fastest way to get a PAN card outside India?',
]
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, 1.0000, 0.2943],
#         [1.0000, 1.0000, 0.2943],
#         [0.2943, 0.2943, 1.0000]])

Evaluation

Metrics

Information Retrieval

Datasets: NanoMSMARCO and NanoNQ
Evaluated with InformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNQ
cosine_accuracy@1	0.28	0.32
cosine_accuracy@3	0.48	0.6
cosine_accuracy@5	0.52	0.66
cosine_accuracy@10	0.58	0.74
cosine_precision@1	0.28	0.32
cosine_precision@3	0.16	0.2
cosine_precision@5	0.104	0.132
cosine_precision@10	0.058	0.074
cosine_recall@1	0.28	0.3
cosine_recall@3	0.48	0.55
cosine_recall@5	0.52	0.61
cosine_recall@10	0.58	0.68
cosine_ndcg@10	0.4281	0.5109
cosine_mrr@10	0.3795	0.4792
cosine_map@100	0.3902	0.4526

Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with NanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nq"
    ],
    "dataset_id": "lightonai/NanoBEIR-en"
}

Metric	Value
cosine_accuracy@1	0.3
cosine_accuracy@3	0.54
cosine_accuracy@5	0.59
cosine_accuracy@10	0.66
cosine_precision@1	0.3
cosine_precision@3	0.18
cosine_precision@5	0.118
cosine_precision@10	0.066
cosine_recall@1	0.29
cosine_recall@3	0.515
cosine_recall@5	0.565
cosine_recall@10	0.63
cosine_ndcg@10	0.4695
cosine_mrr@10	0.4294
cosine_map@100	0.4214

Training Details

Training Dataset

Unnamed Dataset

Size: 21,470 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 5 tokens mean: 19.91 tokens max: 101 tokens	min: 5 tokens mean: 19.91 tokens max: 101 tokens	min: 4 tokens mean: 19.91 tokens max: 101 tokens

Samples:

anchor	positive	negative
`The pale coloration provides camouflage for the beetle on the light sand.`	`The pale coloration provides camouflage for the beetle on the light sand.`	`The pale coloration helps the beetle stand out on the light sand.`
`It is found from Fennoscandinavia to the Pyrenees , Italy and Greece and from Britain to Russia and Ukraine .`	`It is found from Fennoscandinavia to the Pyrenees , Italy and Greece and from Britain to Russia and Ukraine .`	`It is located from Fennoscandinavia to the Pyrenees , Great Britain and Greece and from Italy to Russia and Ukraine .`
`Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated in Chicago?`	`Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated in Chicago?`	`Is Swami Vivekananda's speech at parliament of world's religions, Chicago overrated outside Chicago?`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 7.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Evaluation Dataset

Unnamed Dataset

Size: 2,386 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 19.42 tokens max: 74 tokens	min: 6 tokens mean: 19.42 tokens max: 74 tokens	min: 6 tokens mean: 19.41 tokens max: 74 tokens

Samples:

anchor	positive	negative
`He died at Fort Edward on August 18 , 1861 , and was buried at the Union Cemetery in Sandy Hill .`	`He died at Fort Edward on August 18 , 1861 , and was buried at the Union Cemetery in Sandy Hill .`	`He died at Sandy Hill on August 18 , 1861 , and was buried at the Union Cemetery in Fort Edward .`
`It was this cooperation which led to the development of the satellite AIS system.`	`It was this cooperation which led to the development of the satellite AIS system.`	`It was this cooperation which led to the halting of development of the satellite AIS system.`
`What is the best field of engineering on campus?`	`What is the best field of engineering on campus?`	`What is the best field of engineering off campus?`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 7.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 1e-06
weight_decay: 0.001
max_steps: 3000
warmup_ratio: 0.1
fp16: True
dataloader_drop_last: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 1
load_best_model_at_end: True
optim: adamw_torch
ddp_find_unused_parameters: False
push_to_hub: True
hub_model_id: redis/unified-negatives
eval_on_start: 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: 128
per_device_eval_batch_size: 128
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: 1e-06
weight_decay: 0.001
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: 3000
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
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: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 1
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: False
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: True
resume_from_checkpoint: None
hub_model_id: redis/unified-negatives
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: True
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0	0	-	3.6734	0.6259	0.6583	0.6421
1.4970	250	3.8677	3.3900	0.6334	0.6510	0.6422
2.9940	500	3.188	1.8654	0.5772	0.6252	0.6012
4.4910	750	1.4714	0.6890	0.4032	0.5437	0.4735
5.9880	1000	0.8535	0.5511	0.3617	0.5197	0.4407
7.4850	1250	0.7547	0.5268	0.3469	0.5346	0.4407
8.9820	1500	0.716	0.5123	0.3684	0.5223	0.4454
10.4790	1750	0.6939	0.5039	0.3846	0.5179	0.4512
11.9760	2000	0.6789	0.4986	0.4120	0.5280	0.4700
13.4731	2250	0.6681	0.4953	0.4148	0.5189	0.4669
14.9701	2500	0.662	0.4918	0.4224	0.5109	0.4666
16.4671	2750	0.6575	0.4905	0.4224	0.5109	0.4666
17.9641	3000	0.6555	0.4900	0.4281	0.5109	0.4695

Framework Versions

Python: 3.10.18
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 2.21.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@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",
}

MultipleNegativesRankingLoss

@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}
}