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 tokens
Similarity Function: Cosine Similarity

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': 256, 'do_lower_case': False}) with Transformer model: 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("rjnClarke/sentence-transformers-all-MiniLM-L6-v2-fine-tuned")
# Run inference
sentences = [
    'What is the significance of the tennis balls in the excerpt from the play?',
    "Says that you savour too much of your youth,\n    And bids you be advis'd there's nought in France    That can be with a nimble galliard won;    You cannot revel into dukedoms there.    He therefore sends you, meeter for your spirit,    This tun of treasure; and, in lieu of this,    Desires you let the dukedoms that you claim    Hear no more of you. This the Dauphin speaks.  KING HENRY. What treasure, uncle?  EXETER. Tennis-balls, my liege.  KING HENRY. We are glad the Dauphin is so pleasant with us;    His present and your pains we thank you for.    When we have match'd our rackets to these balls,      We will in France, by God's grace, play a set    Shall strike his father's crown into the hazard.    Tell him he hath made a match with such a wrangler    That all the courts of France will be disturb'd    With chaces. And we understand him well,    How he comes o'er us with our wilder days,    Not measuring what use we made of them.    We never valu'd this poor seat of England;    And therefore, living hence, did give ourself    To barbarous licence; as 'tis ever common    That men are merriest when they are from home.    But tell the Dauphin I will keep my state,    Be like a king, and show my sail of greatness,    When I do rouse me in my throne of France;    For that I have laid by my majesty    And plodded like a man for working-days;    But I will rise there with so full a glory    That I will dazzle all the eyes of France,    Yea, strike the Dauphin blind to look on us.    And tell the pleasant Prince this mock of his      Hath turn'd his balls to gun-stones, and his soul    Shall stand sore charged for the wasteful vengeance\n      That shall fly with them; for many a thousand widows\n",
    "YORK. From Ireland thus comes York to claim his right\n    And pluck the crown from feeble Henry's head:    Ring bells aloud, burn bonfires clear and bright,    To entertain great England's lawful king.    Ah, sancta majestas! who would not buy thee dear?    Let them obey that knows not how to rule;    This hand was made to handle nought but gold.    I cannot give due action to my words    Except a sword or sceptre balance it.\n      A sceptre shall it have, have I a soul\n    On which I'll toss the flower-de-luce of France.\n                         Enter BUCKINGHAM    [Aside] Whom have we here? Buckingham, to disturb me?\n    The King hath sent him, sure: I must dissemble.  BUCKINGHAM. York, if thou meanest well I greet thee well.    YORK. Humphrey of Buckingham, I accept thy greeting.    Art thou a messenger, or come of pleasure?  BUCKINGHAM. A messenger from Henry, our dread liege,    To know the reason of these arms in peace;    Or why thou, being a subject as I am,    Against thy oath and true allegiance sworn,    Should raise so great a power without his leave,    Or dare to bring thy force so near the court.  YORK. [Aside] Scarce can I speak, my choler is so great.    O, I could hew up rocks and fight with flint,    I am so angry at these abject terms;    And now, like Ajax Telamonius,    On sheep or oxen could I spend my fury.    I am far better born than is the King,    More like a king, more kingly in my thoughts;    But I must make fair weather yet awhile,    Till Henry be more weak and I more strong.-    Buckingham, I prithee, pardon me    That I have given no answer all this while;    My mind was troubled with deep melancholy.      The cause why I have brought this army hither    Is to remove proud Somerset from the King,    Seditious to his Grace and to the state.  BUCKINGHAM. That is too much presumption on thy part;    But if thy arms be to no other end,    The King hath yielded unto thy demand:\n      The Duke of Somerset is in the Tower.\n",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Information Retrieval

Dataset: mini-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@3	0.4583
cosine_precision@1	0.3427
cosine_precision@3	0.1528
cosine_precision@5	0.1014
cosine_precision@10	0.0563
cosine_recall@1	0.3427
cosine_recall@3	0.4583
cosine_recall@5	0.507
cosine_recall@10	0.563
cosine_ndcg@10	0.4482
cosine_mrr@200	0.4183
cosine_map@100	0.418
dot_accuracy@3	0.4583
dot_precision@1	0.3427
dot_precision@3	0.1528
dot_precision@5	0.1014
dot_precision@10	0.0563
dot_recall@1	0.3427
dot_recall@3	0.4583
dot_recall@5	0.507
dot_recall@10	0.563
dot_ndcg@10	0.4482
dot_mrr@200	0.4183
dot_map@100	0.418

Training Details

Training Dataset

Unnamed Dataset

Size: 10,359 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 22.32 tokens
max: 56 tokens
min: 35 tokens
mean: 238.33 tokens
max: 256 tokens

Samples:

anchor	positive
`Who is the general being described in the excerpt?`	PHILO. Nay, but this dotage of our general's[object Object] O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst[object Object] The buckles on his breast, reneges all temper,[object Object] And is become the bellows and the fan To cool a gipsy's lust.[object Object] Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,[object Object] with eunuchs fanning her[object Object] Look where they come![object Object] Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.[object Object] Enter a MESSENGER MESSENGER. News, my good lord, from Rome.[object Object] ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame[object Object] When shrill-tongu'd Fulvia scolds. The messengers![object Object]
`What is the main conflict highlighted in the excerpt?`	PHILO. Nay, but this dotage of our general's[object Object] O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst[object Object] The buckles on his breast, reneges all temper,[object Object] And is become the bellows and the fan To cool a gipsy's lust.[object Object] Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,[object Object] with eunuchs fanning her[object Object] Look where they come![object Object] Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.[object Object] Enter a MESSENGER MESSENGER. News, my good lord, from Rome.[object Object] ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame[object Object] When shrill-tongu'd Fulvia scolds. The messengers![object Object]
`The excerpt showcases the tension between Antony's loyalty to Cleopatra and his obligations to Caesar, as well as Cleopatra's influence over him.`	PHILO. Nay, but this dotage of our general's[object Object] O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst[object Object] The buckles on his breast, reneges all temper,[object Object] And is become the bellows and the fan To cool a gipsy's lust.[object Object] Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,[object Object] with eunuchs fanning her[object Object] Look where they come![object Object] Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.[object Object] Enter a MESSENGER MESSENGER. News, my good lord, from Rome.[object Object] ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame[object Object] When shrill-tongu'd Fulvia scolds. The messengers![object Object]

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 2,302 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 21.73 tokens
max: 61 tokens
min: 16 tokens
mean: 239.59 tokens
max: 256 tokens

Samples:

anchor	positive
`The excerpt highlights the tension between Antony's loyalty to Cleopatra and his standing in Rome, showcasing the intricate balance of power and love in the play.`	When shrill-tongu'd Fulvia scolds. The messengers![object Object] ANTONY. Let Rome in Tiber melt, and the wide arch Of the rang'd empire fall! Here is my space. Kingdoms are clay; our dungy earth alike Feeds beast as man. The nobleness of life Is to do thus [emhracing], when such a mutual pair And such a twain can do't, in which I bind, On pain of punishment, the world to weet We stand up peerless. CLEOPATRA. Excellent falsehood! Why did he marry Fulvia, and not love her? I'll seem the fool I am not. Antony Will be himself. ANTONY. But stirr'd by Cleopatra. Now for the love of Love and her soft hours, Let's not confound the time with conference harsh; There's not a minute of our lives should stretch Without some pleasure now. What sport to-night? CLEOPATRA. Hear the ambassadors. ANTONY. Fie, wrangling queen! Whom everything becomes- to chide, to laugh, To weep; whose every passion fully strives To make itself in thee fair and admir'd. No messenger but thine, and all alone To-night we'll wander through the streets and note The qualities of people. Come, my queen; Last night you did desire it. Speak not to us. Exeunt ANTONY and CLEOPATRA, with the train DEMETRIUS. Is Caesar with Antonius priz'd so slight? PHILO. Sir, sometimes when he is not Antony, He comes too short of that great property Which still should go with Antony. DEMETRIUS. I am full sorry That he approves the common liar, who Thus speaks of him at Rome; but I will hope[object Object] Of better deeds to-morrow. Rest you happy! Exeunt[object Object]
`What is the significance of the soothsayer in the context of the play?`	CHARMIAN. Lord Alexas, sweet Alexas, most anything Alexas, almost[object Object] most absolute Alexas, where's the soothsayer that you prais'd so to th' Queen? O that I knew this husband, which you say must charge his horns with garlands! ALEXAS. Soothsayer! SOOTHSAYER. Your will? CHARMIAN. Is this the man? Is't you, sir, that know things? SOOTHSAYER. In nature's infinite book of secrecy A little I can read. ALEXAS. Show him your hand.[object Object] Enter ENOBARBUS ENOBARBUS. Bring in the banquet quickly; wine enough[object Object] Cleopatra's health to drink. CHARMIAN. Good, sir, give me good fortune. SOOTHSAYER. I make not, but foresee. CHARMIAN. Pray, then, foresee me one. SOOTHSAYER. You shall be yet far fairer than you are. CHARMIAN. He means in flesh. IRAS. No, you shall paint when you are old. CHARMIAN. Wrinkles forbid! ALEXAS. Vex not his prescience; be attentive. CHARMIAN. Hush![object Object] SOOTHSAYER. You shall be more beloving than beloved.[object Object]
`What is the setting of the scene in which the excerpt takes place?`	sweet Isis, I beseech thee! And let her die too, and give him a[object Object] worse! And let worse follow worse, till the worst of all follow him laughing to his grave, fiftyfold a cuckold! Good Isis, hear me this prayer, though thou deny me a matter of more weight; good Isis, I beseech thee! IRAS. Amen. Dear goddess, hear that prayer of the people! For, as it is a heartbreaking to see a handsome man loose-wiv'd, so it is a deadly sorrow to behold a foul knave uncuckolded. Therefore, dear Isis, keep decorum, and fortune him accordingly! CHARMIAN. Amen. ALEXAS. Lo now, if it lay in their hands to make me a cuckold, they would make themselves whores but they'ld do't![object Object] Enter CLEOPATRA ENOBARBUS. Hush! Here comes Antony.[object Object] CHARMIAN. Not he; the Queen. CLEOPATRA. Saw you my lord? ENOBARBUS. No, lady. CLEOPATRA. Was he not here? CHARMIAN. No, madam. CLEOPATRA. He was dispos'd to mirth; but on the sudden A Roman thought hath struck him. Enobarbus! ENOBARBUS. Madam? CLEOPATRA. Seek him, and bring him hither. Where's Alexas? ALEXAS. Here, at your service. My lord approaches.[object Object] Enter ANTONY, with a MESSENGER and attendants CLEOPATRA. We will not look upon him. Go with us.[object Object] Exeunt CLEOPATRA, ENOBARBUS, and the rest MESSENGER. Fulvia thy wife first came into the field. ANTONY. Against my brother Lucius? MESSENGER. Ay.[object Object] But soon that war had end, and the time's state[object Object]

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 7
warmup_steps: 50
fp16: True
load_best_model_at_end: True
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: 32
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: 1.0
num_train_epochs: 7
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 50
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: 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: 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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	mini-dev_cosine_map@100
1.0	324	-	1.9598	0.3728
1.5432	500	2.1523	-	-
2.0	648	-	1.8067	0.4023
3.0	972	-	1.7600	0.4144
3.0864	1000	1.4271	-	-
4.0	1296	-	1.746	0.418
4.6296	1500	0.9807	-	-
5.0	1620	-	1.7604	0.4146
6.0	1944	-	1.7558	0.4153
6.1728	2000	0.7846	-	-
7.0	2268	-	1.7571	0.4180

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.43.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

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}
}

	anchor	positive
type	string	string
details	min: 10 tokens mean: 22.32 tokens max: 56 tokens	min: 35 tokens mean: 238.33 tokens max: 256 tokens