metadata

base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:13063
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: I cant wait to leave Chicago
    sentences:
      - This is the shit Chicago needs to be recognized for not Keef
      - is candice singing again tonight
      - half time Chelsea were losing 10
  - source_sentence: Andre miller best lobbing pg in the game
    sentences:
      - Am I the only one who dont get Amber alert
      - Backstrom hurt in warmup Harding could start
      - Andre miller is even slower in person
  - source_sentence: Bayless couldve dunked that from the free throw
    sentences:
      - but what great finger roll by Bayless
      - Wow Bayless has to make EspnSCTop with that end of 3rd
      - i mean calum u didnt follow
  - source_sentence: Backstrom Hurt in warmups Harding gets the start
    sentences:
      - Should I go to Nashville or Chicago for my 17th birthday
      - I hate Chelsea possibly more than most
      - Of course Backstrom would get injured during warmups
  - source_sentence: Calum I love you plz follow me
    sentences:
      - CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME
      - >-
        Walking around downtown Chicago in a dress and listening to the new Iggy
        Pop
      - I think Candice has what it takes to win American Idol AND Angie too
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.6949485250178733
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6626359968437283
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.688092975176289
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6630998028133662
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6880277270034267
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6626358741747785
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.694948520847878
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6626359082695851
            name: Spearman Dot
          - type: pearson_max
            value: 0.6949485250178733
            name: Pearson Max
          - type: spearman_max
            value: 0.6630998028133662
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 tokens
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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
  (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("mspy/twitter-paraphrase-embeddings")
# Run inference
sentences = [
    'Calum I love you plz follow me',
    'CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME',
    'Walking around downtown Chicago in a dress and listening to the new Iggy Pop',
]
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

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6949
spearman_cosine	0.6626
pearson_manhattan	0.6881
spearman_manhattan	0.6631
pearson_euclidean	0.688
spearman_euclidean	0.6626
pearson_dot	0.6949
spearman_dot	0.6626
pearson_max	0.6949
spearman_max	0.6631

Training Details

Training Dataset

Unnamed Dataset

Size: 13,063 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 7 tokens
mean: 11.16 tokens
max: 28 tokens

min: 7 tokens
mean: 12.31 tokens
max: 22 tokens

min: 0.0
mean: 0.33
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 7 tokens mean: 11.16 tokens max: 28 tokens	min: 7 tokens mean: 12.31 tokens max: 22 tokens	min: 0.0 mean: 0.33 max: 1.0

Samples:

sentence1	sentence2	label
`EJ Manuel the 1st QB to go in this draft`	`But my bro from the 757 EJ Manuel is the 1st QB gone`	`1.0`
`EJ Manuel the 1st QB to go in this draft`	`Can believe EJ Manuel went as the 1st QB in the draft`	`1.0`
`EJ Manuel the 1st QB to go in this draft`	`EJ MANUEL IS THE 1ST QB what`	`0.6`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

Unnamed Dataset

Size: 4,727 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 7 tokens
mean: 10.04 tokens
max: 16 tokens

min: 7 tokens
mean: 12.22 tokens
max: 26 tokens

min: 0.0
mean: 0.33
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 7 tokens mean: 10.04 tokens max: 16 tokens	min: 7 tokens mean: 12.22 tokens max: 26 tokens	min: 0.0 mean: 0.33 max: 1.0

Samples:

sentence1	sentence2	label
`A Walk to Remember is the definition of true love`	`A Walk to Remember is on and Im in town and Im upset`	`0.2`
`A Walk to Remember is the definition of true love`	`A Walk to Remember is the cutest thing`	`0.6`
`A Walk to Remember is the definition of true love`	`A walk to remember is on ABC family youre welcome`	`0.2`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
gradient_accumulation_steps: 2
learning_rate: 2e-05
num_train_epochs: 4
warmup_ratio: 0.1
fp16: 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: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
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: 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
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: 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: 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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	spearman_cosine
0.1225	100	-	0.0729	0.6058
0.2449	200	-	0.0646	0.6340
0.3674	300	-	0.0627	0.6397
0.4899	400	-	0.0621	0.6472
0.6124	500	0.0627	0.0626	0.6496
0.7348	600	-	0.0621	0.6446
0.8573	700	-	0.0593	0.6695
0.9798	800	-	0.0636	0.6440
1.1023	900	-	0.0618	0.6525
1.2247	1000	0.0383	0.0604	0.6639
1.3472	1100	-	0.0608	0.6590
1.4697	1200	-	0.0620	0.6504
1.5922	1300	-	0.0617	0.6467
1.7146	1400	-	0.0615	0.6574
1.8371	1500	0.0293	0.0622	0.6536
1.9596	1600	-	0.0609	0.6599
2.0821	1700	-	0.0605	0.6658
2.2045	1800	-	0.0615	0.6588
2.3270	1900	-	0.0615	0.6575
2.4495	2000	0.0215	0.0614	0.6598
2.5720	2100	-	0.0603	0.6681
2.6944	2200	-	0.0606	0.6669
2.8169	2300	-	0.0605	0.6642
2.9394	2400	-	0.0606	0.6630
3.0618	2500	0.018	0.0611	0.6616
3.1843	2600	-	0.0611	0.6619
3.3068	2700	-	0.0611	0.6608
3.4293	2800	-	0.0608	0.6632
3.5517	2900	-	0.0608	0.6623
3.6742	3000	0.014	0.0615	0.6596
3.7967	3100	-	0.0612	0.6616
3.9192	3200	-	0.0610	0.6626

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.43.3
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}