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base_model: sentence-transformers/all-mpnet-base-v2
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:25300
  - loss:MultipleNegativesRankingLoss
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: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine
          - type: pearson_manhattan
            value: .nan
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: .nan
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: .nan
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: .nan
            name: Spearman Euclidean
          - type: pearson_dot
            value: .nan
            name: Pearson Dot
          - type: spearman_dot
            value: .nan
            name: Spearman Dot
          - type: pearson_max
            value: .nan
            name: Pearson Max
          - type: spearman_max
            value: .nan
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: test eval
          type: test-eval
        metrics:
          - type: pearson_cosine
            value: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine
          - type: pearson_manhattan
            value: .nan
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: .nan
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: .nan
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: .nan
            name: Spearman Euclidean
          - type: pearson_dot
            value: .nan
            name: Pearson Dot
          - type: spearman_dot
            value: .nan
            name: Spearman Dot
          - type: pearson_max
            value: .nan
            name: Pearson Max
          - type: spearman_max
            value: .nan
            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

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	nan
spearman_cosine	nan
pearson_manhattan	nan
spearman_manhattan	nan
pearson_euclidean	nan
spearman_euclidean	nan
pearson_dot	nan
spearman_dot	nan
pearson_max	nan
spearman_max	nan

Semantic Similarity

Dataset: test-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	nan
spearman_cosine	nan
pearson_manhattan	nan
spearman_manhattan	nan
pearson_euclidean	nan
spearman_euclidean	nan
pearson_dot	nan
spearman_dot	nan
pearson_max	nan
spearman_max	nan

Training Details

Training Dataset

Unnamed Dataset

Size: 25,300 training samples
Columns: sentence1, sentence2, and texts

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 5,422 evaluation samples
Columns: sentence1, sentence2, and texts

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 2
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: 16
per_device_eval_batch_size: 16
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: 2
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	Validation Loss	spearman_max	test-eval_spearman_max
0.0632	100	4.9949	2.0402	nan	-
0.1264	200	1.9907	1.8355	nan	-
0.1896	300	1.8898	1.9113	nan	-
0.2528	400	1.8334	1.7294	nan	-
0.3161	500	1.7497	1.7388	nan	-
0.3793	600	1.6786	1.6524	nan	-
0.4425	700	1.6914	1.6440	nan	-
0.5057	800	1.6303	1.6218	nan	-
0.5689	900	1.6388	1.6212	nan	-
0.6321	1000	1.6032	1.6182	nan	-
0.6953	1100	1.5957	1.5945	nan	-
0.7585	1200	1.6303	1.5753	nan	-
0.8217	1300	1.5978	1.5705	nan	-
0.8850	1400	1.554	1.5663	nan	-
0.9482	1500	1.4899	1.5525	nan	-
1.0114	1600	1.4792	1.5962	nan	-
1.0746	1700	1.4683	1.5481	nan	-
1.1378	1800	1.4615	1.5256	nan	-
1.2010	1900	1.4395	1.5321	nan	-
1.2642	2000	1.3524	1.5148	nan	-
1.3274	2100	1.3876	1.5356	nan	-
1.3906	2200	1.4376	1.4979	nan	-
1.4539	2300	1.4187	1.5046	nan	-
1.5171	2400	1.4604	1.5011	nan	-
1.5803	2500	1.4194	1.4851	nan	-
1.6435	2600	1.4057	1.4897	nan	-
1.7067	2700	1.3683	1.4921	nan	-
1.7699	2800	1.3333	1.4797	nan	-
1.8331	2900	1.3961	1.4752	nan	-
1.8963	3000	1.3718	1.4693	nan	-
1.9595	3100	1.3263	1.4699	nan	-
2.0	3164	-	-	-	nan

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.0
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
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",
}

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}
}