metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5005
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
  - source_sentence: especialista de risco e prevenção a fraudes
    sentences:
      - risk & compliance
      - internal communication
      - accounting
  - source_sentence: coord integracao do cliente ii
    sentences:
      - strategic planning
      - customer experience
      - não encontrado (adicione nas observações)
  - source_sentence: gerente sr. marketing e performance
    sentences:
      - business operations
      - d&i
      - performance marketing
  - source_sentence: gerente executivo de operacoes
    sentences:
      - business operations
      - sdr
      - product management
  - source_sentence: sr designer
    sentences:
      - product design
      - talent acquisition
      - lawyer
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
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.6245583038869258
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8206713780918727
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8754416961130742
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.926678445229682
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6245583038869258
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2735571260306242
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17508833922261482
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0926678445229682
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6245583038869258
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8206713780918727
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8754416961130742
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.926678445229682
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7790196193570564
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7312496494475299
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7347864977321262
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.6245583038869258
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8206713780918727
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8754416961130742
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.926678445229682
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6245583038869258
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2735571260306242
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.17508833922261482
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.0926678445229682
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.6245583038869258
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8206713780918727
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.8754416961130742
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.926678445229682
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.7790196193570564
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7312496494475299
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7347864977321262
            name: Dot Map@100

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'sr designer',
    'product design',
    'talent acquisition',
]
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

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6246
cosine_accuracy@3	0.8207
cosine_accuracy@5	0.8754
cosine_accuracy@10	0.9267
cosine_precision@1	0.6246
cosine_precision@3	0.2736
cosine_precision@5	0.1751
cosine_precision@10	0.0927
cosine_recall@1	0.6246
cosine_recall@3	0.8207
cosine_recall@5	0.8754
cosine_recall@10	0.9267
cosine_ndcg@10	0.779
cosine_mrr@10	0.7312
cosine_map@100	0.7348
dot_accuracy@1	0.6246
dot_accuracy@3	0.8207
dot_accuracy@5	0.8754
dot_accuracy@10	0.9267
dot_precision@1	0.6246
dot_precision@3	0.2736
dot_precision@5	0.1751
dot_precision@10	0.0927
dot_recall@1	0.6246
dot_recall@3	0.8207
dot_recall@5	0.8754
dot_recall@10	0.9267
dot_ndcg@10	0.779
dot_mrr@10	0.7312
dot_map@100	0.7348

Training Details

Training Dataset

Unnamed Dataset

Size: 5,005 training samples
Columns: input and output
Approximate statistics based on the first 1000 samples:
input output
type string string
details
min: 3 tokens
mean: 8.83 tokens
max: 21 tokens

min: 3 tokens
mean: 7.21 tokens
max: 18 tokens
Samples:

input output

fresador mecanico ii não encontrado (adicione nas observações)

analista de sistemas ui ux iii product design

devops devops engineering

	input	output
type	string	string
details	min: 3 tokens mean: 8.83 tokens max: 21 tokens	min: 3 tokens mean: 7.21 tokens max: 18 tokens

input	output
`fresador mecanico ii`	`não encontrado (adicione nas observações)`
`analista de sistemas ui ux iii`	`product design`
`devops`	`devops engineering`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 1,132 evaluation samples
Columns: input and output
Approximate statistics based on the first 1000 samples:
input output
type string string
details
min: 3 tokens
mean: 8.76 tokens
max: 20 tokens

min: 3 tokens
mean: 7.08 tokens
max: 18 tokens

	input	output
type	string	string
details	min: 3 tokens mean: 8.76 tokens max: 20 tokens	min: 3 tokens mean: 7.08 tokens max: 18 tokens

Samples:

input	output
`produtor (a) de video pleno`	`não encontrado (adicione nas observações)`
`ai staff software engineer`	`software engineering`
`montador digital i`	`não encontrado (adicione nas observações)`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
warmup_ratio: 0.1

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: 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: 3.0
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: 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: 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
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	cosine_map@100
0	0	-	-	0.3578
0.3195	200	-	0.9975	0.5035
0.6390	400	-	0.8471	0.5845
0.7987	500	1.0355	-	-
0.9585	600	-	0.7569	0.6157
1.2780	800	-	0.7542	0.6565
1.5974	1000	0.648	0.6835	0.6786
1.9169	1200	-	0.6569	0.6851
2.2364	1400	-	0.6480	0.7167
2.3962	1500	0.5253	-	-
2.5559	1600	-	0.6506	0.7110
2.8754	1800	-	0.6391	0.7348

Framework Versions

Python: 3.11.6
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 2.14.4
Tokenizers: 0.20.3

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}
}