metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10822
  - loss:CosineSimilarityLoss
base_model: neuralmind/bert-large-portuguese-cased
widget:
  - source_sentence: plastificadora documento a4
    sentences:
      - >-
        produto destinar colecionador figura acao selo moeda quadrinho raro item
        historico comumente vender loja especializar feira tematico
      - >-
        caderno lapis caneta mochila escolar item escritorio grampeador post-it
        alir papel especial trabalho artistico academico
      - >-
        console videogame controle headsets cadeira gamer jogo diferentes
        plataforma pc playstation xbox
  - source_sentence: carrinho supermercado brinquedo
    sentences:
      - >-
        caderno lapis caneta mochila escolar item escritorio grampeador post-it
        alir papel especial trabalho artistico academico
      - >-
        caderno lapis caneta mochila escolar item escritorio grampeador post-it
        alir papel especial trabalho artistico academico
      - produto voltar publico adulto brinquedo sexual jogo adulto
  - source_sentence: kit prato iniciante
    sentences:
      - >-
        paes fresco pizza pre-assadas bolo torta produto artesanal cafe
        sobremeso
      - >-
        movel utensilio domestico item decorativo produto limpeza acessorio
        organizacao manutencao casa
      - >-
        violoes teclado microfone pedal efeito suporte acessorio corda afinador
        voltar musico iniciante profissional
  - source_sentence: capacete seguranca
    sentences:
      - >-
        artigo esportivo bola raquete acessorio academia roupa esportiva
        equipamento esporte outdoor escalada ciclismo
      - >-
        tinta cimento ferramenta construcao material reforma piso azulejo
        equipamento protecao individual
      - >-
        caderno lapis caneta mochila escolar item escritorio grampeador post-it
        alir papel especial trabalho artistico academico
  - source_sentence: livro ficcao
    sentences:
      - produto voltar publico adulto brinquedo sexual jogo adulto
      - >-
        caderno lapis caneta mochila escolar item escritorio grampeador post-it
        alir papel especial trabalho artistico academico
      - >-
        produto basico arroz feijao massa item mercearia snack alimento congelar
        dia dia situacoes emergencial
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on neuralmind/bert-large-portuguese-cased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: eval similarity
          type: eval-similarity
        metrics:
          - type: pearson_cosine
            value: 0.9024497617955924
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8404221831399815
            name: Spearman Cosine

SentenceTransformer based on neuralmind/bert-large-portuguese-cased

This is a sentence-transformers model finetuned from neuralmind/bert-large-portuguese-cased. It maps sentences & paragraphs to a 1024-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: neuralmind/bert-large-portuguese-cased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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("SenhorDasMoscas/acho2-ptbr-e4-lr3e-05")
# Run inference
sentences = [
    'livro ficcao',
    'produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial',
    'produto voltar publico adulto brinquedo sexual jogo adulto',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Semantic Similarity

Dataset: eval-similarity
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9024
spearman_cosine	0.8404

Training Details

Training Dataset

Unnamed Dataset

Size: 10,822 training samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string float
details
min: 3 tokens
mean: 7.16 tokens
max: 15 tokens

min: 11 tokens
mean: 25.08 tokens
max: 36 tokens

min: 0.1
mean: 0.53
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 3 tokens mean: 7.16 tokens max: 15 tokens	min: 11 tokens mean: 25.08 tokens max: 36 tokens	min: 0.1 mean: 0.53 max: 1.0

Samples:

text1	text2	label
`tenis nike`	`artigo esportivo bola raquete acessorio academia roupa esportiva equipamento esporte outdoor escalada ciclismo`	`1.0`
`tapete Sao Carlos`	`tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual`	`0.1`
`kit sensual lua Mel`	`produto voltar publico adulto brinquedo sexual jogo adulto`	`1.0`

Loss: CosineSimilarityLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 1,203 evaluation samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string float
details
min: 3 tokens
mean: 7.09 tokens
max: 14 tokens

min: 11 tokens
mean: 25.62 tokens
max: 36 tokens

min: 0.1
mean: 0.57
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 3 tokens mean: 7.09 tokens max: 14 tokens	min: 11 tokens mean: 25.62 tokens max: 36 tokens	min: 0.1 mean: 0.57 max: 1.0

Samples:

text1	text2	label
`carvao`	`tinta cimento ferramenta construcao material reforma piso azulejo equipamento protecao individual`	`1.0`
`telha fibrocimento`	`produto basico arroz feijao massa item mercearia snack alimento congelar dia dia situacoes emergencial`	`0.1`
`racao cachorro pedigree loja decoracao`	`baloe paineis decorativo item tematico casamento aniversario luminaria bandeirola vela acessorio transformar ambiente festa ocasioes especial`	`0.1`

Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 3e-05
weight_decay: 0.1
num_train_epochs: 4
warmup_ratio: 0.1
warmup_steps: 135
fp16: True
load_best_model_at_end: 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: 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: 3e-05
weight_decay: 0.1
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: 135
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: None
hub_always_push: False
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	eval-similarity_spearman_cosine
0.0147	5	0.2323	-	-
0.0295	10	0.2056	-	-
0.0442	15	0.2203	-	-
0.0590	20	0.1947	-	-
0.0737	25	0.1811	-	-
0.0885	30	0.1526	-	-
0.1032	35	0.1511	-	-
0.1180	40	0.1543	-	-
0.1327	45	0.1529	-	-
0.1475	50	0.1296	-	-
0.1622	55	0.1212	-	-
0.1770	60	0.1023	-	-
0.1917	65	0.1011	-	-
0.2065	70	0.1047	-	-
0.2212	75	0.1077	-	-
0.2360	80	0.0909	-	-
0.2507	85	0.0913	-	-
0.2655	90	0.1045	-	-
0.2802	95	0.0761	-	-
0.2950	100	0.0705	-	-
0.3097	105	0.086	-	-
0.3245	110	0.0753	-	-
0.3392	115	0.0652	-	-
0.3540	120	0.0663	-	-
0.3687	125	0.0862	-	-
0.3835	130	0.085	-	-
0.3982	135	0.0803	-	-
0.4130	140	0.088	-	-
0.4277	145	0.0569	-	-
0.4425	150	0.0689	-	-
0.4572	155	0.0746	-	-
0.4720	160	0.069	-	-
0.4867	165	0.0665	-	-
0.5015	170	0.0778	-	-
0.5162	175	0.0513	-	-
0.5310	180	0.0525	-	-
0.5457	185	0.0817	-	-
0.5605	190	0.0731	-	-
0.5752	195	0.0704	-	-
0.5900	200	0.0742	0.0651	0.8003
0.6047	205	0.0722	-	-
0.6195	210	0.0894	-	-
0.6342	215	0.0679	-	-
0.6490	220	0.0532	-	-
0.6637	225	0.0877	-	-
0.6785	230	0.2859	-	-
0.6932	235	0.3122	-	-
0.7080	240	0.1166	-	-
0.7227	245	0.0785	-	-
0.7375	250	0.0636	-	-
0.7522	255	0.0613	-	-
0.7670	260	0.0648	-	-
0.7817	265	0.0597	-	-
0.7965	270	0.0597	-	-
0.8112	275	0.0662	-	-
0.8260	280	0.0581	-	-
0.8407	285	0.0685	-	-
0.8555	290	0.0629	-	-
0.8702	295	0.0694	-	-
0.8850	300	0.055	-	-
0.8997	305	0.0647	-	-
0.9145	310	0.0634	-	-
0.9292	315	0.0724	-	-
0.9440	320	0.0658	-	-
0.9587	325	0.0594	-	-
0.9735	330	0.053	-	-
0.9882	335	0.0622	-	-
1.0029	340	0.0622	-	-
1.0177	345	0.0593	-	-
1.0324	350	0.0541	-	-
1.0472	355	0.0493	-	-
1.0619	360	0.0504	-	-
1.0767	365	0.0539	-	-
1.0914	370	0.0439	-	-
1.1062	375	0.0613	-	-
1.1209	380	0.0432	-	-
1.1357	385	0.0617	-	-
1.1504	390	0.0546	-	-
1.1652	395	0.0427	-	-
1.1799	400	0.0674	0.0488	0.8279
1.1947	405	0.055	-	-
1.2094	410	0.0393	-	-
1.2242	415	0.0561	-	-
1.2389	420	0.0531	-	-
1.2537	425	0.0374	-	-
1.2684	430	0.0374	-	-
1.2832	435	0.0369	-	-
1.2979	440	0.0408	-	-
1.3127	445	0.0508	-	-
1.3274	450	0.0558	-	-
1.3422	455	0.0566	-	-
1.3569	460	0.0466	-	-
1.3717	465	0.0363	-	-
1.3864	470	0.0489	-	-
1.4012	475	0.0535	-	-
1.4159	480	0.0502	-	-
1.4307	485	0.0429	-	-
1.4454	490	0.0541	-	-
1.4602	495	0.057	-	-
1.4749	500	0.0402	-	-
1.4897	505	0.0464	-	-
1.5044	510	0.0405	-	-
1.5192	515	0.0469	-	-
1.5339	520	0.0519	-	-
1.5487	525	0.0338	-	-
1.5634	530	0.0476	-	-
1.5782	535	0.0385	-	-
1.5929	540	0.0442	-	-
1.6077	545	0.0379	-	-
1.6224	550	0.0477	-	-
1.6372	555	0.0525	-	-
1.6519	560	0.0487	-	-
1.6667	565	0.0499	-	-
1.6814	570	0.0344	-	-
1.6962	575	0.0503	-	-
1.7109	580	0.0568	-	-
1.7257	585	0.0465	-	-
1.7404	590	0.0325	-	-
1.7552	595	0.0479	-	-
1.7699	600	0.046	0.0466	0.8309
1.7847	605	0.0482	-	-
1.7994	610	0.0546	-	-
1.8142	615	0.0465	-	-
1.8289	620	0.049	-	-
1.8437	625	0.0422	-	-
1.8584	630	0.0358	-	-
1.8732	635	0.0519	-	-
1.8879	640	0.0416	-	-
1.9027	645	0.0344	-	-
1.9174	650	0.0339	-	-
1.9322	655	0.0365	-	-
1.9469	660	0.038	-	-
1.9617	665	0.0417	-	-
1.9764	670	0.0521	-	-
1.9912	675	0.0242	-	-
2.0059	680	0.0405	-	-
2.0206	685	0.0233	-	-
2.0354	690	0.0299	-	-
2.0501	695	0.0194	-	-
2.0649	700	0.0424	-	-
2.0796	705	0.0245	-	-
2.0944	710	0.0374	-	-
2.1091	715	0.0295	-	-
2.1239	720	0.0236	-	-
2.1386	725	0.0477	-	-
2.1534	730	0.0211	-	-
2.1681	735	0.0306	-	-
2.1829	740	0.0265	-	-
2.1976	745	0.0398	-	-
2.2124	750	0.0468	-	-
2.2271	755	0.0252	-	-
2.2419	760	0.0329	-	-
2.2566	765	0.0317	-	-
2.2714	770	0.035	-	-
2.2861	775	0.0387	-	-
2.3009	780	0.037	-	-
2.3156	785	0.0285	-	-
2.3304	790	0.0377	-	-
2.3451	795	0.0344	-	-
2.3599	800	0.0335	0.0431	0.8360
2.3746	805	0.0296	-	-
2.3894	810	0.0357	-	-
2.4041	815	0.0244	-	-
2.4189	820	0.0373	-	-
2.4336	825	0.0295	-	-
2.4484	830	0.0353	-	-
2.4631	835	0.0303	-	-
2.4779	840	0.0206	-	-
2.4926	845	0.0284	-	-
2.5074	850	0.0293	-	-
2.5221	855	0.035	-	-
2.5369	860	0.0295	-	-
2.5516	865	0.0349	-	-
2.5664	870	0.0195	-	-
2.5811	875	0.0265	-	-
2.5959	880	0.0298	-	-
2.6106	885	0.0321	-	-
2.6254	890	0.0321	-	-
2.6401	895	0.0299	-	-
2.6549	900	0.0216	-	-
2.6696	905	0.02	-	-
2.6844	910	0.0277	-	-
2.6991	915	0.0381	-	-
2.7139	920	0.0296	-	-
2.7286	925	0.0339	-	-
2.7434	930	0.035	-	-
2.7581	935	0.0293	-	-
2.7729	940	0.038	-	-
2.7876	945	0.0291	-	-
2.8024	950	0.0411	-	-
2.8171	955	0.0377	-	-
2.8319	960	0.0282	-	-
2.8466	965	0.0388	-	-
2.8614	970	0.0286	-	-
2.8761	975	0.0177	-	-
2.8909	980	0.0352	-	-
2.9056	985	0.0329	-	-
2.9204	990	0.0265	-	-
2.9351	995	0.0363	-	-
2.9499	1000	0.021	0.0404	0.8374
2.9646	1005	0.0342	-	-
2.9794	1010	0.0415	-	-
2.9941	1015	0.0232	-	-
3.0088	1020	0.0251	-	-
3.0236	1025	0.0317	-	-
3.0383	1030	0.0344	-	-
3.0531	1035	0.021	-	-
3.0678	1040	0.0271	-	-
3.0826	1045	0.021	-	-
3.0973	1050	0.0151	-	-
3.1121	1055	0.0222	-	-
3.1268	1060	0.0186	-	-
3.1416	1065	0.0357	-	-
3.1563	1070	0.0179	-	-
3.1711	1075	0.0291	-	-
3.1858	1080	0.0313	-	-
3.2006	1085	0.0349	-	-
3.2153	1090	0.0181	-	-
3.2301	1095	0.0294	-	-
3.2448	1100	0.0216	-	-
3.2596	1105	0.0334	-	-
3.2743	1110	0.0256	-	-
3.2891	1115	0.026	-	-
3.3038	1120	0.0176	-	-
3.3186	1125	0.0231	-	-
3.3333	1130	0.0164	-	-
3.3481	1135	0.0226	-	-
3.3628	1140	0.0286	-	-
3.3776	1145	0.02	-	-
3.3923	1150	0.0229	-	-
3.4071	1155	0.0231	-	-
3.4218	1160	0.0289	-	-
3.4366	1165	0.0188	-	-
3.4513	1170	0.0313	-	-
3.4661	1175	0.0179	-	-
3.4808	1180	0.0157	-	-
3.4956	1185	0.0252	-	-
3.5103	1190	0.019	-	-
3.5251	1195	0.0251	-	-
3.5398	1200	0.021	0.0399	0.8404
3.5546	1205	0.0154	-	-
3.5693	1210	0.0187	-	-
3.5841	1215	0.0221	-	-
3.5988	1220	0.0148	-	-
3.6136	1225	0.0168	-	-
3.6283	1230	0.0236	-	-
3.6431	1235	0.0194	-	-
3.6578	1240	0.0245	-	-
3.6726	1245	0.0171	-	-
3.6873	1250	0.0235	-	-
3.7021	1255	0.0243	-	-
3.7168	1260	0.0325	-	-
3.7316	1265	0.0196	-	-
3.7463	1270	0.0362	-	-
3.7611	1275	0.0188	-	-
3.7758	1280	0.0151	-	-
3.7906	1285	0.0189	-	-
3.8053	1290	0.0286	-	-
3.8201	1295	0.0266	-	-
3.8348	1300	0.0216	-	-
3.8496	1305	0.0218	-	-
3.8643	1310	0.0214	-	-
3.8791	1315	0.0224	-	-
3.8938	1320	0.0213	-	-
3.9086	1325	0.0302	-	-
3.9233	1330	0.0196	-	-
3.9381	1335	0.0218	-	-
3.9528	1340	0.0226	-	-
3.9676	1345	0.0204	-	-
3.9823	1350	0.0215	-	-
3.9971	1355	0.0258	-	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Accelerate: 1.1.1
Datasets: 2.14.4
Tokenizers: 0.21.0

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",
}