andegpt-embed
This is a sentence-transformers model finetuned from microsoft/mpnet-base. 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: microsoft/mpnet-base
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Language: es
- 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': 512, '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})
)
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("enpaiva/embed-andegpt-280724")
# Run inference
sentences = [
'¿Cuál es el número del artículo que trata sobre la mínima sección permisible para una lámpara o grupo de lámparas?',
'Reglamento de Baja Tensión de la ANDE: El 14.7.3 trata sobre: La mínima sección permisible para una lámpara, o grupo de lámparas que forman un solo artefacto de iluminación, será de 1 mm².',
'Reglamento de Baja Tensión de la ANDE: El 19.2.1 trata sobre: La caída de tensión máxima permisible, es la siguiente: a) Para iluminación, en general (19.1.1), hasta 4%. -2% en el alimentador, y -2% en el circuito (19.1.2). b) Para fuerza motriz y/o calefacción, hasta 5%. -4% en el alimentador, y -1% en el ramal. c) En el caso de clientes que reciban la energía a tensión diferente de las normales de utilización (19.1.3), hasta 4%.',
]
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
Triplet
- Dataset:
andegpt-dev
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9971 |
dot_accuracy | 0.0032 |
manhattan_accuracy | 0.9968 |
euclidean_accuracy | 0.9971 |
max_accuracy | 0.9971 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
prediction_loss_only
: Falselearning_rate
: 2e-05lr_scheduler_type
: cosinelog_level_replica
: passivelog_on_each_node
: Falselogging_nan_inf_filter
: Falsebf16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_loss_only
: Falseper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0warmup_steps
: 0log_level
: passivelog_level_replica
: passivelog_on_each_node
: Falselogging_nan_inf_filter
: Falsesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falsefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | andegpt-dev_max_accuracy |
---|---|---|---|---|
0 | 0 | - | - | 0.6136 |
0.0270 | 250 | 0.8269 | 0.3100 | 0.9658 |
0.0540 | 500 | 0.3667 | 0.2169 | 0.9721 |
0.0809 | 750 | 0.2305 | 0.1594 | 0.9801 |
0.1079 | 1000 | 0.1866 | 0.1372 | 0.9830 |
0.1349 | 1250 | 0.1639 | 0.1114 | 0.9859 |
0.1619 | 1500 | 0.1375 | 0.0983 | 0.9871 |
0.1889 | 1750 | 0.1082 | 0.0815 | 0.9886 |
0.2158 | 2000 | 0.1023 | 0.0723 | 0.9900 |
0.2428 | 2250 | 0.0777 | 0.0703 | 0.9905 |
0.2698 | 2500 | 0.0809 | 0.0656 | 0.9896 |
0.2968 | 2750 | 0.0639 | 0.0662 | 0.9891 |
0.3238 | 3000 | 0.0633 | 0.0590 | 0.9922 |
0.3507 | 3250 | 0.0545 | 0.0533 | 0.9930 |
0.3777 | 3500 | 0.0541 | 0.0458 | 0.9932 |
0.4047 | 3750 | 0.0475 | 0.0365 | 0.9947 |
0.4317 | 4000 | 0.0394 | 0.0330 | 0.9939 |
0.4587 | 4250 | 0.0561 | 0.0345 | 0.9939 |
0.4856 | 4500 | 0.0432 | 0.0327 | 0.9942 |
0.5126 | 4750 | 0.0417 | 0.0328 | 0.9944 |
0.5396 | 5000 | 0.0388 | 0.0252 | 0.9949 |
0.5666 | 5250 | 0.033 | 0.0284 | 0.9959 |
0.5936 | 5500 | 0.0243 | 0.0229 | 0.9964 |
0.6205 | 5750 | 0.023 | 0.0223 | 0.9959 |
0.6475 | 6000 | 0.0313 | 0.0209 | 0.9966 |
0.6745 | 6250 | 0.0285 | 0.0208 | 0.9961 |
0.7015 | 6500 | 0.022 | 0.0192 | 0.9961 |
0.7285 | 6750 | 0.0219 | 0.0235 | 0.9956 |
0.7555 | 7000 | 0.0258 | 0.0186 | 0.9954 |
0.7824 | 7250 | 0.0226 | 0.0230 | 0.9959 |
0.8094 | 7500 | 0.0226 | 0.0240 | 0.9961 |
0.8364 | 7750 | 0.0208 | 0.0173 | 0.9968 |
0.8634 | 8000 | 0.0147 | 0.0200 | 0.9956 |
0.8904 | 8250 | 0.0193 | 0.0147 | 0.9971 |
0.9173 | 8500 | 0.0254 | 0.0136 | 0.9968 |
0.9443 | 8750 | 0.0148 | 0.0132 | 0.9971 |
0.9713 | 9000 | 0.0174 | 0.0157 | 0.9968 |
0.9983 | 9250 | 0.0221 | 0.0144 | 0.9971 |
Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.2.0+cu121
- Accelerate: 0.28.0
- Datasets: 2.20.0
- Tokenizers: 0.15.2
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}
}
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Base model
microsoft/mpnet-baseEvaluation results
- Cosine Accuracy on andegpt devself-reported0.997
- Dot Accuracy on andegpt devself-reported0.003
- Manhattan Accuracy on andegpt devself-reported0.997
- Euclidean Accuracy on andegpt devself-reported0.997
- Max Accuracy on andegpt devself-reported0.997