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}
}
- Downloads last month
- 14
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for enpaiva/embed-andegpt-H768
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