embed-andegpt-H384
This is a sentence-transformers model finetuned from nreimers/MiniLM-L6-H384-uncased. It maps sentences & paragraphs to a 384-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: nreimers/MiniLM-L6-H384-uncased
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 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: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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-H384")
# Run inference
sentences = [
'¿Cuál es el nombre del reglamento que se menciona en la información proporcionada?',
'Reglamento de Baja Tensión de la ANDE: El 10- trata sobre Partes de que se compone una instalación eléctrica: y tiene las siguientes sub-secciones: <sub-section>10.1</sub-section>',
'Reglamento de Baja Tensión de la ANDE: El 37- trata sobre Soldadura eléctrica: y tiene las siguientes sub-secciones: <sub-section>37.1</sub-section>, <sub-section>37.2</sub-section>, <sub-section>37.3</sub-section>, <sub-section>37.4</sub-section>',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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 | 0.9983 |
dot_accuracy | 0.0022 |
manhattan_accuracy | 0.9985 |
euclidean_accuracy | 0.9983 |
max_accuracy | 0.9985 |
Triplet
- Dataset:
andegpt-test
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9973 |
dot_accuracy | 0.0024 |
manhattan_accuracy | 0.9971 |
euclidean_accuracy | 0.9973 |
max_accuracy | 0.9973 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
prediction_loss_only
: Falseper_device_train_batch_size
: 32learning_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
: 32per_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 | andegpt-test_max_accuracy |
---|---|---|---|---|---|
0 | 0 | - | - | 0.5920 | - |
0.1079 | 250 | 2.3094 | 0.7200 | 0.9597 | - |
0.2158 | 500 | 0.7952 | 0.3598 | 0.9813 | - |
0.3237 | 750 | 0.4862 | 0.2162 | 0.9910 | - |
0.4316 | 1000 | 0.3304 | 0.1558 | 0.9927 | - |
0.5395 | 1250 | 0.2527 | 0.1140 | 0.9961 | - |
0.6474 | 1500 | 0.1987 | 0.0859 | 0.9964 | - |
0.7553 | 1750 | 0.1617 | 0.0729 | 0.9959 | - |
0.8632 | 2000 | 0.1419 | 0.0562 | 0.9966 | - |
0.9711 | 2250 | 0.1132 | 0.0495 | 0.9968 | - |
1.0790 | 2500 | 0.1043 | 0.0429 | 0.9971 | - |
1.1869 | 2750 | 0.0947 | 0.0368 | 0.9978 | - |
1.2948 | 3000 | 0.0736 | 0.0367 | 0.9976 | - |
1.4027 | 3250 | 0.0661 | 0.0296 | 0.9978 | - |
1.5106 | 3500 | 0.0613 | 0.0279 | 0.9985 | - |
1.6185 | 3750 | 0.0607 | 0.0264 | 0.9983 | - |
1.7264 | 4000 | 0.0521 | 0.0238 | 0.9985 | - |
1.8343 | 4250 | 0.0495 | 0.0216 | 0.9985 | - |
1.9422 | 4500 | 0.0425 | 0.0211 | 0.9983 | - |
2.0501 | 4750 | 0.0428 | 0.0200 | 0.9983 | - |
2.1580 | 5000 | 0.0435 | 0.0190 | 0.9985 | - |
2.2659 | 5250 | 0.0393 | 0.0188 | 0.9983 | - |
2.3738 | 5500 | 0.0356 | 0.0182 | 0.9983 | - |
2.4817 | 5750 | 0.0351 | 0.0180 | 0.9988 | - |
2.5896 | 6000 | 0.0394 | 0.0181 | 0.9985 | - |
2.5973 | 6018 | - | - | - | 0.9973 |
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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Model tree for enpaiva/embed-andegpt-H384
Base model
nreimers/MiniLM-L6-H384-uncased
Finetuned
this model
Evaluation results
- Cosine Accuracy on andegpt devself-reported0.998
- Dot Accuracy on andegpt devself-reported0.002
- Manhattan Accuracy on andegpt devself-reported0.999
- Euclidean Accuracy on andegpt devself-reported0.998
- Max Accuracy on andegpt devself-reported0.999
- Cosine Accuracy on andegpt testself-reported0.997
- Dot Accuracy on andegpt testself-reported0.002
- Manhattan Accuracy on andegpt testself-reported0.997
- Euclidean Accuracy on andegpt testself-reported0.997
- Max Accuracy on andegpt testself-reported0.997