Lampistero

This is a sentence-transformers model finetuned from jinaai/jina-embeddings-v3 on the json dataset. 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: jinaai/jina-embeddings-v3
Maximum Sequence Length: 8194 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
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(
  (transformer): Transformer(
    (auto_model): XLMRobertaLoRA(
      (roberta): XLMRobertaModel(
        (embeddings): XLMRobertaEmbeddings(
          (word_embeddings): ParametrizedEmbedding(
            250002, 1024, padding_idx=1
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (token_type_embeddings): ParametrizedEmbedding(
            1, 1024
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
        )
        (emb_drop): Dropout(p=0.1, inplace=False)
        (emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder): XLMRobertaEncoder(
          (layers): ModuleList(
            (0-23): 24 x Block(
              (mixer): MHA(
                (rotary_emb): RotaryEmbedding()
                (Wqkv): ParametrizedLinearResidual(
                  in_features=1024, out_features=3072, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (inner_attn): FlashSelfAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (inner_cross_attn): FlashCrossAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (out_proj): ParametrizedLinear(
                  in_features=1024, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout1): Dropout(p=0.1, inplace=False)
              (drop_path1): StochasticDepth(p=0.0, mode=row)
              (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (mlp): Mlp(
                (fc1): ParametrizedLinear(
                  in_features=1024, out_features=4096, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (fc2): ParametrizedLinear(
                  in_features=4096, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout2): Dropout(p=0.1, inplace=False)
              (drop_path2): StochasticDepth(p=0.0, mode=row)
              (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            )
          )
        )
        (pooler): XLMRobertaPooler(
          (dense): ParametrizedLinear(
            in_features=1024, out_features=1024, bias=True
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (activation): Tanh()
        )
      )
    )
  )
  (pooler): 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})
  (normalizer): 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("csanz91/lampistero_rag_embeddings_2")
# Run inference
sentences = [
    "¿En qué año se demarcó y reconoció la mina 'El Pilar'?",
    "La mina 'El Pilar' se demarcó y reconoció en 1857.",
    'Según la quinta demanda del SOMM, todas compañías mineras debían entregar a todos sus obreros un libramiento de liquidación mensual',
]
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

Information Retrieval

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 1024
}
```

Metric	Value
cosine_accuracy@1	0.7701
cosine_accuracy@3	0.8926
cosine_accuracy@5	0.9155
cosine_accuracy@10	0.933
cosine_precision@1	0.7701
cosine_precision@3	0.2975
cosine_precision@5	0.1831
cosine_precision@10	0.0933
cosine_recall@1	0.7701
cosine_recall@3	0.8926
cosine_recall@5	0.9155
cosine_recall@10	0.933
cosine_ndcg@10	0.8579
cosine_mrr@10	0.8331
cosine_map@100	0.8343

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.7695
cosine_accuracy@3	0.889
cosine_accuracy@5	0.9125
cosine_accuracy@10	0.933
cosine_precision@1	0.7695
cosine_precision@3	0.2963
cosine_precision@5	0.1825
cosine_precision@10	0.0933
cosine_recall@1	0.7695
cosine_recall@3	0.889
cosine_recall@5	0.9125
cosine_recall@10	0.933
cosine_ndcg@10	0.8571
cosine_mrr@10	0.8321
cosine_map@100	0.8333

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.7683
cosine_accuracy@3	0.8865
cosine_accuracy@5	0.9113
cosine_accuracy@10	0.9306
cosine_precision@1	0.7683
cosine_precision@3	0.2955
cosine_precision@5	0.1823
cosine_precision@10	0.0931
cosine_recall@1	0.7683
cosine_recall@3	0.8865
cosine_recall@5	0.9113
cosine_recall@10	0.9306
cosine_ndcg@10	0.8555
cosine_mrr@10	0.8307
cosine_map@100	0.8321

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.764
cosine_accuracy@3	0.8902
cosine_accuracy@5	0.9083
cosine_accuracy@10	0.93
cosine_precision@1	0.764
cosine_precision@3	0.2967
cosine_precision@5	0.1817
cosine_precision@10	0.093
cosine_recall@1	0.764
cosine_recall@3	0.8902
cosine_recall@5	0.9083
cosine_recall@10	0.93
cosine_ndcg@10	0.8535
cosine_mrr@10	0.8283
cosine_map@100	0.8296

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.7447
cosine_accuracy@3	0.8769
cosine_accuracy@5	0.9028
cosine_accuracy@10	0.9215
cosine_precision@1	0.7447
cosine_precision@3	0.2923
cosine_precision@5	0.1806
cosine_precision@10	0.0922
cosine_recall@1	0.7447
cosine_recall@3	0.8769
cosine_recall@5	0.9028
cosine_recall@10	0.9215
cosine_ndcg@10	0.8403
cosine_mrr@10	0.8134
cosine_map@100	0.8149

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.7103
cosine_accuracy@3	0.8491
cosine_accuracy@5	0.8781
cosine_accuracy@10	0.8998
cosine_precision@1	0.7103
cosine_precision@3	0.283
cosine_precision@5	0.1756
cosine_precision@10	0.09
cosine_recall@1	0.7103
cosine_recall@3	0.8491
cosine_recall@5	0.8781
cosine_recall@10	0.8998
cosine_ndcg@10	0.8119
cosine_mrr@10	0.7829
cosine_map@100	0.7851

Training Details

Training Dataset

json

Dataset: json
Size: 14,907 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 9 tokens
mean: 26.09 tokens
max: 66 tokens

min: 4 tokens
mean: 34.02 tokens
max: 405 tokens

	query	answer
type	string	string
details	min: 9 tokens mean: 26.09 tokens max: 66 tokens	min: 4 tokens mean: 34.02 tokens max: 405 tokens

Samples:

query	answer
`¿Qué tipos de palas se utilizan para cargar el carbón y el mineral?`	`Se utiliza una pala convencional y una pala hidráulica, esta última descarga sobre un páncer, puede hacerlo lateralmente y se desplaza sobre ruedas u oruga.`
`Tras el cierre de la tejería de Florencio Salvador, ¿de dónde procedieron finalmente los ladrillos para las doscientas diez viviendas construidas en Utrillas?`	`Los ladrillos y material para las doscientas diez viviendas construidas en Utrillas procedieron finalmente de Letux, Zaragoza .`
`¿Cuál es el formato de los juegos infantiles que se están preparando para el verano en Escucha en 2021?`	`Los juegos infantiles que se están preparando para el verano en Escucha en 2021 están en formato revista.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
per_device_eval_batch_size: 16
gradient_accumulation_steps: 32
learning_rate: 2e-05
num_train_epochs: 8
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 32
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 8
max_steps: -1
lr_scheduler_type: cosine
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: True
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}
tp_size: 0
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_fused
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
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
1.0	8	-	0.7841	0.7835	0.7836	0.7791	0.7665	0.7226
1.2747	10	58.1187	-	-	-	-	-	-
2.0	16	-	0.8348	0.8366	0.8345	0.8301	0.8184	0.7861
2.5494	20	24.4181	-	-	-	-	-	-
3.0	24	-	0.8521	0.8504	0.8503	0.8457	0.8319	0.8007
3.8240	30	16.1488	-	-	-	-	-	-
4.0	32	-	0.8561	0.8548	0.8555	0.8509	0.8387	0.8073
5.0	40	13.4897	0.8585	0.8556	0.8545	0.8528	0.8397	0.8111
6.0	48	-	0.8578	0.8563	0.8550	0.8535	0.8410	0.8110
6.2747	50	13.7469	-	-	-	-	-	-
7.0	56	-	0.8579	0.8571	0.8555	0.8535	0.8403	0.8119

Framework Versions

Python: 3.12.10
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}

csanz91
/

lampistero_rag_embeddings_2

Lampistero

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

json

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for csanz91/lampistero_rag_embeddings_2

Evaluation results