metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:46957
  - loss:TripletLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: How to load documents?
    sentences:
      - >-
        MapCity contains the geometries that are displayed on the interactive
        map on the frontend.
      - >-
        The maps app contains State, Region, Province, Company, City,
        Particella, Map, MapCity, Property, Group, PropertyHasMap, Palette,
        PaletteColor, and Geotiff models.
      - >-
        Use the load_documents command which creates document file instances
        from folders in ./files/2-Database-solare path.
  - source_sentence: What is the MapCity model?
    sentences:
      - >-
        The document app contains Document, DocumentFile, Type, Language, Theme,
        Keyword, and Oss models used in the document consultation section.
      - >-
        Document contains all the document metadata such as name, author, year,
        type, language used in the document consultation section.
      - >-
        MapCity contains the geometries that are displayed on the interactive
        map on the frontend.
  - source_sentence: What is the cleantables command?
    sentences:
      - >-
        Takes care of eliminating all instances of Palette, Group, MapCity, Map,
        Province, and Property models.
      - >-
        Set CORS_ALLOWED_ORIGINS in the environment file with allowed origins
        like localhost,127.0.0.1,http://localhost:3000.
      - |-
        from matplotlib import pyplot as plt

        colors = ['Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r',]
        ax = res_union. plot(cmap=colors[random. randint(0, len(colors))])
        ax = res_union. plot(cmap='Greens_r')
        gdf1. plot(ax=ax, facecolor='none', edgecolor='k')
        gdf2. plot(ax=ax, facecolor='none', edgecolor='k')
        plt. savefig("overlay. png")
        ```.
  - source_sentence: How to restore a database dump?
    sentences:
      - >-
        Use the generategeotiff command which generates Geotiff instances from a
        shapefile. Run with python manage.py generategeotiff <path>.
      - >-
        Use the generategeotiff command which generates Geotiff instances from a
        shapefile. Run with python manage.py generategeotiff <path>.
      - >-
        Copy the dump file to data/postgresql folder, then inside the database
        container run pg_restore -U $POSTGRES_USER -d $POSTGRES_DB --clean
        --if-exists /var/lib/postgresql/data/db_backup.dump
  - source_sentence: What is the State model?
    sentences:
      - >-
        State contains the geometries of the states, in our specific case it
        contains only the entire geometries of the Italian state.
      - >-
        Use the load_documents command which creates document file instances
        from folders in ./files/2-Database-solare path.
      - >-
        The maps app contains State, Region, Province, Company, City,
        Particella, Map, MapCity, Property, Group, PropertyHasMap, Palette,
        PaletteColor, and Geotiff models.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: val triplet eval
          type: val-triplet-eval
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, '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})
  (2): 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("gabrielegabellone/all-mini-mediterraneo-triplets-v4")
# Run inference
sentences = [
    'What is the State model?',
    'State contains the geometries of the states, in our specific case it contains only the entire geometries of the Italian state.',
    'The maps app contains State, Region, Province, Company, City, Particella, Map, MapCity, Property, Group, PropertyHasMap, Palette, PaletteColor, and Geotiff models.',
]
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: val-triplet-eval
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 46,957 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 7 tokens mean: 9.62 tokens max: 15 tokens	min: 21 tokens mean: 35.4 tokens max: 68 tokens	min: 21 tokens mean: 73.85 tokens max: 239 tokens

Samples:

sentence_0	sentence_1	sentence_2
`How to restore a database dump?`	`Copy the dump file to data/postgresql folder, then inside the database container run pg_restore -U $POSTGRES_USER -d $POSTGRES_DB --clean --if-exists /var/lib/postgresql/data/db_backup.dump`	filter(id__in=ids_dataframe1) ids_dataframe2 = df2. split(',') maps = Map. objects. filter(id__in=ids_dataframe2) if not provinces or not maps: return Response('Provinces or maps not found', status=status. HTTP_404_NOT_FOUND) ``` 2. Then we use the **Geodataframe.
`What is the Region model?`	`Region contains the geometries of the regions, in our specific case it only contains the geometries of the Italian regions.`	The command allows loading data into the project based on a compiled excel file. Allows loading data on scenarios, shapefiles, palettes, software, particles and companies. 1. Run the command:: bash<br> python manage. py flow<br> 2. Choose the type of data to load: ``` Executing consistency checks. Load scenarios data. (y/n): n Load softwares data.
`What is the generategeotiff command?`	`This command generates Geotiff instances from a shapefile. For each Property present in the shapefile, a Geotiff instance will be created.`	MINIO_ROOT_USER=minio12345 MINIO_ROOT_PASSWORD=minio12345 MINIO_ENDPOINT=minio:9000 MINIO_EXTERNAL_ENDPOINT=localhost:9000 #CDN MINIO_USE_HTTPS=False MINIO_EXTERNAL_ENDPOINT_USE_HTTPS=False #true online PGADMIN_DEFAULT_EMAIL=admin@admin. com PGADMIN_DEFAULT_PASSWORD=strongpassword VERSION=1. 0. 0 SHAPEFILE_VERSION=gadm41_ITA_ ```.

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: False
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	val-triplet-eval_cosine_accuracy
0.1704	500	4.5287	-
0.3407	1000	4.1121	1.0
0.5111	1500	3.7883	-
0.6814	2000	3.6668	1.0
0.8518	2500	3.6262	-
1.0	2935	-	1.0
1.0221	3000	3.586	1.0
1.1925	3500	3.5752	-
1.3629	4000	3.5576	1.0
1.5332	4500	3.556	-
1.7036	5000	3.5389	1.0
1.8739	5500	3.526	-
2.0	5870	-	1.0
2.0443	6000	3.5228	1.0
2.2147	6500	3.5234	-
2.3850	7000	3.5122	1.0
2.5554	7500	3.517	-
2.7257	8000	3.5056	1.0
2.8961	8500	3.5103	-
3.0	8805	-	1.0
3.0664	9000	3.5071	1.0
3.2368	9500	3.4977	-
3.4072	10000	3.4929	1.0
3.5775	10500	3.4964	-
3.7479	11000	3.4914	1.0
3.9182	11500	3.491	-
4.0	11740	-	1.0

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.53.2
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 4.0.0
Tokenizers: 0.21.2

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",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}