Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-1392/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,62 @@
+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: 'search_query: i love autotrain'
+  sentences:
+  - 'search_query: huggingface auto train'
+  - 'search_query: hugging face auto train'
+  - 'search_query: i love autotrain'
+pipeline_tag: sentence-similarity
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.13348257541656494
+
+runtime: 337.2752
+
+samples_per_second: 2.751
+
+steps_per_second: 0.172
+
+: 3.0
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the Hugging Face Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'search_query: autotrain',
+    'search_query: auto train',
+    'search_query: i love autotrain',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+```

checkpoint-1392/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-1392/README.md

@@ -0,0 +1,449 @@
+---
+base_model: BAAI/bge-m3
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3709
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Quins són els tipus de vehicles que poden accedir a les àrees de
+    vianants?
+  sentences:
+  - Perquè la lesió sigui conseqüència de força major o danys que el particular hagi
+    de suportar de conformitat amb la Llei.
+  - No es especifica
+  - Un període superior a dos mesos.
+- source_sentence: Quin és el benefici de la cultura per a la ciutat de Sitges?
+  sentences:
+  - Contribueix a la seva identitat i al seu desenvolupament social i econòmic
+  - Tramitar la sol·licitud i renovar el carnet de persona cuidadora.
+  - Cobrir necessitats bàsiques de la ciutadania.
+- source_sentence: Quin és l'objectiu de l'ajut per a la creació de noves empreses?
+  sentences:
+  - La creació de noves empreses.
+  - Acreditar les determinacions i previsions urbanístiques aplicables a una o unes
+    finques concretes.
+  - El paper és ajudar les persones amb dificultats econòmiques a pagar el lloguer
+    just dels habitatges.
+- source_sentence: Quin és el paper de les administracions públiques en les reclamacions
+    per responsabilitat patrimonial?
+  sentences:
+  - Reforçar les activitats econòmiques amb suspensió o limitació d’obertura al públic
+    i finançar les despeses de lloguer o hipoteca per empreses i/o establiments comercials.
+  - Indemnitzar les persones per les lesions que pateixin en els seus béns i drets.
+  - Persones grans que necessiten organització, supervisió i assistència en les activitats
+    de la seva vida diària.
+- source_sentence: Quin és el període de durada de les activitats de l'Estiu Jove?
+  sentences:
+  - Determinar l’interès de la proposta.
+  - Per als efectes de la taxa pel servei municipal complementari de recollida, tractament
+    i eliminació de residus comercials.
+  - Els matins de juliol.
+---
+
+# SentenceTransformer based on BAAI/bge-m3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    "Quin és el període de durada de les activitats de l'Estiu Jove?",
+    'Els matins de juliol.',
+    'Determinar l’interès de la proposta.',
+]
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 3,709 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | answer                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 21.31 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.37 tokens</li><li>max: 70 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                      | answer                                                                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
+  | <code>Quin és el paper de la colònia felina en la promoció de la consciència sobre els animals?</code>                                     | <code>Educació la societat sobre la importància del tracte ètic i responsable cap als animals.</code> |
+  | <code>Quin és el requisit per a obtenir la llicència per a la tinença i/o conducció d'animals considerats potencialment perillosos?</code> | <code>La llicència atorgada per l'Ajuntament.</code>                                                  |
+  | <code>Quin és el benefici principal que es busca amb la Inspecció Tècnica dels Edificis (ITE)?</code>                                      | <code>Millorar la seguretat i la salut dels usuaris.</code>                                           |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 928 evaluation samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | answer                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 21.12 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.75 tokens</li><li>max: 87 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                  | answer                                                                                                                                                                                                                                        |
+  |:---------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Quin és el període de temps en què es pot rebre l'ajuda?</code>                                                                  | <code>Sense període de carència.</code>                                                                                                                                                                                                       |
+  | <code>Quin és el període per presentar al·legacions i documents en un procés de selecció de personal de l'Ajuntament de Sitges?</code> | <code>En qualsevol moment del procediment anterior al tràmit d'audiència i especialment en el període establert per a la presentació d'al·legacions a partir de la publicació de la llista provisional de persones admeses i excloses.</code> |
+  | <code>Quin és el nombre de persones que pot acollir la sala d'actes del Casal Municipal de la Gent Gran de Sitges?</code>              | <code>125 persones</code>                                                                                                                                                                                                                     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 3e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `ddp_find_unused_parameters`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `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`: 3e-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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `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`: True
+- `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`: 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}
+- `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`: False
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   |
+|:------:|:----:|:-------------:|:------:|
+| 0.0539 | 25   | 1.0035        | -      |
+| 0.1078 | 50   | 0.6668        | -      |
+| 0.1616 | 75   | 0.5274        | -      |
+| 0.2155 | 100  | 0.5162        | -      |
+| 0.2694 | 125  | 0.3716        | -      |
+| 0.3233 | 150  | 0.3866        | -      |
+| 0.3772 | 175  | 0.3964        | -      |
+| 0.4310 | 200  | 0.3294        | -      |
+| 0.4849 | 225  | 0.321         | -      |
+| 0.5388 | 250  | 0.2215        | -      |
+| 0.5927 | 275  | 0.2209        | -      |
+| 0.6466 | 300  | 0.3976        | -      |
+| 0.7004 | 325  | 0.2338        | -      |
+| 0.7543 | 350  | 0.2957        | -      |
+| 0.8082 | 375  | 0.3162        | -      |
+| 0.8621 | 400  | 0.3654        | -      |
+| 0.9159 | 425  | 0.2917        | -      |
+| 0.9698 | 450  | 0.2876        | -      |
+| 1.0    | 464  | -             | 0.2267 |
+| 1.0237 | 475  | 0.1953        | -      |
+| 1.0776 | 500  | 0.1227        | -      |
+| 1.1315 | 525  | 0.1753        | -      |
+| 1.1853 | 550  | 0.1713        | -      |
+| 1.2392 | 575  | 0.1333        | -      |
+| 1.2931 | 600  | 0.0951        | -      |
+| 1.3470 | 625  | 0.2296        | -      |
+| 1.4009 | 650  | 0.1954        | -      |
+| 1.4547 | 675  | 0.0941        | -      |
+| 1.5086 | 700  | 0.1103        | -      |
+| 1.5625 | 725  | 0.1549        | -      |
+| 1.6164 | 750  | 0.18          | -      |
+| 1.6703 | 775  | 0.1055        | -      |
+| 1.7241 | 800  | 0.1356        | -      |
+| 1.7780 | 825  | 0.1827        | -      |
+| 1.8319 | 850  | 0.0908        | -      |
+| 1.8858 | 875  | 0.0799        | -      |
+| 1.9397 | 900  | 0.0776        | -      |
+| 1.9935 | 925  | 0.1043        | -      |
+| 2.0    | 928  | -             | 0.1602 |
+| 2.0474 | 950  | 0.0742        | -      |
+| 2.1013 | 975  | 0.1111        | -      |
+| 2.1552 | 1000 | 0.0452        | -      |
+| 2.2091 | 1025 | 0.0684        | -      |
+| 2.2629 | 1050 | 0.0867        | -      |
+| 2.3168 | 1075 | 0.0784        | -      |
+| 2.3707 | 1100 | 0.056         | -      |
+| 2.4246 | 1125 | 0.0767        | -      |
+| 2.4784 | 1150 | 0.0297        | -      |
+| 2.5323 | 1175 | 0.0753        | -      |
+| 2.5862 | 1200 | 0.083         | -      |
+| 2.6401 | 1225 | 0.0347        | -      |
+| 2.6940 | 1250 | 0.0912        | -      |
+| 2.7478 | 1275 | 0.0392        | -      |
+| 2.8017 | 1300 | 0.0488        | -      |
+| 2.8556 | 1325 | 0.0611        | -      |
+| 2.9095 | 1350 | 0.0891        | -      |
+| 2.9634 | 1375 | 0.0841        | -      |
+| 3.0    | 1392 | -             | 0.1335 |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.43.1
+- PyTorch: 2.3.0
+- Accelerate: 0.32.0
+- Datasets: 2.19.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@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
+```bibtex
+@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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-1392/config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "BAAI/bge-m3",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.43.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

checkpoint-1392/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.43.1",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

checkpoint-1392/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0d969c6c5db03b5da3119fb4be71f0cb8e3d9bbc5c669113437f0c2446fb2a6
+size 2271064456

checkpoint-1392/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
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