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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -1,3 +1,384 @@
----
-license: apache-2.0
----
+---
+base_model: Supabase/gte-small
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:68
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Rollito de primavera de verdura
+  sentences:
+  - 'Vinos blancos con cuerpo, amplios y sabrosos. En boca potentes, untuosos y densos
+    fruto del paso por barrica. Vinos blancos con intensidad aromática alta y con
+    aromas a manzana Golden, mantequilla, pan tostado, vainilla, frutos secos. Ejemplo:
+    Chardonnay, Garnacha blanca, Viura de Rioja.'
+  - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco
+    denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde,
+    melocotón, piña e hinojo.
+  - Vino blanco joven con buena acidez o un vino rosado afrutado.
+- source_sentence: Pollo al curri rojo
+  sentences:
+  - 'Vino blanco afrutado de medio cuerpo, con aromas a melocotón, piña, uva, fruta
+    de la pasión, queroseno y flores. Ejemplos de variedades: los Verdejo de Rueda,
+    Valencia Moscatell, los Malvasía de Canarias, los Riesling de Alsacia i Alemania.
+    los Gerwüztraminer.'
+  - 'Vino blanco afrutado de medio cuerpo, con aromas a melocotón, piña, uva, fruta
+    de la pasión, queroseno y flores. Ejemplos de variedades: los Verdejo de Rueda,
+    Valencia Moscatell, los Malvasía de Canarias, los Riesling de Alsacia i Alemania.
+    los Gerwüztraminer.'
+  - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco
+    denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde,
+    melocotón, piña e hinojo. O también vinos rosados ligeros, referescantes, delicados
+    y de color pálido. En boca son ligeros y de sabor delicado. Con aromas a fruta
+    roja silvestre, cítricos y herbáceos.
+- source_sentence: Carnes blancas
+  sentences:
+  - ' Vinos blancos con cuerpo, amplios y sabrosos. En boca potentes, untuosos y densos
+    fruto del paso por barrica. Vinos blancos con intensidad aromática alta y con
+    aromas a manzana Golden, mantequilla, pan tostado, vainilla, frutos secos. Ejemplo:
+    Chardonnay, Garnacha blanca, Viura de Rioja. O también, Vinos rosados envejecidos
+    en barrica, y también los vinos elaborados a partir de Cabernet, Merlot o Syrah.
+    Vinos rosados redondos, afrutados, de color intenso y sabor potente y sabroso.
+    Con maceración de la piel.'
+  - Vino blanco con buena acidez y notas florales.
+  - ' Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y
+    poco denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde,
+    melocotón, piña e hinojo.'
+- source_sentence: Chocolates o postres de cacao o café
+  sentences:
+  - 'Vinos tintos afrutados, jugosos y desenfadados. Con aromas a frutos rojos, lácticos.
+    pimienta, ciruela y mermeladas. Son vinos sencillos y amables, golosos y frescos
+    a partes iguales. '
+  - pedro ximenez, vinos de jerez, O también Vino tinto con mucha intensidad y potencia,
+    con notas a fruta tinta madura, notas a madera, notas a pimienta negra, a café,
+    cacao
+  - Vino blanco con buena acidez y notas de fruta blanca.
+- source_sentence: Burger crujiente de zanahoria
+  sentences:
+  - Vino blanco joven con buena acidez o un vino rosado afrutado.
+  - ' Vino tinto con mucha intensidad y potencia, con notas a fruta tinta madura,
+    notas a madera, notas a pimienta negra, a café, cacao. Con presencia de taninos
+    bien integrados fruto del contacto con las pieles durante un largo período. Son
+    sabrosos, corpulentos, impactantes. Rioja o Ribera del Duero, Bordeus, Ródano '
+  - 'Vinos tintos ligeros con mucha acidez y poco volumen en boca, con notas de fruta
+    roja muy fresca, sin presencia de taninos; normalmente con notas verdes. ejemplos:
+    mencia, gammay, pinot noir'
+---
+
+# SentenceTransformer based on Supabase/gte-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Supabase/gte-small](https://huggingface.co/Supabase/gte-small). 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:** [Supabase/gte-small](https://huggingface.co/Supabase/gte-small) <!-- at revision 93b36ff09519291b77d6000d2e86bd8565378086 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 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': 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:
+
+```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 = [
+    'Burger crujiente de zanahoria',
+    'Vino blanco joven con buena acidez o un vino rosado afrutado.',
+    'Vinos tintos ligeros con mucha acidez y poco volumen en boca, con notas de fruta roja muy fresca, sin presencia de taninos; normalmente con notas verdes. ejemplos: mencia, gammay, pinot noir',
+]
+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]
+```
+
+<!--
+### 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: 68 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 16.46 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 64.46 tokens</li><li>max: 178 tokens</li></ul> |
+* Samples:
+  | sentence_0                                         | sentence_1                                                                                                                                                                                             |
+  |:---------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Nuggets de pollo rebozados en tempura</code> | <code>Vino blanco joven con buena acidez o un vino rosado afrutado.</code>                                                                                                                             |
+  | <code>Paellas de mar, paella marinera.</code>      | <code> Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, melocotón, piña e hinojo.</code> |
+  | <code>Fideos de arroz con pollo</code>             | <code>Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, melocotón, piña e hinojo.</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
+
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 30
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 30
+- `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`: 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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch   | Step | Training Loss |
+|:-------:|:----:|:-------------:|
+| 29.4118 | 500  | 0.2567        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- 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.*
+-->

config.json

@@ -1,170 +1,15 @@
 {
-  "_name_or_path": "/content/model/",
+  "_name_or_path": "Supabase/gte-small",
   "architectures": [
-    "BertForSequenceClassification"
+    "BertModel"
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
-  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5",
-    "6": "LABEL_6",
-    "7": "LABEL_7",
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-  },
+  "hidden_size": 384,
   "initializer_range": 0.02,
-  "intermediate_size": 3072,
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-  },
+  "intermediate_size": 1536,
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
   "model_type": "bert",
@@ -172,9 +17,8 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.44.0",
+  "transformers_version": "4.42.4",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.4",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5bd9b4a97244eb84bf01d3a64ae4e1412e05664406e342532220818d14999784
-size 438183196
+oid sha256:2610323da0dc0a62abc91c9ec5403938d54bb29a260a0fb90a30361eb2b2a401
+size 133462128

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -1,7 +1,37 @@
 {
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }

tokenizer_config.json

@@ -46,12 +46,19 @@
   "do_basic_tokenize": true,
   "do_lower_case": true,
   "mask_token": "[MASK]",
+  "max_length": 128,
   "model_max_length": 512,
   "never_split": null,
+  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "sep_token": "[SEP]",
+  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }