Add new SentenceTransformer model.

.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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

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

@@ -0,0 +1,349 @@
+---
+base_model: saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:100
+- loss:SoftmaxLoss
+widget:
+- source_sentence: ilustracion,diseño ux uidiseño ux
+  sentences:
+  - Create AI-generated art using NightCafe. Apply styles and techniques to customize
+    artwork  .  Produce a digital art portfolio showcasing AI creativity
+  - The nature of discrete-time signals. Discrete-time signals are vectors in a vector
+    space. Discrete-time signals can be analyzed in the frequency domain via the Fourier
+    transform
+  - Describe software engineering, Software Development Lifecycle (SDLC), and software
+    development tools, technologies and stacks.  . List different types of programming
+    languages and create basic programming constructs such as loops and conditions
+    using Python.   . Outline approaches to application architecture and design, patterns,
+    and deployment architectures.   . Summarize the skills required in software engineering
+    and describe the career options it provides.
+- source_sentence: profesional
+  sentences:
+  - Create a  Facebook Prophet Machine learning model & Forecast the price of Bitcoin
+    for the future 30 days. Learn to Visualize Bitcoin using Plotly Express. Learn
+    to Extract Financial Data and Analyze it using Google Sheets
+  - What Industry 4.0 is and what factors have enabled the IIoT.. Key skills to develop
+    to be employed in the IIoT space.. What platforms are, and also market information
+    on Software and Services.. What the top application areas are (examples include
+    manufacturing and oil & gas).
+  - Writing
+- source_sentence: ilustracion,diseño ux uidiseño ux
+  sentences:
+  - Creativity, Problem Solving, Writing
+  - Anatomy of the Upper and Lower Extremities
+  - Evaluate the performance of a classifier using visual diagnostic tools from Yellowbrick.
+    Diagnose and handle class imbalance problems
+- source_sentence: Maestría en educación
+  sentences:
+  - Explain the seminal ideas leading to the birth of AI, the major difficulties and
+    how the international community overtook them.. Describe what AI is today in terms
+    of goals, scientific community, companies’ interests. Describe the taxonomy of
+    the know-how on AI in terms of techniques, software and hardware methodologies.
+    . Explain the need for national strategies on AI and identify the major Italian
+    and European players on AI
+  - How a hardware component can be adapted at runtime to better respond to users/environment
+    needs using FPGAs
+  - A framework to evaluate DeFi risk; Environmental implications of cryptocurrency;
+    and winners and losers in the future of finance.
+- source_sentence: ilustracion,diseño ux uidiseño ux
+  sentences:
+  - Planning
+  - 1.     Transform numbers between number bases and perform arithmetic in number
+    bases . 2. Identify, describe and compute sequences of numbers and their sums.
+    . 3. Represent and describe space numerically using coordinates and graphs.. 4.
+    Study, represent and describe variations of quantities via functions and their
+    graphs.
+  - Create PivotTables to assess specific relationships within the data.. Create line,
+    bar, and pie charts to present the information from the PivotTables.. Compose
+    a dashboard with the charts and tables created to present a global picture of
+    the data.
+---
+
+# SentenceTransformer based on saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-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:** [saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision a73bbb48c69aae3d4ddfec208a2b666c7f5978c8 -->
+- **Maximum Sequence Length:** 128 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': 128, '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("saraleivam/GURU-train-paraphrase-multilingual-MiniLM-L12-v2")
+# Run inference
+sentences = [
+    'ilustracion,diseño ux uidiseño ux',
+    'Create PivotTables to assess specific relationships within the data.. Create line, bar, and pie charts to present the information from the PivotTables.. Compose a dashboard with the charts and tables created to present a global picture of the data.',
+    '1.     Transform numbers between number bases and perform arithmetic in number bases . 2. Identify, describe and compute sequences of numbers and their sums. . 3. Represent and describe space numerically using coordinates and graphs.. 4. Study, represent and describe variations of quantities via functions and their graphs.',
+]
+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: 100 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                          | label                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | int                                                                |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 12.47 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 46.48 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~39.00%</li><li>1: ~24.00%</li><li>2: ~37.00%</li></ul> |
+* Samples:
+  | sentence1                                         | sentence2                                           | label          |
+  |:--------------------------------------------------|:----------------------------------------------------|:---------------|
+  | <code>ilustracion,diseño ux ui, sdiseño ux</code> | <code>The Ancient Greeks</code>                     | <code>2</code> |
+  | <code>Marketing digital, Bachiller</code>         | <code>The Modern and the Postmodern (Part 1)</code> | <code>2</code> |
+  | <code>profesional</code>                          | <code>Writing</code>                                | <code>1</code> |
+* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
+
+### Training Hyperparameters
+
+#### 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`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 1.0
+- `num_train_epochs`: 3.0
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers and SoftmaxLoss
+```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",
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "saraleivam/GURU2-paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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modules.json

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

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "normalized": false,
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+}

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tokenizer_config.json

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+  "cls_token": "<s>",
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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

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