Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ 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

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+ "word_embedding_dimension": 768,
+ "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 +1,326 @@
+---
+base_model: Alibaba-NLP/gte-multilingual-base
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2000
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: انقلاب صنعتی چگونه جامعه را تغییر داد؟
+ sentences:
+ - انقلاب صنعتی منجر به شهرنشینی شد زیرا مردم به شهرها نقل مکان کردند تا در کارخانه‌ها
+ کار کنند.
+ - انقلاب صنعتی منجر به شهرنشینی شد زیرا مردم به شهرها نقل مکان کردند تا در کارخانه‌ها
+ کار کنند.
+ - انقلاب صنعتی چگونه جامعه را تغییر داد؟
+- source_sentence: الکترون‌ها چه نقشی در پیوندهای شیمیایی ایفا می‌کنند؟
+ sentences:
+ - الکترون‌ها چه نقشی در پیوندهای شیمیایی ایفا می‌کنند؟
+ - پروتون‌ها در هسته بر هویت و ویژگی‌های اتم تأثیر می‌گذارند که به طور غیرمستقیم
+ بر نحوه‌ی تشکیل پیوندهای شیمیایی تأثیر دارد.
+ - پروتون‌ها در هسته بر هویت و ویژگی‌های اتم تأثیر می‌گذارند که به طور غیرمستقیم
+ بر نحوه‌ی تشکیل پیوندهای شیمیایی تأثیر دارد.
+- source_sentence: عملکردهای اصلی سیستم ایمنی انسان چیست؟
+ sentences:
+ - آنتی بادی‌ها توسط سیستم ایمنی برای کمک به خنثی کردن پاتوژن‌های ایجادکننده عفونت
+ تولید می‌شوند.
+ - آنتی بادی‌ها توسط سیستم ایمنی برای کمک به خنثی کردن پاتوژن‌های ایجادکننده عفونت
+ تولید می‌شوند.
+ - عملکردهای اصلی سیستم ایمنی انسان چیست؟
+- source_sentence: ماهیت بنیادی واقعیت چیست؟
+ sentences:
+ - مفهوم واقعیت شامل درک جهان فیزیکی و قوانین طبیعتی است که بر آن حاکم هستند.
+ - مفهوم واقعیت شامل درک جهان فیزیکی و قوانین طبیعتی است که بر آن حاکم هستند.
+ - ماهیت بنیادی واقعیت چیست؟
+- source_sentence: شخصیت‌های اصلی در جنبش کوبیسم چه کسانی بودند؟
+ sentences:
+ - لئوناردو داوینچی به خاطر مشارکت‌هایش در رنسانس شناخته می‌شود، نه کوبیسم.
+ - لئوناردو داوینچی به خاطر مشارکت‌هایش در رنسانس شناخته می‌شود، نه کوبیسم.
+ - شخصیت‌های اصلی در جنبش کوبیسم چه کسانی بودند؟
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 7fc06782350c1a83f88b15dd4b38ef853d3b8503 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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: NewModel 
+ (1): Pooling({'word_embedding_dimension': 768, '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("xmanii/maux-gte-persian")
+# Run inference
+sentences = [
+ 'شخصیت\u200cهای اصلی در جنبش کوبیسم چه کسانی بودند؟',
+ 'لئوناردو داوینچی به خاطر مشارکت\u200cهایش در رنسانس شناخته می\u200cشود، نه کوبیسم.',
+ 'شخصیت\u200cهای اصلی در جنبش کوبیسم چه کسانی بودند؟',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 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`: 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`: 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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.8772 | 50 | 0.0584 | - |
+| 1.7544 | 100 | 0.0342 | 0.0379 |
+| 2.6316 | 150 | 0.0277 | - |
+
+
+### Framework Versions
+- Python: 3.10.8
+- Sentence Transformers: 3.2.0
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.1
+
+## 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",
+}
+```
+
+<!--
+## 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

@@ -0,0 +1,50 @@
+{
+ "_name_or_path": "Alibaba-NLP/gte-multilingual-base",
+ "architectures": [
+ "NewModel"
+ ],
+ "attention_probs_dropout_prob": 0.0,
+ "auto_map": {
+ "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
+ "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+ "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+ "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+ "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+ "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+ "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+ },
+ "classifier_dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "id2label": {
+ "0": "LABEL_0"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "label2id": {
+ "LABEL_0": 0
+ },
+ "layer_norm_eps": 1e-12,
+ "layer_norm_type": "layer_norm",
+ "logn_attention_clip1": false,
+ "logn_attention_scale": false,
+ "max_position_embeddings": 8192,
+ "model_type": "new",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pack_qkv": true,
+ "pad_token_id": 1,
+ "position_embedding_type": "rope",
+ "rope_scaling": {
+ "factor": 8.0,
+ "type": "ntk"
+ },
+ "rope_theta": 20000,
+ "torch_dtype": "float32",
+ "transformers_version": "4.45.2",
+ "type_vocab_size": 1,
+ "unpad_inputs": false,
+ "use_memory_efficient_attention": false,
+ "vocab_size": 250048
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+ "__version__": {
+ "sentence_transformers": "3.2.0",
+ "transformers": "4.45.2",
+ "pytorch": "2.4.1+cu121"
+ },
+ "prompts": {},
+ "default_prompt_name": null,
+ "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:a6ca4dbfeb469c4164c9c41bc9f10d83db0a2b10b814c0adc9088bdb79bacfe1
+size 1221487872

modules.json

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+[
+ {
+ "idx": 0,
+ "name": "0",
+ "path": "",
+ "type": "sentence_transformers.models.Transformer"
+ },
+ {
+ "idx": 1,
+ "name": "1",
+ "path": "1_Pooling",
+ "type": "sentence_transformers.models.Pooling"
+ },
+ {
+ "idx": 2,
+ "name": "2",
+ "path": "2_Normalize",
+ "type": "sentence_transformers.models.Normalize"
+ }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+ "max_seq_length": 8192,
+ "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+ "bos_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "cls_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "mask_token": {
+ "content": "<mask>",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<pad>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "sep_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenizer.json

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+oid sha256:aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
+size 17082988

tokenizer_config.json

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+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
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+ "lstrip": false,
+ "normalized": false,
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+ "lstrip": false,
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+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "250001": {
+ "content": "<mask>",
+ "lstrip": true,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "bos_token": "<s>",
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "<s>",
+ "eos_token": "</s>",
+ "mask_token": "<mask>",
+ "model_max_length": 8192,
+ "pad_token": "<pad>",
+ "sep_token": "</s>",
+ "tokenizer_class": "XLMRobertaTokenizer",
+ "unk_token": "<unk>"
+}