Upload folder using huggingface_hub

.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 +1,5 @@
+{
+    "embedding_dimension": 1024,
+    "pooling_mode": "cls",
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,385 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:4858
+- loss:MultipleNegativesRankingLoss
+base_model: sanganaka/bge-m3-sanskritFT
+widget:
+- source_sentence: I've achieved a lot in my career, but I still feel a deep sense
+    of emptiness. I thought reaching these milestones would bring lasting satisfaction,
+    but it hasn't. Was it all for nothing? What is my true purpose if external achievements
+    don't fulfill me?
+  sentences:
+  - abhyāsa-yoga-yuktena cetasā nānya-gāminā | paramaṃ puruṣaṃ divyaṃ yāti pārthānucintayan
+    ||8||
+  - abhyāse 'py asamartho 'si mat-karma-paramo bhava | mad-artham api karmāṇi kurvan
+    siddhim avāpsyasi ||10||
+  - na kartṛtvaṃ na karmāṇi lokasya sṛjati prabhuḥ | na karma-phala-saṃyogaṃ svabhāvas
+    tu pravartate ||14||
+- source_sentence: I always feel so tired and sluggish, even after a full night's
+    sleep. My mind feels foggy, and I can't concentrate at work. What can I do to
+    regain my vitality and focus?
+  sentences:
+  - ye tu dharmyāmṛtam idaṃ yathoktaṃ paryupāsate | śraddadhānā mat-paramā bhaktās
+    te 'tīva me priyāḥ ||20||
+  - āyuḥ-sattva-balārogya-sukha-prīti-vivardhanāḥ | rasyāḥ snigdhāḥ sthirā hṛdyā āhārāḥ
+    sāttvika-priyāḥ ||8||
+  - devān bhāvayatānena te devā bhāvayantu vaḥ | parasparaṃ bhāvayantaḥ śreyaḥ param
+    avāpsyatha ||11||
+- source_sentence: I'm a working parent, constantly juggling responsibilities, and
+    I feel utterly overwhelmed and burnt out. I don't have a moment for myself, and
+    I'm losing my sense of self.
+  sentences:
+  - idaṃ jñānam upāśritya mama sādharmyam āgatāḥ | sarge 'pi nopajāyante pralaye na
+    vyathanti ca ||2||
+  - teṣām evānukampārtham aham ajñānajaṃ tamaḥ | nāśayāmy ātma-bhāva-stho jñāna-dīpena
+    bhāsvatā ||11||
+  - amānitvam adambhitvam ahiṃsā kṣāntir ārjavam | ācāryopāsanaṃ śaucaṃ sthairyam
+    ātma-vinigrahaḥ ||7|| indriyārtheṣu vairāgyam anahaṃkāra eva ca | janma-mṛtyu-jarā-vyādhi-duḥkha-doṣānudarśanam
+    ||8|| asaktir anabhiṣvaṅgaḥ putra-dāra-gṛhādiṣu | nityaṃ ca sama-cittatvam iṣṭāniṣṭopapattiṣu
+    ||9|| mayi cānanya-yogena bhaktir avyabhicāriṇī | vivikta-deśa-sevitvam aratir
+    jana-saṃsadi ||10|| adhyātma-jñāna-nityatvaṃ tattva-jñānārtha-darśanam | etaj
+    jñānam iti proktam ajñānaṃ yad ato 'nyathā ||11||
+- source_sentence: I've always been so worried about what others think of me, especially
+    online. One negative comment can ruin my entire day, even if there are hundreds
+    of positive ones. How can I develop a stronger sense of self-worth that isn't
+    dependent on external validation?
+  sentences:
+  - nirmāna-mohā jita-saṅga-doṣā adhyātma-nityā vinivṛtta-kāmāḥ | dvandvair vimuktāḥ
+    sukha-duḥkha-saṃjñair gacchanty amūḍhāḥ padam avyayaṃ tat ||5||
+  - pravṛttiṃ ca nivṛttiṃ ca janā na vidur āsurāḥ | na śaucaṃ nāpi cācāro na satyaṃ
+    teṣu vidyate ||7||
+  - samaḥ śatrau ca mitre ca tathā mānāpamānayoḥ | śītoṣṇa-sukha-duḥkheṣu samaḥ saṅga-vivarjitaḥ
+    ||18|| tulya-nindā-stutir maunī saṃtuṣṭo yena kenacit | aniketaḥ sthira-matir
+    bhaktimān me priyo naraḥ ||19||
+- source_sentence: I've been grieving a significant loss for a long time, and while
+    I know I need to move forward, my thoughts constantly pull me back to the past.
+    How do I let go and find peace?
+  sentences:
+  - daivī saṃpad vimokṣāya nibandhāyāsurī matā | mā śucaḥ saṃpadaṃ daivīm abhijāto
+    'si pāṇḍava ||5||
+  - etair vimuktaḥ kaunteya tamo-dvārais tribhir naraḥ | ācaraty ātmanaḥ śreyas tato
+    yāti parāṃ gatim ||22||
+  - uddhared ātmanātmānaṃ nātmānam avasādayet | ātmaiva hy ātmano bandhur ātmaiva
+    ripur ātmanaḥ ||5||
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sanganaka/bge-m3-sanskritFT
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sanganaka/bge-m3-sanskritFT](https://huggingface.co/sanganaka/bge-m3-sanskritFT). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sanganaka/bge-m3-sanskritFT](https://huggingface.co/sanganaka/bge-m3-sanskritFT) <!-- at revision bcad4d3ffe0990d09bbc07f821bbbd5050ba0530 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
+  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', '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 = [
+    "I've been grieving a significant loss for a long time, and while I know I need to move forward, my thoughts constantly pull me back to the past. How do I let go and find peace?",
+    'uddhared ātmanātmānaṃ nātmānam avasādayet | ātmaiva hy ātmano bandhur ātmaiva ripur ātmanaḥ ||5||',
+    'etair vimuktaḥ kaunteya tamo-dvārais tribhir naraḥ | ācaraty ātmanaḥ śreyas tato yāti parāṃ gatim ||22||',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.4964, 0.1087],
+#         [0.4964, 1.0000, 0.3406],
+#         [0.1087, 0.3406, 1.0000]])
+```
+<!--
+### 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: 4,858 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 100 samples:
+  |          | sentence_0                                                                        | sentence_1                                                                          | sentence_2                                                                         |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              | string                                                                             |
+  | modality | text                                                                              | text                                                                                | text                                                                               |
+  | details  | <ul><li>min: 18 tokens</li><li>mean: 46.5 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 66.11 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 84.2 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                            | sentence_1                                                                                                                                                                                                                           | sentence_2                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
+  | <code>As a professional, I feel constantly burnt out, always chasing the next promotion or project. I've lost touch with why I even started, and joy seems like a distant memory. Is there a way to reconnect with my passion?</code> | <code>yaṃ labdhvā cāparaṃ lābhaṃ manyate nādhikaṃ tataḥ \| yasmin sthito na duḥkhena guruṇāpi vicālyate \|\|22\|\| taṃ vidyād duḥkha-saṃyoga-viyogaṃ yoga-saṃjñitam \| sa niścayena yoktavyo yogo 'nirviṇṇa-cetasā \|\|23\|\|</code> | <code>yaṃ hi na vyathayanty ete puruṣaṃ puruṣarṣabha \| sama-duḥkha-sukhaṃ dhīraṃ so 'mṛtatvāya kalpate \|\|15\|\|</code>  |
+  | <code>My teenage son is rebelling and pushing me away. I feel like I'm losing him. What can I do?</code>                                                                                                                              | <code>ayaneṣu ca sarveṣu yathābhāgam avasthitāḥ \| bhīṣmam evābhirakṣantu bhavantaḥ sarva eva hi \|\|11\|\|</code>                                                                                                                   | <code>acchedyo 'yam adāhyo 'yam akledyo 'śoṣya eva ca \| nityaḥ sarva-gataḥ sthāṇur acalo 'yaṃ sanātanaḥ \|\|24\|\|</code> |
+  | <code>I'm constantly worried about the future – what if my plans fail? What if things don't go my way? This anxiety paralyzes me and prevents me from acting.</code>                                                                  | <code>yajñadānatapaḥkarma na tyājyaṃ kāryam eva tat \| yajño dānaṃ tapaś caiva pāvanāni manīṣiṇām \|\|5\|\|</code>                                                                                                                   | <code>ahiṃsā samatā tuṣṭis tapo dānaṃ yaśo 'yaśaḥ \| bhavanti bhāvā bhūtānāṃ matta eva pṛthagvidhāḥ \|\|5\|\|</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",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 2
+- `per_device_eval_batch_size`: 16
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `learning_rate`: 5e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: None
+- `trackio_bucket_id`: None
+- `trackio_static_space_id`: None
+- `per_device_eval_batch_size`: 16
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_static_graph`: None
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: None
+- `fsdp_config`: None
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 1.6447 | 500  | 2.8599        |
+
+
+### Training Time
+- **Training**: 10.0 minutes
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 5.5.1
+- Transformers: 5.12.1
+- PyTorch: 2.12.0+cu130
+- Accelerate: 1.14.0
+- Datasets: 5.0.0
+- Tokenizers: 0.22.2
+
+## 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{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## 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 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "is_decoder": false,
+  "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",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.12.1"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 2271064456

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer.json

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+size 17082987

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "max_length": 8192,
+  "model_max_length": 256,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}