Update README.md

README.md

@@ -12,13 +12,6 @@ tags:
 - dataset_size:45199
 - loss:MultipleNegativesRankingLoss
 widget:
-- source_sentence: प्रधानमन्त्री नरेन्द्र मोदी सरकारका असफलताहरू के के हुन्?
-  sentences:
-  - >-
-    पूर्वोत्तर राज्यहरूका मुख्य समस्याहरू के के हुन् र तिनीहरूको केन्द्रीय
-    सरकारसँग असन्तोष के हो?
-  - पूर्णांक के हो?
-  - नरेन्द्र मोदी सरकारले कुन क्षेत्रमा असफल भएको छ?
 - source_sentence: >-
     मैले विचार गर्नुपर्ने कलेजहरू के के हुन्, विचार गर्नुपर्ने कारकहरू: केएमसी
     म्यानिपल वा केएमसी मंगोलमा?
@@ -52,30 +45,12 @@ license: apache-2.0
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision baa7be480a7de1539afce709c8f13f833a510e0a -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 1024 tokens
 - **Similarity Function:** Cosine Similarity
 - **Training Dataset:**
     - universalml0/nepali_embedding_dataset
-<!-- - **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: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, '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})
-  (2): Normalize()
-)
-```
 
 ## Usage
 
@@ -92,7 +67,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("universalml0/finetuned_embedding_model_e5-large-multilingual-large")
+model = SentenceTransformer("universalml/Nepali_Embedding_Model")
 # Run inference
 sentences = [
     'म कसरी बिस्तारै तौल घटाउन सक्छु?',
@@ -107,372 +82,4 @@ print(embeddings.shape)
 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.*
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-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-
-## Training Details
-
-### Training Dataset
-
-#### universalml0/nepali_embedding_dataset
-
-* Dataset: universalml0/nepali_embedding_dataset
-* Size: 45,199 training samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             | string                                                                            |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 17.53 tokens</li><li>max: 486 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.68 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.9 tokens</li><li>max: 156 tokens</li></ul> |
-* Samples:
-  | anchor                                                                                  | positive                                                                                                                                             | negative                                                                                                                                                  |
-  |:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>भारतीय सरकारले ५०० र १००० रुपयाको नोटमाथि प्रतिबन्ध लगाउनुको कारण के थियो?</code> | <code>भारतीय सरकारले ५०० र १००० को नोटलाई निष्क्रिय पारेको छ तर तिनीहरूलाई ५०० र २००० को नोटहरूसँग प्रतिस्थापन गरेको छ। के यो विरोधाभासी छैन?</code> | <code>भारतीय सरकारले किन चाहेको भए सीमित मात्रामा नोटहरू मुद्रण गर्न र बजेट घाटा क्लियर गर्न सक्दैन? विशेष गरी, किन कुनै पनि देशले यो गर्न सक्दैन?</code> |
-  | <code>भारतीय हुनुको अनुभूति कस्तो हुन्छ?</code>                                         | <code>भारतीय हुनुको अनुभूति कस्तो हुन्छ?</code>                                                                                                      | <code>भारतीय महिला हुनुको अनुभव कस्तो हुन्छ?</code>                                                                                                       |
-  | <code>के कुनै व्यक्तिले edWisor मार्फत कुनै नौकरी पाएको छ?</code>                       | <code>एडवाइजर वैध छ र के कसैले यस मार्फत कुनै नौकरी पाएको छ?</code>                                                                                  | <code>एलिटमसको माध्यमबाट कसैले काम पाएको छ?</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
-- `learning_rate`: 1e-06
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.3
-- `bf16`: True
-- `batch_sampler`: no_duplicates
-
-#### 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`: 8
-- `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`: 1e-06
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 1
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.3
-- `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`: True
-- `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
-- `eval_use_gather_object`: False
-- `batch_sampler`: no_duplicates
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-<details><summary>Click to expand</summary>
-
-| Epoch  | Step  | Training Loss |
-|:------:|:-----:|:-------------:|
-| 0.0088 | 100   | 0.8671        |
-| 0.0177 | 200   | 0.8234        |
-| 0.0265 | 300   | 0.8223        |
-| 0.0354 | 400   | 0.7423        |
-| 0.0442 | 500   | 0.6605        |
-| 0.0531 | 600   | 0.5558        |
-| 0.0619 | 700   | 0.4076        |
-| 0.0708 | 800   | 0.3617        |
-| 0.0796 | 900   | 0.3087        |
-| 0.0885 | 1000  | 0.2747        |
-| 0.0973 | 1100  | 0.2409        |
-| 0.1062 | 1200  | 0.229         |
-| 0.1150 | 1300  | 0.209         |
-| 0.1239 | 1400  | 0.2556        |
-| 0.1327 | 1500  | 0.2536        |
-| 0.1416 | 1600  | 0.2092        |
-| 0.1504 | 1700  | 0.2464        |
-| 0.1593 | 1800  | 0.1727        |
-| 0.1681 | 1900  | 0.281         |
-| 0.1770 | 2000  | 0.2289        |
-| 0.1858 | 2100  | 0.2065        |
-| 0.1947 | 2200  | 0.1751        |
-| 0.2035 | 2300  | 0.231         |
-| 0.2124 | 2400  | 0.2127        |
-| 0.2212 | 2500  | 0.1908        |
-| 0.2301 | 2600  | 0.2131        |
-| 0.2389 | 2700  | 0.1704        |
-| 0.2478 | 2800  | 0.1923        |
-| 0.2566 | 2900  | 0.1635        |
-| 0.2655 | 3000  | 0.2061        |
-| 0.2743 | 3100  | 0.1843        |
-| 0.2832 | 3200  | 0.1443        |
-| 0.2920 | 3300  | 0.1513        |
-| 0.3009 | 3400  | 0.1879        |
-| 0.3097 | 3500  | 0.2372        |
-| 0.3186 | 3600  | 0.1542        |
-| 0.3274 | 3700  | 0.2523        |
-| 0.3363 | 3800  | 0.2055        |
-| 0.3451 | 3900  | 0.1474        |
-| 0.3540 | 4000  | 0.1647        |
-| 0.3628 | 4100  | 0.1615        |
-| 0.3717 | 4200  | 0.1271        |
-| 0.3805 | 4300  | 0.1451        |
-| 0.3894 | 4400  | 0.1887        |
-| 0.3982 | 4500  | 0.1334        |
-| 0.4071 | 4600  | 0.1962        |
-| 0.4159 | 4700  | 0.1695        |
-| 0.4248 | 4800  | 0.1561        |
-| 0.4336 | 4900  | 0.1146        |
-| 0.4425 | 5000  | 0.1381        |
-| 0.4513 | 5100  | 0.1452        |
-| 0.4602 | 5200  | 0.2388        |
-| 0.4690 | 5300  | 0.1951        |
-| 0.4779 | 5400  | 0.1142        |
-| 0.4867 | 5500  | 0.182         |
-| 0.4956 | 5600  | 0.1968        |
-| 0.5044 | 5700  | 0.1744        |
-| 0.5133 | 5800  | 0.1868        |
-| 0.5221 | 5900  | 0.1452        |
-| 0.5310 | 6000  | 0.1345        |
-| 0.5398 | 6100  | 0.1318        |
-| 0.5487 | 6200  | 0.218         |
-| 0.5575 | 6300  | 0.2118        |
-| 0.5664 | 6400  | 0.1972        |
-| 0.5752 | 6500  | 0.0935        |
-| 0.5841 | 6600  | 0.1991        |
-| 0.5929 | 6700  | 0.1252        |
-| 0.6018 | 6800  | 0.1128        |
-| 0.6106 | 6900  | 0.1585        |
-| 0.6195 | 7000  | 0.2293        |
-| 0.6283 | 7100  | 0.2104        |
-| 0.6372 | 7200  | 0.1416        |
-| 0.6460 | 7300  | 0.2004        |
-| 0.6549 | 7400  | 0.1446        |
-| 0.6637 | 7500  | 0.1171        |
-| 0.6726 | 7600  | 0.1386        |
-| 0.6814 | 7700  | 0.1291        |
-| 0.6903 | 7800  | 0.1546        |
-| 0.6991 | 7900  | 0.1484        |
-| 0.7080 | 8000  | 0.129         |
-| 0.7168 | 8100  | 0.1873        |
-| 0.7257 | 8200  | 0.1333        |
-| 0.7345 | 8300  | 0.1713        |
-| 0.7434 | 8400  | 0.1016        |
-| 0.7522 | 8500  | 0.1519        |
-| 0.7611 | 8600  | 0.1851        |
-| 0.7699 | 8700  | 0.144         |
-| 0.7788 | 8800  | 0.1488        |
-| 0.7876 | 8900  | 0.1568        |
-| 0.7965 | 9000  | 0.1672        |
-| 0.8053 | 9100  | 0.1236        |
-| 0.8142 | 9200  | 0.0973        |
-| 0.8230 | 9300  | 0.1491        |
-| 0.8319 | 9400  | 0.2251        |
-| 0.8407 | 9500  | 0.1433        |
-| 0.8496 | 9600  | 0.2634        |
-| 0.8584 | 9700  | 0.1723        |
-| 0.8673 | 9800  | 0.2373        |
-| 0.8761 | 9900  | 0.1065        |
-| 0.8850 | 10000 | 0.1578        |
-| 0.8938 | 10100 | 0.1127        |
-| 0.9027 | 10200 | 0.1632        |
-| 0.9115 | 10300 | 0.19          |
-| 0.9204 | 10400 | 0.0958        |
-| 0.9292 | 10500 | 0.1029        |
-| 0.9381 | 10600 | 0.1183        |
-| 0.9469 | 10700 | 0.1779        |
-| 0.9558 | 10800 | 0.1571        |
-| 0.9646 | 10900 | 0.1666        |
-| 0.9735 | 11000 | 0.1405        |
-| 0.9823 | 11100 | 0.147         |
-| 0.9912 | 11200 | 0.1428        |
-| 1.0    | 11300 | 0.1724        |
-
-</details>
-
-### Framework Versions
-- Python: 3.9.5
-- Sentence Transformers: 3.0.1
-- Transformers: 4.44.2
-- PyTorch: 2.3.0+cu121
-- Accelerate: 0.33.0
-- Datasets: 2.21.0
-- 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
-
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