---
base_model: intfloat/multilingual-e5-large-instruct
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- 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?'
sentences:
- मंगलोर शान्त वा हिंस्रक स्थान हो?
- पुरुषहरूको तुलनामा महिलाहरूको लागि यौनिक आनन्द बढी हुन्छ कि हुँदैन?
- के कसैले केएमसी मानिपाल र मंगलोरको संक्षिप्त तुलना गर्न सक्छ?
- source_sentence: म कसरी मेरो अङ्ग्रेजी भाषा सुधार गर्न सक्छु?
sentences:
- म कसरी एक नेचुरल अंग्रेजी वक्ता बन्न सक्छु?
- म जहाँ कुनै मूल अंग्रेजी वक्ताहरू छन् जो मेरो साथ मित्र बन्न चाहन्छन् र मलाई मद्दत
गर्न चाहन्छन्?
- ने टी २०१ 6 को लागि निजी कलेजहरूको लागि एमबीबीएसको लागि के कटअफ हुनेछ?
- source_sentence: समय यात्रा सम्भव छ कि छैन? यदि छ भने, कसरी?
sentences:
- अन्धकारमय वेब सुरक्षित छ कि छैन ब्राउज गर्न?
- यदि कुनै बितेको समय राम्रो थियो र समयको यात्रा सम्भव थियो भने म किन वर्तमान समयमा
बाँचिरहेको छु?
- भविष्यमा समय यात्रा सम्भव हुनेछ कि छैन?
- source_sentence: म कसरी बिस्तारै तौल घटाउन सक्छु?
sentences:
- कसरी कुनै केटाले त्यो केटीसँग बदला लिन सक्छ जसले उसलाई धोका दिएको छ?
- कस्तो प्रकारको आहार कसैले आहार नचाहने व्यक्तिका लागि उत्तम हुन्छ?
- वजन घटाउनको लागि कुनै राम्रो आहार हो?
---
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the universalml0/nepali_embedding_dataset dataset. It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- universalml0/nepali_embedding_dataset
### 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
### 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("universalml0/finetuned_embedding_model_e5-large-multilingual-large")
# Run inference
sentences = [
'म कसरी बिस्तारै तौल घटाउन सक्छु?',
'वजन घटाउनको लागि कुनै राम्रो आहार हो?',
'कस्तो प्रकारको आहार कसैले आहार नचाहने व्यक्तिका लागि उत्तम हुन्छ?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### universalml0/nepali_embedding_dataset
* Dataset: universalml0/nepali_embedding_dataset
* Size: 45,199 training samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 7 tokens
- mean: 17.53 tokens
- max: 486 tokens
| - min: 6 tokens
- mean: 17.68 tokens
- max: 512 tokens
| - min: 6 tokens
- mean: 18.9 tokens
- max: 156 tokens
|
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| भारतीय सरकारले ५०० र १००० रुपयाको नोटमाथि प्रतिबन्ध लगाउनुको कारण के थियो? | भारतीय सरकारले ५०० र १००० को नोटलाई निष्क्रिय पारेको छ तर तिनीहरूलाई ५०० र २००० को नोटहरूसँग प्रतिस्थापन गरेको छ। के यो विरोधाभासी छैन? | 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? |
| भारतीय हुनुको अनुभूति कस्तो हुन्छ? | भारतीय हुनुको अनुभूति कस्तो हुन्छ? | भारतीय महिला हुनुको अनुभव कस्तो हुन्छ? |
| के कुनै व्यक्तिले edWisor मार्फत कुनै नौकरी पाएको छ? | एडवाइजर वैध छ र के कसैले यस मार्फत कुनै नौकरी पाएको छ? | एलिटमसको माध्यमबाट कसैले काम पाएको छ? |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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
Click to expand
| 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 |