README.md

---
base_model: pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: আমি "... comoving মহাজাগতিক বিশ্ৰাম ফ্ৰেমৰ তুলনাত ... সিংহ নক্ষত্ৰমণ্ডলৰ
    ফালে কিছু 371 কিলোমিটাৰ প্ৰতি ছেকেণ্ডত" আগবাঢ়িছো.
  sentences:
  - বাস্কেটবল খেলুৱৈগৰাকীয়ে নিজৰ দলৰ হৈ পইণ্ট লাভ কৰিবলৈ ওলাইছে।
  - আন কোনো বস্তুৰ লগত আপেক্ষিক নহোৱা কোনো ‘ষ্টিল’ নাই।
  - এজনী ছোৱালীয়ে বতাহ বাদ্যযন্ত্ৰ বজায়।
- source_sentence: চাৰিটা ল’ৰা-ছোৱালীয়ে ভঁৰালৰ জীৱ-জন্তুবোৰলৈ চাই আছে।
  sentences:
  - ডাইনিং টেবুল এখনৰ চাৰিওফালে বৃদ্ধৰ দল এটাই পোজ দিছে।
  - বিকিনি পিন্ধা চাৰিগৰাকী মহিলাই বিলত ভলীবল খেলি আছে।
  - ল’ৰা-ছোৱালীয়ে ভেড়া চাই।
- source_sentence: ডালত বহি থকা দুটা টান ঈগল।
  sentences:
  - জাতৰ জেব্ৰা ডানিঅ’ অত্যন্ত কঠোৰ মাছ, ইহঁতক হত্যা কৰাটো প্ৰায় কঠিন।
  - এটা ডালত দুটা ঈগল বহি আছে।
  - নূন্যতম মজুৰিৰ আইনসমূহে কম দক্ষ, কম উৎপাদনশীল লোকক আটাইতকৈ বেছি আঘাত দিয়ে।
- source_sentence: '"মই আচলতে যি বিচাৰিছো সেয়া হৈছে মুছলমান জনসংখ্যাৰ এটা অনুমান..."
    @ThanosK আৰু @T.E.D., এটা সামগ্ৰিক, সাধাৰণ জনসংখ্যাৰ অনুমান f.e.'
  sentences:
  - এগৰাকী মহিলাই সেউজীয়া পিঁয়াজ কাটি আছে।
  - তলত দিয়া কথাখিনি মোৰ কুকুৰ কাণৰ দৰে কপিৰ পৰা লোৱা হৈছে নিউ পেংগুইন এটলাছ অৱ মেডিভেল
    হিষ্ট্ৰীৰ।
  - আমাৰ দৰে সৌৰজগতৰ কোনো তাৰকাৰাজ্যৰ বাহিৰত থকাটো সম্ভৱ হ’ব পাৰে।
- source_sentence: ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।
  sentences:
  - গছৰ শাৰী এটাৰ সন্মুখত পথাৰত ভেড়া চৰিছে।
  - এজন মানুহে গীটাৰ বজাই আছে।
  - ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।
model-index:
- name: SentenceTransformer based on pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: pritamdeka/stsb assamese translated dev
      type: pritamdeka/stsb-assamese-translated-dev
    metrics:
    - type: pearson_cosine
      value: 0.8525258323169252
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8506593647943235
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8334889460288037
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.843042040822402
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8351723933495433
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8450734552112781
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8273071926204811
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8277520425148079
      name: Spearman Dot
    - type: pearson_max
      value: 0.8525258323169252
      name: Pearson Max
    - type: spearman_max
      value: 0.8506593647943235
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: pritamdeka/stsb assamese translated test
      type: pritamdeka/stsb-assamese-translated-test
    metrics:
    - type: pearson_cosine
      value: 0.8138083526567048
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8119367763029309
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8044112753419641
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8073243490029997
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.805728285628756
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8086070843216111
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7754575809083841
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7720173359758135
      name: Spearman Dot
    - type: pearson_max
      value: 0.8138083526567048
      name: Pearson Max
    - type: spearman_max
      value: 0.8119367763029309
      name: Spearman Max
---

# SentenceTransformer based on pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1). 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:** [pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1) <!-- at revision ec876d6ec1d2030ad233470e574f1d3d3fe56c74 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("pritamdeka/muril-base-cased-assamese-indicxnli-random-negatives-v1-sts")
# Run inference
sentences = [
    'ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।',
    'ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।',
    'এজন মানুহে গীটাৰ বজাই আছে।',
]
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.*
-->

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8525     |
| **spearman_cosine** | **0.8507** |
| pearson_manhattan   | 0.8335     |
| spearman_manhattan  | 0.843      |
| pearson_euclidean   | 0.8352     |
| spearman_euclidean  | 0.8451     |
| pearson_dot         | 0.8273     |
| spearman_dot        | 0.8278     |
| pearson_max         | 0.8525     |
| spearman_max        | 0.8507     |

#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8138     |
| **spearman_cosine** | **0.8119** |
| pearson_manhattan   | 0.8044     |
| spearman_manhattan  | 0.8073     |
| pearson_euclidean   | 0.8057     |
| spearman_euclidean  | 0.8086     |
| pearson_dot         | 0.7755     |
| spearman_dot        | 0.772      |
| pearson_max         | 0.8138     |
| spearman_max        | 0.8119     |

<!--
## 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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 10
- `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`: True
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | loss       | pritamdeka/stsb-assamese-translated-dev_spearman_cosine | pritamdeka/stsb-assamese-translated-test_spearman_cosine |
|:----------:|:-------:|:-------------:|:----------:|:-------------------------------------------------------:|:--------------------------------------------------------:|
| 1.1111     | 100     | 0.0331        | 0.0259     | 0.8482                                                  | -                                                        |
| **2.2222** | **200** | **0.0176**    | **0.0253** | **0.8515**                                              | **-**                                                    |
| 3.3333     | 300     | 0.011         | 0.0253     | 0.8513                                                  | -                                                        |
| 4.4444     | 400     | 0.0066        | 0.0259     | 0.8492                                                  | -                                                        |
| 5.5556     | 500     | 0.0048        | 0.0255     | 0.8511                                                  | -                                                        |
| 6.6667     | 600     | 0.0037        | 0.0256     | 0.8508                                                  | -                                                        |
| 7.7778     | 700     | 0.0033        | 0.0254     | 0.8515                                                  | -                                                        |
| 8.8889     | 800     | 0.0029        | 0.0255     | 0.8512                                                  | -                                                        |
| 10.0       | 900     | 0.0027        | 0.0257     | 0.8507                                                  | 0.8119                                                   |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

<!--
## 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.*
-->