---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1M<n<10M
- loss:MSELoss
base_model: l3cube-pune/indic-sentence-similarity-sbert
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- negative_mse
widget:
- source_sentence: Nobody is standing
  sentences:
  - The person staring has no vision.
  - The person in black T-shirt is sitting.
  - The two girls are at the amusement park.
- source_sentence: The door is open.
  sentences:
  - A child is looking out of a door.
  - a woman is shopping by fisher's popcorn
  - Team owner, president and head coach Don Sims is a Christian.
- source_sentence: A man is jogging.
  sentences:
  - A man is rock climbing with protective rope.
  - There is a Coca-Cola sign on a building.
  - A group of women are selling their wares
- source_sentence: A woman is outside
  sentences:
  - A girl is posing outside.
  - A woman is having a drink with a friend.
  - The man is sitting on Santa's lap.
- source_sentence: Men are outdoors.
  sentences:
  - A man is outside.
  - A Little girl is enjoying cake outside.
  - The child is dancing inside.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.6061168880496322
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.63159627628102
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.4867734432158827
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5132315973464433
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5060055860550953
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.530647353370298
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.2197998289852973
      name: Pearson Dot
    - type: spearman_dot
      value: 0.2098437681521414
      name: Spearman Dot
    - type: pearson_max
      value: 0.6061168880496322
      name: Pearson Max
    - type: spearman_max
      value: 0.63159627628102
      name: Spearman Max
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: negative_mse
      value: -3.0273379758000374
      name: Negative Mse
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7908829263963781
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7964877056053918
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7759961128627
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7730137991653084
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7764317252322528
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7735945428555226
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6958642398985296
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6842506896957747
      name: Spearman Dot
    - type: pearson_max
      value: 0.7908829263963781
      name: Pearson Max
    - type: spearman_max
      value: 0.7964877056053918
      name: Spearman Max
---

# SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert). 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:** [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) <!-- at revision b07ef91a96390f3e35ce94ddb42340861519bf07 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
<!-- - **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("ammumadhu/Indic_Bert-8-layers")
# Run inference
sentences = [
    'Men are outdoors.',
    'A man is outside.',
    'A Little girl is enjoying cake outside.',
]
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]
```

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## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6061     |
| **spearman_cosine** | **0.6316** |
| pearson_manhattan   | 0.4868     |
| spearman_manhattan  | 0.5132     |
| pearson_euclidean   | 0.506      |
| spearman_euclidean  | 0.5306     |
| pearson_dot         | 0.2198     |
| spearman_dot        | 0.2098     |
| pearson_max         | 0.6061     |
| spearman_max        | 0.6316     |

#### Knowledge Distillation

* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

| Metric           | Value       |
|:-----------------|:------------|
| **negative_mse** | **-3.0273** |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7909     |
| **spearman_cosine** | **0.7965** |
| pearson_manhattan   | 0.776      |
| spearman_manhattan  | 0.773      |
| pearson_euclidean   | 0.7764     |
| spearman_euclidean  | 0.7736     |
| pearson_dot         | 0.6959     |
| spearman_dot        | 0.6843     |
| pearson_max         | 0.7909     |
| spearman_max        | 0.7965     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 1,147,385 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                            | list                                 |
  | details | <ul><li>min: 4 tokens</li><li>mean: 12.59 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | sentence                                                                   | label                                                                                                                                 |
  |:---------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>[-0.0009042086312547326, 0.02319158799946308, 0.016657305881381035, -0.004571350757032633, -0.008184989914298058, ...]</code>   |
  | <code>Children smiling and waving at camera</code>                         | <code>[-0.020024249330163002, -0.0005705401999875903, 0.025419672951102257, -0.014105383306741714, 0.009407470934092999, ...]</code>  |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.01713346689939499, -2.3264645278686658e-05, -0.0005397812929004431, 0.002506087301298976, 0.027286207303404808, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### sentence-transformers/wikipedia-en-sentences

* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
* Size: 10,000 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                            | list                                 |
  | details | <ul><li>min: 5 tokens</li><li>mean: 13.53 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | sentence                                                                                                                                                                       | label                                                                                                                                  |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>[-0.000599742284975946, 0.0042074089869856834, 0.0013686479069292545, -0.0009170330595225096, -0.010106148198246956, ...]</code> |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[0.003711540251970291, -0.005768307950347662, -0.03475787863135338, 0.010626137256622314, -0.0023863380774855614, ...]</code>    |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>[-0.014246350154280663, 0.015385480597615242, 0.0016394935082644224, -0.013386472128331661, -0.015061145648360252, ...]</code>   |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `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`: 0.0001
- `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`: 0.1
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | negative_mse | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------------:|:-----------------------:|:------------------------:|
| 0      | 0    | -             | -3.0273      | 0.6316                  | -                        |
| 0.2231 | 1000 | 0.0015        | -            | -                       | -                        |
| 0.4462 | 2000 | 0.0001        | -            | -                       | -                        |
| 0.6693 | 3000 | 0.0001        | -            | -                       | -                        |
| 0.8925 | 4000 | 0.0001        | -            | -                       | -                        |
| 1.0    | 4482 | -             | -            | -                       | 0.7965                   |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.0
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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",
}
```

#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

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