---
base_model: pritamdeka/distilbert-base-multilingual-cased-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/distilbert-base-multilingual-cased-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: 0.8103888874564235
name: Pearson Cosine
- type: spearman_cosine
value: 0.808745256408391
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7856524098322162
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7931254692762979
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.787635055496797
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7951615705258325
name: Spearman Euclidean
- type: pearson_dot
value: 0.7706254928060731
name: Pearson Dot
- type: spearman_dot
value: 0.7771019257164439
name: Spearman Dot
- type: pearson_max
value: 0.8103888874564235
name: Pearson Max
- type: spearman_max
value: 0.808745256408391
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: 0.7701562538442139
name: Pearson Cosine
- type: spearman_cosine
value: 0.7660618813636367
name: Spearman Cosine
- type: pearson_manhattan
value: 0.749425583772647
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7529158472529595
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7498757891992801
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7531339468525071
name: Spearman Euclidean
- type: pearson_dot
value: 0.7193336616396375
name: Pearson Dot
- type: spearman_dot
value: 0.7151802549941848
name: Spearman Dot
- type: pearson_max
value: 0.7701562538442139
name: Pearson Max
- type: spearman_max
value: 0.7660618813636367
name: Spearman Max
---
# SentenceTransformer based on pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/distilbert-base-multilingual-cased-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/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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: DistilBertModel
(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/distilbert-base-multilingual-cased-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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8104 |
| **spearman_cosine** | **0.8087** |
| pearson_manhattan | 0.7857 |
| spearman_manhattan | 0.7931 |
| pearson_euclidean | 0.7876 |
| spearman_euclidean | 0.7952 |
| pearson_dot | 0.7706 |
| spearman_dot | 0.7771 |
| pearson_max | 0.8104 |
| spearman_max | 0.8087 |
#### Semantic Similarity
* Dataset: `pritamdeka/stsb-assamese-translated-test`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7702 |
| **spearman_cosine** | **0.7661** |
| pearson_manhattan | 0.7494 |
| spearman_manhattan | 0.7529 |
| pearson_euclidean | 0.7499 |
| spearman_euclidean | 0.7531 |
| pearson_dot | 0.7193 |
| spearman_dot | 0.7152 |
| pearson_max | 0.7702 |
| spearman_max | 0.7661 |
## 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
Click to expand
- `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