README.md

metadata

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 model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Semantic Similarity

• Dataset: pritamdeka/stsb-assamese-translated-dev
• Evaluated with 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 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

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

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

@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",
}