SentenceTransformer based on pritamdeka/muril-base-cased-assamese
This is a sentence-transformers model finetuned from pritamdeka/muril-base-cased-assamese. 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
- 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-nli-v2")
# Run inference
sentences = [
'মই ভালদৰে জানিব নোৱাৰোঁ আপোনালোকৰ সৈতে কথা বতৰা আৰু এক ভাল সন্ধ্যা আছিল',
'মই নিশ্চিত নহয় কিন্তু মই অলপ ভাল, আজি ৰাতি আপোনালোকৰ সৈতে কথা পাতিবলৈ পাই ভাল লাগিল।',
'Shannon এ বাৰ্তা উপেক্ষা কৰিছে।',
]
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:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.7488 |
spearman_cosine | 0.7569 |
pearson_manhattan | 0.7797 |
spearman_manhattan | 0.7768 |
pearson_euclidean | 0.7811 |
spearman_euclidean | 0.7787 |
pearson_dot | 0.6807 |
spearman_dot | 0.6757 |
pearson_max | 0.7811 |
spearman_max | 0.7787 |
Semantic Similarity
- Dataset:
sts-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.727 |
spearman_cosine | 0.7259 |
pearson_manhattan | 0.7407 |
spearman_manhattan | 0.7356 |
pearson_euclidean | 0.7426 |
spearman_euclidean | 0.7377 |
pearson_dot | 0.6322 |
spearman_dot | 0.6226 |
pearson_max | 0.7426 |
spearman_max | 0.7377 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 1warmup_ratio
: 0.1fp16
: Trueload_best_model_at_end
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | - | 0.6627 | - |
0.0489 | 500 | 2.1056 | 0.9849 | 0.7647 | - |
0.0978 | 1000 | 0.4447 | 0.9752 | 0.7478 | - |
0.1467 | 1500 | 0.4239 | 0.8761 | 0.7525 | - |
0.1956 | 2000 | 0.3976 | 0.8384 | 0.7323 | - |
0.2445 | 2500 | 0.3761 | 0.8305 | 0.7316 | - |
0.2933 | 3000 | 0.3756 | 0.8044 | 0.7516 | - |
0.3422 | 3500 | 0.3497 | 0.7878 | 0.7359 | - |
0.3911 | 4000 | 0.3285 | 0.7639 | 0.7439 | - |
0.4400 | 4500 | 0.371 | 0.7253 | 0.7398 | - |
0.4889 | 5000 | 0.3023 | 0.6936 | 0.7461 | - |
0.5378 | 5500 | 0.317 | 0.7230 | 0.7578 | - |
0.5867 | 6000 | 0.3222 | 0.6769 | 0.7469 | - |
0.6356 | 6500 | 0.3039 | 0.6596 | 0.7445 | - |
0.6845 | 7000 | 0.3086 | 0.6117 | 0.7640 | - |
0.7334 | 7500 | 0.309 | 0.6051 | 0.7710 | - |
0.7822 | 8000 | 0.3064 | 0.5915 | 0.7656 | - |
0.8311 | 8500 | 0.3122 | 0.5753 | 0.7646 | - |
0.8800 | 9000 | 0.2986 | 0.5758 | 0.7604 | - |
0.9289 | 9500 | 0.3065 | 0.5607 | 0.7567 | - |
0.9778 | 10000 | 0.2624 | 0.5614 | 0.7569 | - |
1.0 | 10227 | - | - | - | 0.7259 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Evaluation results
- Pearson Cosine on sts devself-reported0.749
- Spearman Cosine on sts devself-reported0.757
- Pearson Manhattan on sts devself-reported0.780
- Spearman Manhattan on sts devself-reported0.777
- Pearson Euclidean on sts devself-reported0.781
- Spearman Euclidean on sts devself-reported0.779
- Pearson Dot on sts devself-reported0.681
- Spearman Dot on sts devself-reported0.676
- Pearson Max on sts devself-reported0.781
- Spearman Max on sts devself-reported0.779