metadata

base_model: l3cube-pune/assamese-bert
datasets:
  - sentence-transformers/all-nli
language:
  - en
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:557850
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: A man is jumping unto his filthy bed.
    sentences:
      - A young male is looking at a newspaper while 2 females walks past him.
      - The bed is dirty.
      - The man is on the moon.
  - source_sentence: >-
      A carefully balanced male stands on one foot near a clean ocean beach
      area.
    sentences:
      - A man is ouside near the beach.
      - Three policemen patrol the streets on bikes
      - A man is sitting on his couch.
  - source_sentence: The man is wearing a blue shirt.
    sentences:
      - Near the trashcan the man stood and smoked
      - >-
        A man in a blue shirt leans on a wall beside a road with a blue van and
        red car with water in the background.
      - A man in a black shirt is playing a guitar.
  - source_sentence: The girls are outdoors.
    sentences:
      - Two girls riding on an amusement part ride.
      - a guy laughs while doing laundry
      - >-
        Three girls are standing together in a room, one is listening, one is
        writing on a wall and the third is talking to them.
  - source_sentence: >-
      A construction worker peeking out of a manhole while his coworker sits on
      the sidewalk smiling.
    sentences:
      - A worker is looking out of a manhole.
      - A man is giving a presentation.
      - The workers are both inside the manhole.
model-index:
  - name: SentenceTransformer based on l3cube-pune/assamese-bert
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.8448431188558219
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.848270397607023
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8429962459024234
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8461225961159852
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8450811877325317
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8481702238714027
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7600437454974306
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7604490741243843
            name: Spearman Dot
          - type: pearson_max
            value: 0.8450811877325317
            name: Pearson Max
          - type: spearman_max
            value: 0.848270397607023
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8160018744466311
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8230016183156494
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8104201802445242
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8104000391884387
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8108715587588242
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8112881633291651
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7088828153549986
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6991542788989243
            name: Spearman Dot
          - type: pearson_max
            value: 0.8160018744466311
            name: Pearson Max
          - type: spearman_max
            value: 0.8230016183156494
            name: Spearman Max

SentenceTransformer based on l3cube-pune/assamese-bert

This is a sentence-transformers model finetuned from l3cube-pune/assamese-bert on the sentence-transformers/all-nli dataset. 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/assamese-bert
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/all-nli
Language: en

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/assamese-bert-nli-v2")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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	0.8448
spearman_cosine	0.8483
pearson_manhattan	0.843
spearman_manhattan	0.8461
pearson_euclidean	0.8451
spearman_euclidean	0.8482
pearson_dot	0.76
spearman_dot	0.7604
pearson_max	0.8451
spearman_max	0.8483

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.816
spearman_cosine	0.823
pearson_manhattan	0.8104
spearman_manhattan	0.8104
pearson_euclidean	0.8109
spearman_euclidean	0.8113
pearson_dot	0.7089
spearman_dot	0.6992
pearson_max	0.816
spearman_max	0.823

Training Details

Training Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/all-nli at d482672
Size: 557,850 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 10.55 tokens max: 48 tokens	min: 6 tokens mean: 13.08 tokens max: 40 tokens	min: 5 tokens mean: 13.7 tokens max: 53 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/all-nli at d482672
Size: 6,584 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 18.54 tokens max: 74 tokens	min: 4 tokens mean: 9.97 tokens max: 30 tokens	min: 5 tokens mean: 10.59 tokens max: 29 tokens

Samples:

anchor	positive	negative
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`The men are fighting outside a deli.`
`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.`	`Two kids in numbered jerseys wash their hands.`	`Two kids in jackets walk to school.`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`A man selling donuts to a customer.`	`A woman drinks her coffee in a small cafe.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

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: 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: False
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	-	0.6401	-
0.0574	500	2.5567	1.2774	0.7654	-
0.1147	1000	1.3874	1.0303	0.7997	-
0.1721	1500	1.1493	0.9597	0.7867	-
0.2294	2000	0.9885	0.7656	0.7895	-
0.2868	2500	0.9588	0.8041	0.7797	-
0.3442	3000	0.922	0.7280	0.7785	-
0.4015	3500	0.8693	0.6803	0.7925	-
0.4589	4000	0.8436	0.6892	0.7866	-
0.5162	4500	0.8033	0.7127	0.7818	-
0.5736	5000	0.8061	0.6854	0.7746	-
0.6310	5500	0.8069	0.6496	0.7856	-
0.6883	6000	0.8133	0.6490	0.7787	-
0.7457	6500	0.7857	0.5926	0.8010	-
0.8030	7000	0.4404	0.4472	0.8457	-
0.8604	7500	0.3422	0.4441	0.8473	-
0.9177	8000	0.308	0.4315	0.8494	-
0.9751	8500	0.299	0.4305	0.8483	-
1.0	8717	-	-	-	0.8230

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",
}

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}
}