Bi-Tib-mbert-v1 / README.md

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:600
  - loss:CoSENTLoss
  - dataset_size:2500
base_model: Intellexus/mbert-tibetan-continual-wylie-final
widget:
  - source_sentence: gong bu gzhan min de de'i min//
    sentences:
      - >-
        de la byang chub kyi yan lag bdun gang zhe na/ 'di lta ste/ dran pa yang
        dag byang chub kyi yan lag dang / chos rab rnam 'byed yang dag byang
        chub kyi yan lag dang / brtson 'grus yang dag byang chub kyi yan lag
        dang / dga' ba yang dag byang chub kyi yan lag dang / shin tu sbyangs pa
        yang dag byang chub kyi yan lag dang / ting nge 'dzin yang dag byang
        chub kyi yan lag dang / btang snyoms yang dag byang chub kyi yan lag
        ste/ de dag ni byang chub kyi yan lag bdun ces bya'o//
      - phung myin gal te de de myin//
      - >-
        sha ra dwa ti'i bu gzhan yang byang chub sems dpa' sems dpa' chen po
        byang sa las 'da' bar 'dod pas/ shes rab kyi pha rol tu phyin pa la
        bslab par bya'o//
  - source_sentence: kun rdzob tu ni thugs brtse bas// rgyu mthun de dag thub pa bzhed//
    sentences:
      - yang na sku gzugs ma nyams spyan ras sngar zlas
      - kun rdzob 'jig rten grags pa la// brtan na tshad ma'i rnam gzhag 'gal//
      - "gzhan gyi dbang gi ngo bo nyid//\r\nrnam rtog yin te rkyen las byung //\r\ngrub ni de la snga ma po//\r\nrtag tu med par gyur pa gang //"
  - source_sentence: >
      bdag las ma yin gzhan las min// gnyis las ma yin rgyu med min// dngos po
      gang dag gang na yang // skye ba nam yang yod ma yin//
    sentences:
      - |-
        shing rta che bu sems can che//

        rtag mo bkres mthong stag phrug rnams//

        thar bar bya phyir snying rje yis//
      - >
        phyogs chos de chas khyab pa yi// gtan tshigs de ni rnam gsum nyid// med
        na mi 'byung nges phyir ro// gtan tshigs ltar snang de las gzhan//
      - sems can rnams kyi 'dod chags byang gyur cig//
  - source_sentence: >-
      gang gi tshe rgyal po pad ma chen po dpung dang mthu che ba de'i tshe na/
      des kyang dpung gi tshogs yan lag bzhi pa/ glang po che pa'i tshogs dang /
      rta pa'i tshogs dang / shing rta pa'i tshogs dang / dpung bu chung gi
      tshogs go bskon te/ yul ang ga tsam pa ma gtogs pa bcom nas phyir ldog par
      byed do//
    sentences:
      - >-
        de tshe rig pa'i rgyal po bsgrub// gal te de ni rab byung gyur// sdom pa
        gsum la yang dag gnas// so sor thar dang byang chub sems// rig 'dzin
        sdom pa mchog yin no//
      - >-
        spyir theg pa zhes bya ba'i nges tshig ni/ ya na zhes bya ba 'gro ba'i
        bya ba ston pa'i tshig yin pas tshig gzugs por lam la bya'o//
      - >-
        rgyal po chen po 'di ltar yang dge sbyong dang / bram ze kha cig dad pas
        byin pa dag spyad nas ltad mo sna tshogs rtsom pa la sbyor bar brtson
        pas gnas pa 'di lta ste/
  - source_sentence: >-
      dam tshig nyams pa'i nyes pa ni/ 'dod pa'i phyogs mi 'grub cing / mi 'dod
      pa'i phyogs rnams thob pa ste/
    sentences:
      - |
        dam tshig dang ni mi ldan na// bsgrubs kyang 'grub par mi 'gyur te//
        rgyu med pa yi 'bras bu bzhin// tshe yi  dus byas dmyal bar 'gro//
      - >-
        rang sangs rgyas rnams kyi rnam par grol ba ni/ ngag gi lam dang bral ba
        las skyes pa/
      - |
        lha dang lha mo ji lta bas// bdud rtsi'i  bum pas dbang bskur ba//
        chu'i dgongs pa  ye shes lnga'i// rtags su  sku lnga rdzogs pa'o//
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: >-
      SentenceTransformer based on
      Intellexus/mbert-tibetan-continual-wylie-final
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.8350341193647188
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8539838973084938
            name: Spearman Cosine

SentenceTransformer based on Intellexus/mbert-tibetan-continual-wylie-final

This is a sentence-transformers model finetuned from Intellexus/mbert-tibetan-continual-wylie-final. 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: Intellexus/mbert-tibetan-continual-wylie-final
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
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("sentence_transformers_model_id")
# Run inference
sentences = [
    "dam tshig nyams pa'i nyes pa ni/ 'dod pa'i phyogs mi 'grub cing / mi 'dod pa'i phyogs rnams thob pa ste/",
    "dam tshig dang ni mi ldan na// bsgrubs kyang 'grub par mi 'gyur te//\nrgyu med pa yi 'bras bu bzhin// tshe yi  dus byas dmyal bar 'gro//\n",
    "lha dang lha mo ji lta bas// bdud rtsi'i  bum pas dbang bskur ba//\nchu'i dgongs pa  ye shes lnga'i// rtags su  sku lnga rdzogs pa'o//\n",
]
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

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.835
spearman_cosine	0.854

Training Details

Training Dataset

Unnamed Dataset

Size: 2,500 training samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string float
details
min: 6 tokens
mean: 19.74 tokens
max: 67 tokens

min: 5 tokens
mean: 22.11 tokens
max: 83 tokens

min: 0.02
mean: 0.51
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 6 tokens mean: 19.74 tokens max: 67 tokens	min: 5 tokens mean: 22.11 tokens max: 83 tokens	min: 0.02 mean: 0.51 max: 1.0

Samples:

text1	text2	label
`'on pa rnams kyang rna bas sgra thos p`	`'on pa rnams rna bas sgra thes par bya'o snyam pa dang / smyon pa rnams dran pa thob par`	`0.5`
`com ldan 'das de bzhin gshegs pa dgra bc`	`mkhas pa yongs su gzung bar 'dod pa'i byang chub sems dpa' sems dpa' chen`	`0.229`
`pa /sems can thams cad`	`ng / snying rje'i sems dang ldan pa`	`0.3335`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 150 evaluation samples
Columns: text1, text2, and label
Approximate statistics based on the first 150 samples:
text1 text2 label
type string string float
details
min: 8 tokens
mean: 32.74 tokens
max: 126 tokens

min: 6 tokens
mean: 32.12 tokens
max: 121 tokens

min: 0.0
mean: 0.5
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 8 tokens mean: 32.74 tokens max: 126 tokens	min: 6 tokens mean: 32.12 tokens max: 121 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

text1	text2	label
`khang ljon shing rgyal mtshan seng ge rta`	`khang bzangs ljong shing bram ze seng ge rta`	`0.5625`
`rnam par thar pa'i sgo mtshan ma med pa/`	`yod ces bya bar yang dag par rjes su mi mthong ba/`	`0.375`
`byang chub ni chos kyi dbyings kyi gnas kyis gnas pa'o// byang chub ni de bzhin nyid rjes su rtogs pa'o//`	`nges pa yod na mngon sum min// 'dra bar 'dzin pa rtog pa yin//`	`0.0`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 7
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 7
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	spearman_cosine
1.0	2	56.9409	2.7480	0.8357
2.0	4	53.1489	2.7016	0.8412
3.0	6	52.3657	2.6812	0.8462
3.8421	7	89.1774	2.6767	0.8471
0.8101	4	96.7978	2.7350	0.8455
1.8101	8	94.8279	2.6985	0.8497
2.8101	12	93.583	2.6846	0.8540

Framework Versions

Python: 3.12.11
Sentence Transformers: 4.1.0
Transformers: 4.50.0
PyTorch: 2.5.1
Accelerate: 1.7.0
Datasets: 3.3.2
Tokenizers: 0.21.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}