Add new SentenceTransformer model

58da7ec verified over 1 year ago

30.4 kB

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:73
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-base
widget:
  - source_sentence: >-
      What is the maximum value of equipment that can be purchased with a CUE
      Student Research Project Grant?
    sentences:
      - Equipment costs (valued up to $1000).
      - >-
        Variable awards to recognize and reward academic achievement at the
        senior high school level and to encourage students to pursue post
        -secondary studies.
      - >-
        The Amazon Future Engineer Scholarship provides students with an
        opportunity to upgrade their careers with a $7,500 CAD/year scholarship
        available for up to four years.
  - source_sentence: >-
      What is the minimum distance a recipient's hometown must be from Concordia
      University of Edmonton to be eligible for the Alberta Blue Cross Away from
      Home Scholarship?
    sentences:
      - Three awards are available
      - >-
        The recipient’s hometown must be at least 100 kilometres from Concordia
        University of Edmonton.
      - 'Application Deadline: September 1'
  - source_sentence: >-
      According to the selection criteria, what level of subjects are used to
      determine the academic standing of a potential Alberta Blue Cross Away
      from Home Scholarship recipient?
    sentences:
      - >-
        Selection is ba sed on the academic standing of 30 -level subjects used
        for admission.
      - >-
        These eligible and ineligible lists are not exhaustive. Doubts about the
        eligibility of expenses should be directed to the ORI’s Research
        Administration Service s (RAS): ras@concordia.ab.ca .
      - '*Value: $11000 Master’s; $14,000 Doctoral'
  - source_sentence: >-
      According to the text, how many days does a grant recipient have to submit
      a final report after the grant ends?
    sentences:
      - >-
        All Fall grant recipients are expected to submit an abstract to present
        an oral and/or poster presentation of their work, either in its
        progression or final stage.
      - >-
        a business program offered by an Alberta college, polytechnic, or
        university that offers the prerequisite courses required for entrance
        into the CPA Professional Education Program (CPA PEP).
      - >-
        The applicant is required to complete and submit a final report within 5
        days of the end of the grant.
  - source_sentence: >-
      In what format should applicants acknowledge the funding provided by
      Concordia University of Edmonton for their Student Project Grant?
    sentences:
      - >-
        All oral or poster presentations, publications, including public
        messages, arising from research supported by CUE grants must acknowledge
        the support of the institution. Acknowledgement can be in the written
        format, such as " This research is funded by the generous support of
        Concordia University of Edmonton through their CUE Student Research
        Project Grants program ", or similar phrasing.
      - >-
        This $1,000 scholarship is awarded to post -secondary students who have
        completed at least one year towards their Bachelor of Science with a
        focus on Computer Science, achieved an average GPA of 3.5 or higher, and
        are still enrolled in post -secondary studie s.
      - >-
        The recipient will be selected based on the highest grade in MARK320. In
        the event of a tie, preference will be given to the student with the
        highest cumulative GPA.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.5555555555555556
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5555555555555556
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5555555555555556
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8214210289682637
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7592592592592592
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7592592592592592
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.4444444444444444
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8888888888888888
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4444444444444444
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2962962962962963
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4444444444444444
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8888888888888888
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7678413135022636
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6888888888888889
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6888888888888889
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.4444444444444444
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4444444444444444
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4444444444444444
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7658654734127082
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6851851851851851
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6851851851851851
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.4444444444444444
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8888888888888888
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8888888888888888
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8888888888888888
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4444444444444444
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2962962962962963
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17777777777777778
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08888888888888889
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4444444444444444
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8888888888888888
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8888888888888888
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8888888888888888
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7103099178571526
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6481481481481483
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6521164021164021
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6666666666666666
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6666666666666666
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7777777777777778
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8888888888888888
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6666666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2222222222222222
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.15555555555555556
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08888888888888889
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6666666666666666
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7777777777777778
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8888888888888888
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7515566546007473
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7103174603174602
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.71494708994709
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from thenlper/gte-base on the json 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: thenlper/gte-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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})
  (2): Normalize()
)

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("TatvaRA/gte-base-finetuned-schorlaships-matryonshka")
# Run inference
sentences = [
    'In what format should applicants acknowledge the funding provided by Concordia University of Edmonton for their Student Project Grant?',
    'All oral or poster presentations, publications, including public messages, arising from research supported by CUE grants must acknowledge the support of the institution. Acknowledgement can be in the written format, such as " This research is funded by the generous support of Concordia University of Edmonton through their CUE Student Research Project Grants program ", or similar phrasing.',
    'The recipient will be selected based on the highest grade in MARK320. In the event of a tie, preference will be given to the student with the highest cumulative GPA.',
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.5556
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.5556
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.5556
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8214
cosine_mrr@10	0.7593
cosine_map@100	0.7593

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.4444
cosine_accuracy@3	0.8889
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.4444
cosine_precision@3	0.2963
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.4444
cosine_recall@3	0.8889
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.7678
cosine_mrr@10	0.6889
cosine_map@100	0.6889

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.4444
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.4444
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.4444
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.7659
cosine_mrr@10	0.6852
cosine_map@100	0.6852

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.4444
cosine_accuracy@3	0.8889
cosine_accuracy@5	0.8889
cosine_accuracy@10	0.8889
cosine_precision@1	0.4444
cosine_precision@3	0.2963
cosine_precision@5	0.1778
cosine_precision@10	0.0889
cosine_recall@1	0.4444
cosine_recall@3	0.8889
cosine_recall@5	0.8889
cosine_recall@10	0.8889
cosine_ndcg@10	0.7103
cosine_mrr@10	0.6481
cosine_map@100	0.6521

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.6667
cosine_accuracy@3	0.6667
cosine_accuracy@5	0.7778
cosine_accuracy@10	0.8889
cosine_precision@1	0.6667
cosine_precision@3	0.2222
cosine_precision@5	0.1556
cosine_precision@10	0.0889
cosine_recall@1	0.6667
cosine_recall@3	0.6667
cosine_recall@5	0.7778
cosine_recall@10	0.8889
cosine_ndcg@10	0.7516
cosine_mrr@10	0.7103
cosine_map@100	0.7149

Training Details

Training Dataset

json

Dataset: json
Size: 73 training samples
Columns: anchor and positive
Approximate statistics based on the first 73 samples:
anchor positive
type string string
details
min: 14 tokens
mean: 23.0 tokens
max: 41 tokens

min: 6 tokens
mean: 32.74 tokens
max: 346 tokens

	anchor	positive
type	string	string
details	min: 14 tokens mean: 23.0 tokens max: 41 tokens	min: 6 tokens mean: 32.74 tokens max: 346 tokens

Samples:

anchor	positive
`What specific type of students are the Alberta Innovates Graduate Student Scholarships designed to support?`	`The Alberta Innovates Graduate Student Scholarships support academically superior graduate students who are receiving training and conducting research in areas that are strategically important to Alberta’s economy.`
`What is the specific date by which students must submit their reports for the Spring 2025 grant period?`	`Report due date April 20th (5 days post grant closure)`
`In what format should applicants acknowledge the funding provided by Concordia University of Edmonton for their Student Project Grant?`	`All oral or poster presentations, publications, including public messages, arising from research supported by CUE grants must acknowledge the support of the institution. Acknowledgement can be in the written format, such as " This research is funded by the generous support of Concordia University of Edmonton through their CUE Student Research Project Grants program ", or similar phrasing.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_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: 4
max_steps: -1
lr_scheduler_type: cosine
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_fused
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
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
1.0	1	0.7249	0.7249	0.7473	0.7026	0.6686
2.0	2	0.7619	0.7249	0.7533	0.7026	0.7480
3.0	3	0.7804	0.7619	0.7659	0.7103	0.7496
4.0	4	0.8214	0.7678	0.7659	0.7103	0.7516

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 1.5.2
Datasets: 2.19.1
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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}