|
--- |
|
base_model: Snowflake/snowflake-arctic-embed-m |
|
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 |
|
- dot_accuracy@1 |
|
- dot_accuracy@3 |
|
- dot_accuracy@5 |
|
- dot_accuracy@10 |
|
- dot_precision@1 |
|
- dot_precision@3 |
|
- dot_precision@5 |
|
- dot_precision@10 |
|
- dot_recall@1 |
|
- dot_recall@3 |
|
- dot_recall@5 |
|
- dot_recall@10 |
|
- dot_ndcg@10 |
|
- dot_mrr@10 |
|
- dot_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:600 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Which entities are responsible for enforcing the requirements discussed |
|
in the context? |
|
sentences: |
|
- 'ALGORITHMIC DISCRIMINATION PROTECTIONS |
|
|
|
You should not face discrimination by algorithms and systems should be used and |
|
designed in' |
|
- 'SECTION TITLE |
|
|
|
HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK |
|
|
|
You should be able to opt out, where appropriate, and have access to a person |
|
who can quickly' |
|
- requirements of the Federal agencies that enforce them. These principles are not |
|
intended to, and do not, |
|
- source_sentence: How is safety addressed in the development process according to |
|
the context? |
|
sentences: |
|
- "TABLE OF CONTENTS\nFROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE\ |
|
\ BLUEPRINT \nFOR AN AI BILL OF RIGHTS \n \nUSING THIS TECHNICAL COMPANION\n \n\ |
|
SAFE AND EFFECTIVE SYSTEMS" |
|
- "stemming from unintended, yet foreseeable, uses or \n \n \n \n \n \n \n \nSECTION\ |
|
\ TITLE\nBLUEPRINT FOR AN\nSAFE AND E \nYou should be protected from unsafe or\ |
|
\ \ndeveloped with consultation from diverse" |
|
- tion or implemented under existing U.S. laws. For example, government surveillance, |
|
and data search and |
|
- source_sentence: How should the deployment of automated systems be aligned with |
|
the principles for protecting the American public? |
|
sentences: |
|
- "public and private sector contexts; \nEqual opportunities, including equitable\ |
|
\ access to education, housing, credit, employment, and other \nprograms; or," |
|
- use, and deployment of automated systems to protect the rights of the American |
|
public in the age of artificial |
|
- five principles that should guide the design, use, and deployment of automated |
|
systems to protect the American |
|
- source_sentence: Who should designers, developers, and deployers of automated systems |
|
seek permission from? |
|
sentences: |
|
- This important progress must not come at the price of civil rights or democratic |
|
values, foundational American |
|
- a blueprint for building and deploying automated systems that are aligned with |
|
democratic values and protect |
|
- context is collected. Designers, developers, and deployers of automated systems |
|
should seek your permission |
|
- source_sentence: What changes are suggested for notice-and-choice practices regarding |
|
broad uses of data? |
|
sentences: |
|
- mated systems, and researchers developing innovative guardrails. Advocates, researchers, |
|
and government |
|
- tial to meaningfully impact rights, opportunities, or access. Additionally, this |
|
framework does not analyze or |
|
- understand notice-and-choice practices for broad uses of data should be changed. |
|
Enhanced protections and |
|
model-index: |
|
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: Unknown |
|
type: unknown |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.99 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.99 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33000000000000007 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19799999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.99 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.99 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9601170111547646 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9464285714285714 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9464285714285714 |
|
name: Cosine Map@100 |
|
- type: dot_accuracy@1 |
|
value: 0.9 |
|
name: Dot Accuracy@1 |
|
- type: dot_accuracy@3 |
|
value: 0.99 |
|
name: Dot Accuracy@3 |
|
- type: dot_accuracy@5 |
|
value: 0.99 |
|
name: Dot Accuracy@5 |
|
- type: dot_accuracy@10 |
|
value: 1.0 |
|
name: Dot Accuracy@10 |
|
- type: dot_precision@1 |
|
value: 0.9 |
|
name: Dot Precision@1 |
|
- type: dot_precision@3 |
|
value: 0.33000000000000007 |
|
name: Dot Precision@3 |
|
- type: dot_precision@5 |
|
value: 0.19799999999999998 |
|
name: Dot Precision@5 |
|
- type: dot_precision@10 |
|
value: 0.09999999999999998 |
|
name: Dot Precision@10 |
|
- type: dot_recall@1 |
|
value: 0.9 |
|
name: Dot Recall@1 |
|
- type: dot_recall@3 |
|
value: 0.99 |
|
name: Dot Recall@3 |
|
- type: dot_recall@5 |
|
value: 0.99 |
|
name: Dot Recall@5 |
|
- type: dot_recall@10 |
|
value: 1.0 |
|
name: Dot Recall@10 |
|
- type: dot_ndcg@10 |
|
value: 0.9601170111547646 |
|
name: Dot Ndcg@10 |
|
- type: dot_mrr@10 |
|
value: 0.9464285714285714 |
|
name: Dot Mrr@10 |
|
- type: dot_map@100 |
|
value: 0.9464285714285714 |
|
name: Dot Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### 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': True, 'pooling_mode_mean_tokens': False, '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: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("dstampfli/finetuned-snowflake-arctic-embed-m") |
|
# Run inference |
|
sentences = [ |
|
'What changes are suggested for notice-and-choice practices regarding broad uses of data?', |
|
'understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and', |
|
'tial to meaningfully impact rights, opportunities, or access. Additionally, this framework does not analyze or', |
|
] |
|
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] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.9 | |
|
| cosine_accuracy@3 | 0.99 | |
|
| cosine_accuracy@5 | 0.99 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9 | |
|
| cosine_precision@3 | 0.33 | |
|
| cosine_precision@5 | 0.198 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.9 | |
|
| cosine_recall@3 | 0.99 | |
|
| cosine_recall@5 | 0.99 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9601 | |
|
| cosine_mrr@10 | 0.9464 | |
|
| **cosine_map@100** | **0.9464** | |
|
| dot_accuracy@1 | 0.9 | |
|
| dot_accuracy@3 | 0.99 | |
|
| dot_accuracy@5 | 0.99 | |
|
| dot_accuracy@10 | 1.0 | |
|
| dot_precision@1 | 0.9 | |
|
| dot_precision@3 | 0.33 | |
|
| dot_precision@5 | 0.198 | |
|
| dot_precision@10 | 0.1 | |
|
| dot_recall@1 | 0.9 | |
|
| dot_recall@3 | 0.99 | |
|
| dot_recall@5 | 0.99 | |
|
| dot_recall@10 | 1.0 | |
|
| dot_ndcg@10 | 0.9601 | |
|
| dot_mrr@10 | 0.9464 | |
|
| dot_map@100 | 0.9464 | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 600 training samples |
|
* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
|
* Approximate statistics based on the first 600 samples: |
|
| | sentence_0 | sentence_1 | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 9 tokens</li><li>mean: 17.15 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 23.93 tokens</li><li>max: 46 tokens</li></ul> | |
|
* Samples: |
|
| sentence_0 | sentence_1 | |
|
|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>What is the purpose of the AI Bill of Rights mentioned in the context?</code> | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> | |
|
| <code>When was the Blueprint for an AI Bill of Rights published?</code> | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> | |
|
| <code>What is the main purpose of the Blueprint for an AI Bill of Rights?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"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`: steps |
|
- `per_device_train_batch_size`: 20 |
|
- `per_device_eval_batch_size`: 20 |
|
- `num_train_epochs`: 5 |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 20 |
|
- `per_device_eval_batch_size`: 20 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_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 |
|
- `num_train_epochs`: 5 |
|
- `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`: 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 |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | cosine_map@100 | |
|
|:------:|:----:|:--------------:| |
|
| 1.0 | 30 | 0.9458 | |
|
| 1.6667 | 50 | 0.9461 | |
|
| 2.0 | 60 | 0.9461 | |
|
| 3.0 | 90 | 0.9463 | |
|
| 3.3333 | 100 | 0.9463 | |
|
| 4.0 | 120 | 0.9464 | |
|
| 5.0 | 150 | 0.9464 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.9 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 3.0.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@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 |
|
```bibtex |
|
@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 |
|
```bibtex |
|
@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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