|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
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_ndcg@100 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:10000 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Enzalutamide ( brand name Xtandi ) is a synthetic non-steroidal |
|
antiandrogen ( NSAA ) which was developed by the pharmaceutical company Medivation |
|
for the treatment of metastatic , castration-resistant prostate cancer . Medivation |
|
has reported up to an 89 % decrease in serum prostate specific antigen ( PSA ) |
|
levels after a month of taking the drug . Research suggests that enzalutamide |
|
may also be effective in the treatment of certain types of breast cancer . In |
|
August 2012 , the United States ( U.S. ) Food and Drug Administration ( FDA ) |
|
approved enzalutamide for the treatment of castration-resistant prostate cancer |
|
. |
|
sentences: |
|
- what type of cancer is enzalutamide |
|
- who is simon cho |
|
- who is dr william farone |
|
- source_sentence: Sohel Rana is a Bangladeshi footballer who plays as a midfielder |
|
. He currently plays for Sheikh Jamal Dhanmondi Club . |
|
sentences: |
|
- who is sohel rana |
|
- who is olympicos |
|
- who is roberto laserna |
|
- source_sentence: Qarah Qayeh ( قره قيه , also Romanized as Qareh Qīyeh ) is a village |
|
in Chaharduli Rural District , Keshavarz District , Shahin Dezh County , West |
|
Azerbaijan Province , Iran . At the 2006 census , its population was 465 , in |
|
93 families . |
|
sentences: |
|
- what was the knoxville riot |
|
- what language is kbif |
|
- where is qarah qayeh |
|
- source_sentence: Martin Severin Janus From ( 8 April 1828 -- 6 May 1895 ) was a |
|
Danish chess master . Born in Nakskov , From received his first education at |
|
the grammar school of Nykøbing Falster . He entered the army as a volunteer during |
|
the Prussian-Danish War ( Schleswig-Holstein War of Succession ) , where he served |
|
in the brigade of Major-General Olaf Rye and partook in the Battle of Fredericia |
|
on July 6 , 1849 . After the war From settled in Copenhagen . He was employed |
|
by the Statistical Bureau , where he met Magnus Oscar Møllerstrøm , then the strongest |
|
chess player in Copenhagen . Next , he worked in the central office for prison |
|
management , and in 1890 he became an inspector of the penitentiary of Christianshavn |
|
. In 1891 he received the order Ridder af Dannebrog ( `` Knight of the Danish |
|
cloth '' , i.e. flag of Denmark ) , which is the second highest of Danish orders |
|
. In 1895 Severin From died of cancer . He is interred at Vestre Cemetery , |
|
Copenhagen . |
|
sentences: |
|
- when did martin from die |
|
- what is hymenoxys lemmonii |
|
- where is macomb square il |
|
- source_sentence: The Recession of 1937 -- 1938 was an economic downturn that occurred |
|
during the Great Depression in the United States . By the spring of 1937 , production |
|
, profits , and wages had regained their 1929 levels . Unemployment remained high |
|
, but it was slightly lower than the 25 % rate seen in 1933 . The American economy |
|
took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 |
|
. Industrial production declined almost 30 percent and production of durable goods |
|
fell even faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 |
|
. Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels |
|
. Producers reduced their expenditures on durable goods , and inventories declined |
|
, but personal income was only 15 % lower than it had been at the peak in 1937 |
|
. In most sectors , hourly earnings continued to rise throughout the recession |
|
, which partly compensated for the reduction in the number of hours worked . As |
|
unemployment rose , consumers expenditures declined , thereby leading to further |
|
cutbacks in production . |
|
sentences: |
|
- when did the great depression peak in the u.s. economy? |
|
- what is tom mount's specialty |
|
- where is poulton |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.906 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.954 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.962 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.975 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.906 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.31799999999999995 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19240000000000004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09750000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.906 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.954 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.962 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.975 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9422297521305668 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@100 |
|
value: 0.9458947974911144 |
|
name: Cosine Ndcg@100 |
|
- type: cosine_mrr@10 |
|
value: 0.9315763888888889 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9323383888065935 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### 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': True}) 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("MugheesAwan11/bge-base-climate_fever-dataset-10k-2k-e2") |
|
# Run inference |
|
sentences = [ |
|
'The Recession of 1937 -- 1938 was an economic downturn that occurred during the Great Depression in the United States . By the spring of 1937 , production , profits , and wages had regained their 1929 levels . Unemployment remained high , but it was slightly lower than the 25 % rate seen in 1933 . The American economy took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 . Industrial production declined almost 30 percent and production of durable goods fell even faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels . Producers reduced their expenditures on durable goods , and inventories declined , but personal income was only 15 % lower than it had been at the peak in 1937 . In most sectors , hourly earnings continued to rise throughout the recession , which partly compensated for the reduction in the number of hours worked . As unemployment rose , consumers expenditures declined , thereby leading to further cutbacks in production .', |
|
'when did the great depression peak in the u.s. economy?', |
|
'where is poulton', |
|
] |
|
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 |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.906 | |
|
| cosine_accuracy@3 | 0.954 | |
|
| cosine_accuracy@5 | 0.962 | |
|
| cosine_accuracy@10 | 0.975 | |
|
| cosine_precision@1 | 0.906 | |
|
| cosine_precision@3 | 0.318 | |
|
| cosine_precision@5 | 0.1924 | |
|
| cosine_precision@10 | 0.0975 | |
|
| cosine_recall@1 | 0.906 | |
|
| cosine_recall@3 | 0.954 | |
|
| cosine_recall@5 | 0.962 | |
|
| cosine_recall@10 | 0.975 | |
|
| cosine_ndcg@10 | 0.9422 | |
|
| cosine_ndcg@100 | 0.9459 | |
|
| cosine_mrr@10 | 0.9316 | |
|
| **cosine_map@100** | **0.9323** | |
|
|
|
<!-- |
|
## 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: 10,000 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 2 tokens</li><li>mean: 116.45 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.6 tokens</li><li>max: 19 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------| |
|
| <code>Professor Maurice Cockrill , RA , FBA ( 8 October 1936 -- 1 December 2013 ) was a British painter and poet . Born in Hartlepool , County Durham , he studied at Wrexham School of Art , north east Wales , then Denbigh Technical College and later the University of Reading from 1960 -- 64 . In Liverpool , where he lived for nearly twenty years from 1964 , he taught at Liverpool College of Art and Liverpool Polytechnic . He was a central figure in Liverpool 's artistic life , regularly exhibiting at the Walker Art Gallery , before his departure for London in 1982 . Cockrill 's Liverpool work was in line with that of John Baum , Sam Walsh and Adrian Henri , employing Pop and Photo-Realist styles , but later he moved towards Romantic Expressionism , as it was shown in his retrospective at the Walker Art Gallery , Liverpool in 1995 . His poetry was published in magazines such as `` Ambit '' and `` Poetry Review '' . He was formerly the Keeper of the Royal Academy , and as such managed the RA Schools of the Establishment as well as being a member of the Board and Executive Committee .</code> | <code>who was maurice cockrill</code> | |
|
| <code>Nowa Dąbrowa -LSB- ` nowa-dom ` browa -RSB- is a village in the administrative district of Gmina Kwilcz , within Międzychód County , Greater Poland Voivodeship , in west-central Poland . It lies approximately 16 km south-east of Międzychód and 59 km west of the regional capital Poznań . The village has a population of 40 .</code> | <code>where is nowa dbrowa poland</code> | |
|
| <code>Hymenoxys lemmonii is a species of flowering plant in the daisy family known by the common names Lemmon 's rubberweed , Lemmon 's bitterweed , and alkali hymenoxys . It is native to the western United States in and around the Great Basin in Utah , Nevada , northern California , and southeastern Oregon . Hymenoxys lemmonii is a biennial or perennial herb with one or more branching stems growing erect to a maximum height near 50 centimeters ( 20 inches ) . It produces straight , dark green leaves up to 9 centimeters ( 3.6 inches ) long and divided into a number of narrow , pointed lobes . The foliage and stem may be hairless to quite woolly . The daisy-like flower head is generally at least 1.5 centimeters ( 0.6 inches ) wide , with a center of 50 -- 125 thick golden disc florets and a shaggy fringe of 9 -- 12 golden ray florets . The species is named for John Gill Lemmon , husband of prominent American botanist Sarah Plummer Lemmon .</code> | <code>what is hymenoxys lemmonii</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 |
|
], |
|
"matryoshka_weights": [ |
|
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 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 2 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `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`: 1 |
|
- `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`: 2 |
|
- `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`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_map@100 | |
|
|:-------:|:-------:|:-------------:|:----------------------:| |
|
| 0.0319 | 10 | 0.1626 | - | |
|
| 0.0639 | 20 | 0.1168 | - | |
|
| 0.0958 | 30 | 0.0543 | - | |
|
| 0.1278 | 40 | 0.1227 | - | |
|
| 0.1597 | 50 | 0.061 | - | |
|
| 0.1917 | 60 | 0.0537 | - | |
|
| 0.2236 | 70 | 0.0693 | - | |
|
| 0.2556 | 80 | 0.1115 | - | |
|
| 0.2875 | 90 | 0.0541 | - | |
|
| 0.3195 | 100 | 0.0774 | - | |
|
| 0.3514 | 110 | 0.0639 | - | |
|
| 0.3834 | 120 | 0.0639 | - | |
|
| 0.4153 | 130 | 0.0567 | - | |
|
| 0.4473 | 140 | 0.0385 | - | |
|
| 0.4792 | 150 | 0.0452 | - | |
|
| 0.5112 | 160 | 0.0641 | - | |
|
| 0.5431 | 170 | 0.042 | - | |
|
| 0.5751 | 180 | 0.0243 | - | |
|
| 0.6070 | 190 | 0.0405 | - | |
|
| 0.6390 | 200 | 0.062 | - | |
|
| 0.6709 | 210 | 0.0366 | - | |
|
| 0.7029 | 220 | 0.0399 | - | |
|
| 0.7348 | 230 | 0.0382 | - | |
|
| 0.7668 | 240 | 0.0387 | - | |
|
| 0.7987 | 250 | 0.0575 | - | |
|
| 0.8307 | 260 | 0.0391 | - | |
|
| 0.8626 | 270 | 0.0776 | - | |
|
| 0.8946 | 280 | 0.0258 | - | |
|
| 0.9265 | 290 | 0.0493 | - | |
|
| 0.9585 | 300 | 0.037 | - | |
|
| 0.9904 | 310 | 0.0499 | - | |
|
| **1.0** | **313** | **-** | **0.9397** | |
|
| 0.0319 | 10 | 0.0111 | - | |
|
| 0.0639 | 20 | 0.007 | - | |
|
| 0.0958 | 30 | 0.0023 | - | |
|
| 0.1278 | 40 | 0.0109 | - | |
|
| 0.1597 | 50 | 0.0046 | - | |
|
| 0.1917 | 60 | 0.0043 | - | |
|
| 0.2236 | 70 | 0.0037 | - | |
|
| 0.2556 | 80 | 0.0118 | - | |
|
| 0.2875 | 90 | 0.0026 | - | |
|
| 0.3195 | 100 | 0.0079 | - | |
|
| 0.3514 | 110 | 0.0045 | - | |
|
| 0.3834 | 120 | 0.0163 | - | |
|
| 0.4153 | 130 | 0.0058 | - | |
|
| 0.4473 | 140 | 0.0154 | - | |
|
| 0.4792 | 150 | 0.0051 | - | |
|
| 0.5112 | 160 | 0.0152 | - | |
|
| 0.5431 | 170 | 0.0058 | - | |
|
| 0.5751 | 180 | 0.0041 | - | |
|
| 0.6070 | 190 | 0.0118 | - | |
|
| 0.6390 | 200 | 0.0165 | - | |
|
| 0.6709 | 210 | 0.0088 | - | |
|
| 0.7029 | 220 | 0.014 | - | |
|
| 0.7348 | 230 | 0.0195 | - | |
|
| 0.7668 | 240 | 0.024 | - | |
|
| 0.7987 | 250 | 0.0472 | - | |
|
| 0.8307 | 260 | 0.0341 | - | |
|
| 0.8626 | 270 | 0.0684 | - | |
|
| 0.8946 | 280 | 0.0193 | - | |
|
| 0.9265 | 290 | 0.0488 | - | |
|
| 0.9585 | 300 | 0.0388 | - | |
|
| 0.9904 | 310 | 0.0485 | - | |
|
| **1.0** | **313** | **-** | **0.9349** | |
|
| 1.0224 | 320 | 0.0119 | - | |
|
| 1.0543 | 330 | 0.013 | - | |
|
| 1.0863 | 340 | 0.0024 | - | |
|
| 1.1182 | 350 | 0.012 | - | |
|
| 1.1502 | 360 | 0.0042 | - | |
|
| 1.1821 | 370 | 0.0091 | - | |
|
| 1.2141 | 380 | 0.0041 | - | |
|
| 1.2460 | 390 | 0.0096 | - | |
|
| 1.2780 | 400 | 0.0053 | - | |
|
| 1.3099 | 410 | 0.0043 | - | |
|
| 1.3419 | 420 | 0.0059 | - | |
|
| 1.3738 | 430 | 0.0138 | - | |
|
| 1.4058 | 440 | 0.0132 | - | |
|
| 1.4377 | 450 | 0.0124 | - | |
|
| 1.4696 | 460 | 0.0049 | - | |
|
| 1.5016 | 470 | 0.0043 | - | |
|
| 1.5335 | 480 | 0.0045 | - | |
|
| 1.5655 | 490 | 0.0037 | - | |
|
| 1.5974 | 500 | 0.0081 | - | |
|
| 1.6294 | 510 | 0.0038 | - | |
|
| 1.6613 | 520 | 0.0055 | - | |
|
| 1.6933 | 530 | 0.003 | - | |
|
| 1.7252 | 540 | 0.0022 | - | |
|
| 1.7572 | 550 | 0.0042 | - | |
|
| 1.7891 | 560 | 0.0158 | - | |
|
| 1.8211 | 570 | 0.0088 | - | |
|
| 1.8530 | 580 | 0.0154 | - | |
|
| 1.8850 | 590 | 0.0057 | - | |
|
| 1.9169 | 600 | 0.0086 | - | |
|
| 1.9489 | 610 | 0.0069 | - | |
|
| 1.9808 | 620 | 0.0076 | - | |
|
| 2.0 | 626 | - | 0.9323 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
