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recomb-pred-bge-large-en

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:784827
loss:ContrastiveLoss
text-embeddings-inference
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:784827
- loss:ContrastiveLoss
base_model: BAAI/bge-large-en-v1.5
widget:
- source_sentence: >-
Represent this sentence for searching relevant passages: Existing methods
for anomaly detection on dynamic graphs struggle with capturing complex time
information in graph structures and generating effective negative samples
for unsupervised learning. These challenges highlight the need for improved
methodologies that can address the limitations of current approaches in this
field.We suggest combining 'a message-passing framework' and
sentences:
- a single global model
- videos
- sequential polygon generation
- source_sentence: >-
Represent this sentence for searching relevant passages: The study addresses
the need for effective tools that allow both novice and expert users to
analyze the diversity of news coverage about events. It highlights the
importance of tailoring the interface to accommodate non-expert users while
also considering the insights of journalism-savvy users, indicating a gap in
existing systems that cater to varying levels of expertise in news
analysis.We suggest combining 'a coordinated visualization interface
tailored for visualization non-expert users' and
sentences:
- worst-case resource analysis
- Graph Convolution Networks
- a text encoder
- source_sentence: >-
Represent this sentence for searching relevant passages: The accuracy of
pixel flows is crucial for achieving high-quality video enhancement, yet
most prior works focus on estimating dense flows that are generally less
robust and computationally expensive. This highlights a gap in existing
methodologies that fail to prioritize accuracy over density, necessitating a
more efficient approach to flow estimation for video enhancement tasks.We
suggest combining 'sparse point cloud data' and
sentences:
- a Temporal Eigenvalue Loss
- diffusion models
- explicit 3D representations, such as polygonal meshes
- source_sentence: >-
Represent this sentence for searching relevant passages: The traditional
frame of discernment lacks a crucial factor, the sequence of propositions,
which limits the effectiveness of existing methods to measure uncertainty.
This gap highlights the need for a more comprehensive approach that can
better represent the relationships between the elements of the frame of
discernment.We suggest 'combine the order of propositions and the mass of
them' inspired by
sentences:
- >-
the traditional matching-optimization methods where matching is introduced
to handle large displacements before energy-based optimizations
- encoder-decoder models
- >-
In another vein, researchers propose new attention augmentation methods to
make transformers more accurate, efficient and interpretable
- source_sentence: >-
Represent this sentence for searching relevant passages: The study addresses
the need for effective time series forecasting methods to estimate the
spread of epidemics, particularly in light of the resurgence of COVID-19
cases. It highlights the importance of accurately modeling both linear and
non-linear features of epidemic data to provide state authorities and health
officials with reliable short-term forecasts and strategies.We suggest
combining 'ARIMA' and
sentences:
- Transformers
- a traditional feature-mixed branch
- >-
the human brain is able to efficiently learn effective control strategies
using limited resources
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: cc
datasets:
- noystl/Recombination-Pred
language:
- en
---
# SentenceTransformer based on BAAI/bge-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
"Represent this sentence for searching relevant passages: The study addresses the need for effective time series forecasting methods to estimate the spread of epidemics, particularly in light of the resurgence of COVID-19 cases. It highlights the importance of accurately modeling both linear and non-linear features of epidemic data to provide state authorities and health officials with reliable short-term forecasts and strategies.We suggest combining 'ARIMA' and ",
'Transformers',
'the human brain is able to efficiently learn effective control strategies using limited resources',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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.*
-->
<!--
## 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: 784,827 training samples
* Columns: <code>query</code>, <code>answer</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | label |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 66 tokens</li><li>mean: 83.86 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.63 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~96.70%</li><li>1: ~3.30%</li></ul> |
* Samples:
| query | answer | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|:---------------|
| <code>Represent this sentence for searching relevant passages: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and </code> | <code>a multilayer perceptron</code> | <code>1</code> |
| <code>Represent this sentence for searching relevant passages: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and </code> | <code>global expression information</code> | <code>0</code> |
| <code>Represent this sentence for searching relevant passages: The study addresses the challenge of action segmentation under weak supervision, where the available ground truth only indicates the presence of actions without providing their temporal ordering or occurrence timing in training videos. This limitation necessitates the development of a method to generate pseudo-ground truth for effective training and improve performance in action segmentation and alignment tasks.We suggest combining 'a Hidden Markov Model' and </code> | <code>some relevant physical parameters</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `learning_rate`: 2.3351317368662443e-06
- `warmup_ratio`: 0.11883406097525227
- `bf16`: True
- `prompts`: {'query': 'Represent this sentence for searching relevant passages: '}
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `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`: 2.3351317368662443e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.11883406097525227
- `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`: 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`: 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`: {'query': 'Represent this sentence for searching relevant passages: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0082 | 100 | 0.0051 |
| 0.0163 | 200 | 0.0038 |
| 0.0245 | 300 | 0.0037 |
| 0.0326 | 400 | 0.0036 |
| 0.0408 | 500 | 0.0046 |
| 0.0489 | 600 | 0.0035 |
| 0.0571 | 700 | 0.0035 |
| 0.0652 | 800 | 0.0034 |
| 0.0734 | 900 | 0.0044 |
| 0.0815 | 1000 | 0.0034 |
| 0.0897 | 1100 | 0.0035 |
| 0.0979 | 1200 | 0.0034 |
| 0.1060 | 1300 | 0.0034 |
| 0.1142 | 1400 | 0.0045 |
| 0.1223 | 1500 | 0.0034 |
| 0.1305 | 1600 | 0.0034 |
| 0.1386 | 1700 | 0.0033 |
| 0.1468 | 1800 | 0.0043 |
| 0.1549 | 1900 | 0.0034 |
| 0.1631 | 2000 | 0.0033 |
| 0.1712 | 2100 | 0.0032 |
| 0.1794 | 2200 | 0.0033 |
| 0.1876 | 2300 | 0.0044 |
| 0.1957 | 2400 | 0.0033 |
| 0.2039 | 2500 | 0.0034 |
| 0.2120 | 2600 | 0.0033 |
| 0.2202 | 2700 | 0.0042 |
| 0.2283 | 2800 | 0.0034 |
| 0.2365 | 2900 | 0.0033 |
| 0.2446 | 3000 | 0.0033 |
| 0.2528 | 3100 | 0.0036 |
| 0.2609 | 3200 | 0.0039 |
| 0.2691 | 3300 | 0.0033 |
| 0.2773 | 3400 | 0.0032 |
| 0.2854 | 3500 | 0.0034 |
| 0.2936 | 3600 | 0.0041 |
| 0.3017 | 3700 | 0.0031 |
| 0.3099 | 3800 | 0.0032 |
| 0.3180 | 3900 | 0.0031 |
| 0.3262 | 4000 | 0.004 |
| 0.3343 | 4100 | 0.0034 |
| 0.3425 | 4200 | 0.003 |
| 0.3506 | 4300 | 0.0032 |
| 0.3588 | 4400 | 0.0032 |
| 0.3670 | 4500 | 0.004 |
| 0.3751 | 4600 | 0.0031 |
| 0.3833 | 4700 | 0.0033 |
| 0.3914 | 4800 | 0.0031 |
| 0.3996 | 4900 | 0.004 |
| 0.4077 | 5000 | 0.0032 |
| 0.4159 | 5100 | 0.0031 |
| 0.4240 | 5200 | 0.0031 |
| 0.4322 | 5300 | 0.0031 |
| 0.4403 | 5400 | 0.0039 |
| 0.4485 | 5500 | 0.003 |
| 0.4567 | 5600 | 0.003 |
| 0.4648 | 5700 | 0.0031 |
| 0.4730 | 5800 | 0.0038 |
| 0.4811 | 5900 | 0.0031 |
| 0.4893 | 6000 | 0.0032 |
| 0.4974 | 6100 | 0.0031 |
| 0.5056 | 6200 | 0.0033 |
| 0.5137 | 6300 | 0.0035 |
| 0.5219 | 6400 | 0.0031 |
| 0.5300 | 6500 | 0.0031 |
| 0.5382 | 6600 | 0.0031 |
| 0.5464 | 6700 | 0.0038 |
| 0.5545 | 6800 | 0.0031 |
| 0.5627 | 6900 | 0.003 |
| 0.5708 | 7000 | 0.0029 |
| 0.5790 | 7100 | 0.0037 |
| 0.5871 | 7200 | 0.0033 |
| 0.5953 | 7300 | 0.0031 |
| 0.6034 | 7400 | 0.003 |
| 0.6116 | 7500 | 0.003 |
| 0.6198 | 7600 | 0.004 |
| 0.6279 | 7700 | 0.0031 |
| 0.6361 | 7800 | 0.0031 |
| 0.6442 | 7900 | 0.0031 |
| 0.6524 | 8000 | 0.0039 |
| 0.6605 | 8100 | 0.0029 |
| 0.6687 | 8200 | 0.003 |
| 0.6768 | 8300 | 0.0029 |
| 0.6850 | 8400 | 0.0028 |
| 0.6931 | 8500 | 0.0036 |
| 0.7013 | 8600 | 0.0031 |
| 0.7095 | 8700 | 0.0029 |
| 0.7176 | 8800 | 0.0028 |
| 0.7258 | 8900 | 0.0035 |
| 0.7339 | 9000 | 0.0033 |
| 0.7421 | 9100 | 0.003 |
| 0.7502 | 9200 | 0.0028 |
| 0.7584 | 9300 | 0.0029 |
| 0.7665 | 9400 | 0.0035 |
| 0.7747 | 9500 | 0.003 |
| 0.7828 | 9600 | 0.0028 |
| 0.7910 | 9700 | 0.0027 |
| 0.7992 | 9800 | 0.0034 |
| 0.8073 | 9900 | 0.0032 |
| 0.8155 | 10000 | 0.003 |
| 0.8236 | 10100 | 0.0029 |
| 0.8318 | 10200 | 0.0032 |
| 0.8399 | 10300 | 0.0032 |
| 0.8481 | 10400 | 0.003 |
| 0.8562 | 10500 | 0.0029 |
| 0.8644 | 10600 | 0.0029 |
| 0.8725 | 10700 | 0.0033 |
| 0.8807 | 10800 | 0.003 |
| 0.8889 | 10900 | 0.0029 |
| 0.8970 | 11000 | 0.0028 |
| 0.9052 | 11100 | 0.0035 |
| 0.9133 | 11200 | 0.003 |
| 0.9215 | 11300 | 0.0029 |
| 0.9296 | 11400 | 0.0029 |
| 0.9378 | 11500 | 0.0029 |
| 0.9459 | 11600 | 0.0034 |
| 0.9541 | 11700 | 0.0031 |
| 0.9622 | 11800 | 0.0028 |
| 0.9704 | 11900 | 0.003 |
| 0.9786 | 12000 | 0.0035 |
| 0.9867 | 12100 | 0.0032 |
| 0.9949 | 12200 | 0.003 |
| 1.0030 | 12300 | 0.0033 |
| 1.0112 | 12400 | 0.0029 |
| 1.0193 | 12500 | 0.003 |
| 1.0275 | 12600 | 0.0029 |
| 1.0356 | 12700 | 0.0036 |
| 1.0438 | 12800 | 0.003 |
| 1.0519 | 12900 | 0.0027 |
| 1.0601 | 13000 | 0.0028 |
| 1.0683 | 13100 | 0.0028 |
| 1.0764 | 13200 | 0.0036 |
| 1.0846 | 13300 | 0.0027 |
| 1.0927 | 13400 | 0.0028 |
| 1.1009 | 13500 | 0.0029 |
| 1.1090 | 13600 | 0.0037 |
| 1.1172 | 13700 | 0.0029 |
| 1.1253 | 13800 | 0.0029 |
| 1.1335 | 13900 | 0.0027 |
| 1.1416 | 14000 | 0.0033 |
| 1.1498 | 14100 | 0.003 |
| 1.1580 | 14200 | 0.0027 |
| 1.1661 | 14300 | 0.0028 |
| 1.1743 | 14400 | 0.0026 |
| 1.1824 | 14500 | 0.0036 |
| 1.1906 | 14600 | 0.0028 |
| 1.1987 | 14700 | 0.0027 |
| 1.2069 | 14800 | 0.0029 |
| 1.2150 | 14900 | 0.0035 |
| 1.2232 | 15000 | 0.0027 |
| 1.2313 | 15100 | 0.0027 |
| 1.2395 | 15200 | 0.0027 |
| 1.2477 | 15300 | 0.0028 |
| 1.2558 | 15400 | 0.0035 |
| 1.2640 | 15500 | 0.0027 |
| 1.2721 | 15600 | 0.0027 |
| 1.2803 | 15700 | 0.0027 |
| 1.2884 | 15800 | 0.0037 |
| 1.2966 | 15900 | 0.0027 |
| 1.3047 | 16000 | 0.0027 |
| 1.3129 | 16100 | 0.0027 |
| 1.3210 | 16200 | 0.0028 |
| 1.3292 | 16300 | 0.0033 |
| 1.3374 | 16400 | 0.0026 |
| 1.3455 | 16500 | 0.0025 |
| 1.3537 | 16600 | 0.0028 |
| 1.3618 | 16700 | 0.0034 |
| 1.3700 | 16800 | 0.0027 |
| 1.3781 | 16900 | 0.0026 |
| 1.3863 | 17000 | 0.0027 |
| 1.3944 | 17100 | 0.0033 |
| 1.4026 | 17200 | 0.0027 |
| 1.4107 | 17300 | 0.0027 |
| 1.4189 | 17400 | 0.0026 |
| 1.4271 | 17500 | 0.0027 |
| 1.4352 | 17600 | 0.0034 |
| 1.4434 | 17700 | 0.0027 |
| 1.4515 | 17800 | 0.0025 |
| 1.4597 | 17900 | 0.0027 |
| 1.4678 | 18000 | 0.0031 |
| 1.4760 | 18100 | 0.0027 |
| 1.4841 | 18200 | 0.0027 |
| 1.4923 | 18300 | 0.0027 |
| 1.5004 | 18400 | 0.0027 |
| 1.5086 | 18500 | 0.0031 |
| 1.5168 | 18600 | 0.0025 |
| 1.5249 | 18700 | 0.0026 |
| 1.5331 | 18800 | 0.0027 |
| 1.5412 | 18900 | 0.0035 |
| 1.5494 | 19000 | 0.0025 |
| 1.5575 | 19100 | 0.0027 |
| 1.5657 | 19200 | 0.0026 |
| 1.5738 | 19300 | 0.0028 |
| 1.5820 | 19400 | 0.0032 |
| 1.5901 | 19500 | 0.0025 |
| 1.5983 | 19600 | 0.0027 |
| 1.6065 | 19700 | 0.0026 |
| 1.6146 | 19800 | 0.0034 |
| 1.6228 | 19900 | 0.0027 |
| 1.6309 | 20000 | 0.0027 |
| 1.6391 | 20100 | 0.0028 |
| 1.6472 | 20200 | 0.0031 |
| 1.6554 | 20300 | 0.0028 |
| 1.6635 | 20400 | 0.0025 |
| 1.6717 | 20500 | 0.0025 |
| 1.6798 | 20600 | 0.0026 |
| 1.6880 | 20700 | 0.003 |
| 1.6962 | 20800 | 0.0029 |
| 1.7043 | 20900 | 0.0027 |
| 1.7125 | 21000 | 0.0025 |
| 1.7206 | 21100 | 0.0029 |
| 1.7288 | 21200 | 0.0029 |
| 1.7369 | 21300 | 0.0027 |
| 1.7451 | 21400 | 0.0026 |
| 1.7532 | 21500 | 0.0025 |
| 1.7614 | 21600 | 0.003 |
| 1.7696 | 21700 | 0.0028 |
| 1.7777 | 21800 | 0.0024 |
| 1.7859 | 21900 | 0.0025 |
| 1.7940 | 22000 | 0.003 |
| 1.8022 | 22100 | 0.0026 |
| 1.8103 | 22200 | 0.0027 |
| 1.8185 | 22300 | 0.0027 |
| 1.8266 | 22400 | 0.0026 |
| 1.8348 | 22500 | 0.003 |
| 1.8429 | 22600 | 0.0029 |
| 1.8511 | 22700 | 0.0025 |
| 1.8593 | 22800 | 0.0026 |
| 1.8674 | 22900 | 0.0031 |
| 1.8756 | 23000 | 0.0027 |
| 1.8837 | 23100 | 0.0026 |
| 1.8919 | 23200 | 0.0025 |
| 1.9000 | 23300 | 0.0028 |
| 1.9082 | 23400 | 0.0027 |
| 1.9163 | 23500 | 0.0027 |
| 1.9245 | 23600 | 0.0027 |
| 1.9326 | 23700 | 0.0026 |
| 1.9408 | 23800 | 0.0031 |
| 1.9490 | 23900 | 0.0027 |
| 1.9571 | 24000 | 0.0027 |
| 1.9653 | 24100 | 0.0026 |
| 1.9734 | 24200 | 0.0032 |
| 1.9816 | 24300 | 0.0029 |
| 1.9897 | 24400 | 0.0026 |
| 1.9979 | 24500 | 0.0028 |
| 2.0060 | 24600 | 0.0029 |
| 2.0142 | 24700 | 0.0026 |
| 2.0223 | 24800 | 0.0027 |
| 2.0305 | 24900 | 0.0033 |
| 2.0387 | 25000 | 0.0026 |
| 2.0468 | 25100 | 0.0026 |
| 2.0550 | 25200 | 0.0024 |
| 2.0631 | 25300 | 0.0026 |
| 2.0713 | 25400 | 0.0033 |
| 2.0794 | 25500 | 0.0025 |
| 2.0876 | 25600 | 0.0026 |
| 2.0957 | 25700 | 0.0026 |
| 2.1039 | 25800 | 0.0033 |
| 2.1120 | 25900 | 0.0025 |
| 2.1202 | 26000 | 0.0026 |
| 2.1284 | 26100 | 0.0026 |
| 2.1365 | 26200 | 0.0025 |
| 2.1447 | 26300 | 0.0031 |
| 2.1528 | 26400 | 0.0026 |
| 2.1610 | 26500 | 0.0025 |
| 2.1691 | 26600 | 0.0026 |
| 2.1773 | 26700 | 0.0032 |
| 2.1854 | 26800 | 0.0026 |
| 2.1936 | 26900 | 0.0026 |
| 2.2017 | 27000 | 0.0025 |
| 2.2099 | 27100 | 0.0032 |
| 2.2181 | 27200 | 0.0025 |
| 2.2262 | 27300 | 0.0025 |
| 2.2344 | 27400 | 0.0024 |
| 2.2425 | 27500 | 0.0025 |
| 2.2507 | 27600 | 0.0033 |
| 2.2588 | 27700 | 0.0024 |
| 2.2670 | 27800 | 0.0024 |
| 2.2751 | 27900 | 0.0024 |
| 2.2833 | 28000 | 0.0033 |
| 2.2914 | 28100 | 0.0025 |
| 2.2996 | 28200 | 0.0024 |
| 2.3078 | 28300 | 0.0026 |
| 2.3159 | 28400 | 0.0024 |
| 2.3241 | 28500 | 0.0032 |
| 2.3322 | 28600 | 0.0025 |
| 2.3404 | 28700 | 0.0024 |
| 2.3485 | 28800 | 0.0024 |
| 2.3567 | 28900 | 0.0032 |
| 2.3648 | 29000 | 0.0025 |
| 2.3730 | 29100 | 0.0024 |
| 2.3811 | 29200 | 0.0024 |
| 2.3893 | 29300 | 0.0028 |
| 2.3975 | 29400 | 0.003 |
| 2.4056 | 29500 | 0.0023 |
| 2.4138 | 29600 | 0.0025 |
| 2.4219 | 29700 | 0.0024 |
| 2.4301 | 29800 | 0.0032 |
| 2.4382 | 29900 | 0.0025 |
| 2.4464 | 30000 | 0.0024 |
| 2.4545 | 30100 | 0.0023 |
| 2.4627 | 30200 | 0.003 |
| 2.4708 | 30300 | 0.0024 |
| 2.4790 | 30400 | 0.0025 |
| 2.4872 | 30500 | 0.0025 |
| 2.4953 | 30600 | 0.0025 |
| 2.5035 | 30700 | 0.0031 |
| 2.5116 | 30800 | 0.0022 |
| 2.5198 | 30900 | 0.0024 |
| 2.5279 | 31000 | 0.0024 |
| 2.5361 | 31100 | 0.0032 |
| 2.5442 | 31200 | 0.0024 |
| 2.5524 | 31300 | 0.0023 |
| 2.5605 | 31400 | 0.0025 |
| 2.5687 | 31500 | 0.0024 |
| 2.5769 | 31600 | 0.0031 |
| 2.5850 | 31700 | 0.0024 |
| 2.5932 | 31800 | 0.0024 |
| 2.6013 | 31900 | 0.0024 |
| 2.6095 | 32000 | 0.0031 |
| 2.6176 | 32100 | 0.0025 |
| 2.6258 | 32200 | 0.0025 |
| 2.6339 | 32300 | 0.0025 |
| 2.6421 | 32400 | 0.0027 |
| 2.6502 | 32500 | 0.0029 |
| 2.6584 | 32600 | 0.0024 |
| 2.6666 | 32700 | 0.0023 |
| 2.6747 | 32800 | 0.0025 |
| 2.6829 | 32900 | 0.0028 |
| 2.6910 | 33000 | 0.0026 |
| 2.6992 | 33100 | 0.0025 |
| 2.7073 | 33200 | 0.0024 |
| 2.7155 | 33300 | 0.0025 |
| 2.7236 | 33400 | 0.0026 |
| 2.7318 | 33500 | 0.0027 |
| 2.7399 | 33600 | 0.0025 |
| 2.7481 | 33700 | 0.0024 |
| 2.7563 | 33800 | 0.0028 |
| 2.7644 | 33900 | 0.0025 |
| 2.7726 | 34000 | 0.0024 |
| 2.7807 | 34100 | 0.0023 |
| 2.7889 | 34200 | 0.0027 |
| 2.7970 | 34300 | 0.0024 |
| 2.8052 | 34400 | 0.0025 |
| 2.8133 | 34500 | 0.0024 |
| 2.8215 | 34600 | 0.0024 |
| 2.8297 | 34700 | 0.0029 |
| 2.8378 | 34800 | 0.0027 |
| 2.8460 | 34900 | 0.0025 |
| 2.8541 | 35000 | 0.0023 |
| 2.8623 | 35100 | 0.0029 |
| 2.8704 | 35200 | 0.0025 |
| 2.8786 | 35300 | 0.0024 |
| 2.8867 | 35400 | 0.0024 |
| 2.8949 | 35500 | 0.0024 |
| 2.9030 | 35600 | 0.0028 |
| 2.9112 | 35700 | 0.0026 |
| 2.9194 | 35800 | 0.0023 |
| 2.9275 | 35900 | 0.0024 |
| 2.9357 | 36000 | 0.003 |
| 2.9438 | 36100 | 0.0025 |
| 2.9520 | 36200 | 0.0025 |
| 2.9601 | 36300 | 0.0024 |
| 2.9683 | 36400 | 0.0028 |
| 2.9764 | 36500 | 0.0027 |
| 2.9846 | 36600 | 0.0027 |
| 2.9927 | 36700 | 0.0025 |
</details>
### Framework Versions
- Python: 3.11.2
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.0.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
```bibtex
@misc{sternlicht2025chimeraknowledgebaseidea,
title={CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature},
author={Noy Sternlicht and Tom Hope},
year={2025},
eprint={2505.20779},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.20779},
}
```
#### 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
**Quick Links**
- 🌐 [Project](https://noy-sternlicht.github.io/CHIMERA-Web)
- 📃 [Paper](https://arxiv.org/abs/2505.20779)
- 🛠️ [Code](https://github.com/noy-sternlicht/CHIMERA-KB)
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