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---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1893949
- loss:Contrastive
base_model: nreimers/MiniLM-L6-H384-uncased
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on nreimers/MiniLM-L6-H384-uncased
results:
- task:
type: col-berttriplet
name: Col BERTTriplet
dataset:
name: Unknown
type: unknown
metrics:
- type: accuracy
value: 0.37379997968673706
name: Accuracy
---
# PyLate model based on nreimers/MiniLM-L6-H384-uncased
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 31, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-1-neg-1-epoch-gooaq-1995000,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-1-neg-1-epoch-gooaq-1995000,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
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</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Col BERTTriplet
* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
| Metric | Value |
|:-------------|:-----------|
| **accuracy** | **0.3738** |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,893,949 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 12.73 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.78 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.7 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| question | answer | negative |
|:------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how do i import photos from iphone onto mac?</code> | <code>['Open the Photos app.', 'Connect your iPhone to Mac using a USB cable.', 'In the upper menu of the Photos app, choose Import.', 'Here you will see all the photos your iPhone has.', 'To import all photos, click Import all new photos on the upper-right corner of the window.']</code> | <code>Import to your Mac Connect your iPhone, iPad, or iPod touch to your Mac with a USB cable. Open the Photos app. The Photos app shows an Import screen with all the photos and videos that are on your connected device. If the Import screen doesn't automatically appear, click the device's name in the Photos sidebar.</code> |
| <code>what are hyperplastic colon polyps?</code> | <code>A hyperplastic polyp is a growth of extra cells that projects out from tissues inside your body. They occur in areas where your body has repaired damaged tissue, especially along your digestive tract. Hyperplastic colorectal polyps happen in your colon, the lining of your large intestine.</code> | <code>During the colonoscopy, it's hard to differentiate between the benign hyperplastic and the more worrisome adenomatous polyp. Polyps appear as lumps inside the colon. Some are flat and others hang down from a stalk. Each polyp is biopsied and tissue from the polyp is sent to a lab and tested for cancer.</code> |
| <code>what are the flaws of the electoral college quizlet?</code> | <code>['the winner of the popular vote is not guaranteed the presidency. ... ', 'electors are not required to vote in accord with the popular vote. ... ', 'any election might have to be decided in the HOR. ... ', 'small states are overrepresented- they have more electoral votes per a smaller amount of people than larger states.']</code> | <code>In other U.S. elections, candidates are elected directly by popular vote. But the president and vice president are not elected directly by citizens. Instead, they're chosen by “electors” through a process called the Electoral College. ... It was a compromise between a popular vote by citizens and a vote in Congress.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Evaluation Dataset
#### Unnamed Dataset
* Size: 5,000 evaluation samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 12.84 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.77 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 31.47 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| question | answer | negative_1 |
|:----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>1 cup how many grams of flour?</code> | <code>A cup of all-purpose flour weighs 4 1/4 ounces or 120 grams. This chart is a quick reference for volume, ounces, and grams equivalencies for common ingredients.</code> | <code>Convert 25 grams or g of flour to cups. 25 grams flour equals 1/4 cup.</code> |
| <code>is lasker rink owned by trump?</code> | <code>Lasker Rink was announced in 1962 and completed in 1966. It has been operated by The Trump Organization since 1987. In 2018, the city announced that the rink would be closed and rebuilt between 2021 and 2024.</code> | <code>Lasker Rink was announced in 1962 and completed in 1966. It has been operated by The Trump Organization since 1987. In 2018, the city announced that the rink would be closed and rebuilt between 2021 and 2024.</code> |
| <code>how many litres of water to drink a day for weight loss?</code> | <code>Bottom Line: According to the studies, 1–2 liters of water per day is enough to assist with weight loss, especially when consumed before meals.</code> | <code>Based on the studies, drinking 1-2 liters of water per day should be sufficient to help with weight loss. Here's how much water you should drink, in different measurements: Liters: 1–2.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 3e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 12
- `load_best_model_at_end`: True
#### 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 3e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: 12
- `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`: 12
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | accuracy |
|:------:|:-----:|:-------------:|:--------:|
| 0 | 0 | - | 0.3738 |
| 0.0001 | 1 | 9.8144 | - |
| 0.0135 | 200 | 8.6046 | - |
| 0.0270 | 400 | 6.3812 | - |
| 0.0405 | 600 | 4.0823 | - |
| 0.0541 | 800 | 2.3103 | - |
| 0.0676 | 1000 | 1.7525 | - |
| 0.0811 | 1200 | 1.4658 | - |
| 0.0946 | 1400 | 1.2898 | - |
| 0.1081 | 1600 | 1.1659 | - |
| 0.1216 | 1800 | 1.0575 | - |
| 0.1352 | 2000 | 1.0146 | - |
| 0.1487 | 2200 | 0.9502 | - |
| 0.1622 | 2400 | 0.9233 | - |
| 0.1757 | 2600 | 0.8957 | - |
| 0.1892 | 2800 | 0.8514 | - |
| 0.2027 | 3000 | 0.8499 | - |
| 0.2163 | 3200 | 0.8311 | - |
| 0.2298 | 3400 | 0.8007 | - |
| 0.2433 | 3600 | 0.787 | - |
| 0.2568 | 3800 | 0.7648 | - |
| 0.2703 | 4000 | 0.7538 | - |
| 0.2838 | 4200 | 0.7373 | - |
| 0.2974 | 4400 | 0.732 | - |
| 0.3109 | 4600 | 0.7335 | - |
| 0.3244 | 4800 | 0.7084 | - |
| 0.3379 | 5000 | 0.7109 | - |
| 0.3514 | 5200 | 0.7091 | - |
| 0.3649 | 5400 | 0.691 | - |
| 0.3785 | 5600 | 0.6814 | - |
| 0.3920 | 5800 | 0.6817 | - |
| 0.4055 | 6000 | 0.6694 | - |
| 0.4190 | 6200 | 0.6602 | - |
| 0.4325 | 6400 | 0.6594 | - |
| 0.4460 | 6600 | 0.6526 | - |
| 0.4596 | 6800 | 0.6433 | - |
| 0.4731 | 7000 | 0.6378 | - |
| 0.4866 | 7200 | 0.6362 | - |
| 0.5001 | 7400 | 0.6273 | - |
| 0.5136 | 7600 | 0.6293 | - |
| 0.5271 | 7800 | 0.6198 | - |
| 0.5407 | 8000 | 0.6166 | - |
| 0.5542 | 8200 | 0.6194 | - |
| 0.5677 | 8400 | 0.618 | - |
| 0.5812 | 8600 | 0.6109 | - |
| 0.5947 | 8800 | 0.6145 | - |
| 0.6082 | 9000 | 0.598 | - |
| 0.6217 | 9200 | 0.5982 | - |
| 0.6353 | 9400 | 0.5989 | - |
| 0.6488 | 9600 | 0.5926 | - |
| 0.6623 | 9800 | 0.5956 | - |
| 0.6758 | 10000 | 0.597 | - |
| 0.6893 | 10200 | 0.5803 | - |
| 0.7028 | 10400 | 0.5889 | - |
| 0.7164 | 10600 | 0.5907 | - |
| 0.7299 | 10800 | 0.5904 | - |
| 0.7434 | 11000 | 0.5857 | - |
| 0.7569 | 11200 | 0.5825 | - |
| 0.7704 | 11400 | 0.5825 | - |
| 0.7839 | 11600 | 0.5786 | - |
| 0.7975 | 11800 | 0.5797 | - |
| 0.8110 | 12000 | 0.5746 | - |
| 0.8245 | 12200 | 0.577 | - |
| 0.8380 | 12400 | 0.5765 | - |
| 0.8515 | 12600 | 0.5803 | - |
| 0.8650 | 12800 | 0.5671 | - |
| 0.8786 | 13000 | 0.5716 | - |
| 0.8921 | 13200 | 0.5822 | - |
| 0.9056 | 13400 | 0.5806 | - |
| 0.9191 | 13600 | 0.5734 | - |
| 0.9326 | 13800 | 0.578 | - |
| 0.9461 | 14000 | 0.569 | - |
| 0.9597 | 14200 | 0.5637 | - |
| 0.9732 | 14400 | 0.5777 | - |
| 0.9867 | 14600 | 0.5653 | - |
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 4.0.1
- PyLate: 1.1.7
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
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