| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - colbert |
| - PyLate |
| - feature-extraction |
| - text-classification |
| - sentence-pair-classification |
| - semantic-similarity |
| - semantic-search |
| - retrieval |
| - reranking |
| - generated_from_trainer |
| - dataset_size:1452533 |
| - loss:Contrastive |
| base_model: colbert-ir/colbertv2.0 |
| datasets: |
| - redis/langcache-sentencepairs-v1 |
| pipeline_tag: sentence-similarity |
| library_name: PyLate |
| metrics: |
| - accuracy |
| model-index: |
| - name: Fine-tuned ColBERT model for semantic caching |
| results: |
| - task: |
| type: col-berttriplet |
| name: Col BERTTriplet |
| dataset: |
| name: test triplet |
| type: test_triplet |
| metrics: |
| - type: accuracy |
| value: 0.8205713629722595 |
| name: Accuracy |
| --- |
| |
| # Fine-tuned ColBERT model for semantic caching |
|
|
| This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. 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:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf --> |
| - **Document Length:** 128 tokens |
| - **Query Length:** 128 tokens |
| - **Output Dimensionality:** 128 tokens |
| - **Similarity Function:** MaxSim |
| - **Training Dataset:** |
| - [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### 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': 127, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False}) |
| ) |
| ``` |
|
|
| ## Usage |
| First install the PyLate library: |
|
|
| ```bash |
| pip install -U pylate |
| ``` |
|
|
| ### Retrieval |
|
|
| Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search. |
|
|
| #### Indexing documents |
|
|
| Load the ColBERT model and initialize the PLAID 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="aditeyabaral/langcache-colbert-v1-4gpu", |
| ) |
| |
| # Step 2: Initialize the PLAID index |
| index = indexes.PLAID( |
| 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.PLAID( |
| 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="aditeyabaral/langcache-colbert-v1-4gpu", |
| ) |
| |
| 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) |
|
|
| <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 |
|
|
| #### Col BERTTriplet |
| * Dataset: `test_triplet` |
| * Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code> |
| |
| | Metric | Value | |
| |:-------------|:-----------| |
| | **accuracy** | **0.8206** | |
| |
| <!-- |
| ## 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 |
| |
| #### LangCache Sentence Pairs (subsets=['all'], train+val=True) |
| |
| * Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
| * Size: 1,452,533 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative_1 | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 9 tokens</li><li>mean: 29.49 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.18 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.79 tokens</li><li>max: 52 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative_1 | |
| |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------| |
| | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code>Are there many Canadians living and working illegally in the United States?</code> | |
| | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code>Is there any tricks for straight lines mcqs?</code> | |
| | <code> Can I pay with a debit card on PayPal?</code> | <code> Can I pay with a debit card on PayPal?</code> | <code>Can you transfer PayPal funds onto a debit card/credit card?</code> | |
| * Loss: <code>pylate.losses.contrastive.Contrastive</code> |
|
|
| ### Evaluation Dataset |
|
|
| #### LangCache Sentence Pairs (split=test) |
|
|
| * Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
| * Size: 110,066 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative_1 | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 28.57 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 28.01 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.73 tokens</li><li>max: 65 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative_1 | |
| |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
| | <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> | |
| | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> | |
| | <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Do you get more homework in 9th grade than 8th?</code> | |
| * Loss: <code>pylate.losses.contrastive.Contrastive</code> |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 48 |
| - `num_train_epochs`: 5 |
| - `learning_rate`: 0.0002 |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch |
| - `weight_decay`: 0.001 |
| - `eval_strategy`: steps |
| - `per_device_eval_batch_size`: 48 |
| - `eval_on_start`: True |
| - `push_to_hub`: True |
| - `hub_model_id`: aditeyabaral/langcache-colbert-v1-4gpu |
| - `load_best_model_at_end`: True |
| - `ddp_find_unused_parameters`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 48 |
| - `num_train_epochs`: 5 |
| - `max_steps`: -1 |
| - `learning_rate`: 0.0002 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `weight_decay`: 0.001 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: False |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `eval_strategy`: steps |
| - `per_device_eval_batch_size`: 48 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: True |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: True |
| - `hub_private_repo`: None |
| - `hub_model_id`: aditeyabaral/langcache-colbert-v1-4gpu |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: True |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: True |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | accuracy | |
| |:------:|:-----:|:-------------:|:---------------:|:--------:| |
| | 0 | 0 | - | 1261.8169 | 0.8206 | |
| | 0.1322 | 1000 | 100.6328 | - | - | |
| | 0.2644 | 2000 | 0.6220 | - | - | |
| | 0.3966 | 3000 | 0.5276 | - | - | |
| | 0.5288 | 4000 | 0.7564 | - | - | |
| | 0.6609 | 5000 | 0.5519 | - | - | |
| | 0.7931 | 6000 | 1.8754 | - | - | |
| | 0.9253 | 7000 | 4.2339 | - | - | |
| | 1.0575 | 8000 | 1.8449 | - | - | |
| | 1.1897 | 9000 | 1.6022 | - | - | |
| | 1.3219 | 10000 | 1.4372 | - | - | |
| | 1.4541 | 11000 | 1.2331 | - | - | |
| | 1.5863 | 12000 | 1.1511 | - | - | |
| | 1.7184 | 13000 | 1.0779 | - | - | |
| | 1.8506 | 14000 | 1.0823 | - | - | |
| | 1.9828 | 15000 | 0.9632 | - | - | |
| | 2.1150 | 16000 | 0.8800 | - | - | |
| | 2.2472 | 17000 | 0.8625 | - | - | |
| | 2.3794 | 18000 | 0.8055 | - | - | |
| | 2.5116 | 19000 | 0.6943 | - | - | |
| | 2.6438 | 20000 | 0.7342 | - | - | |
| | 2.7759 | 21000 | 0.7034 | - | - | |
| | 2.9081 | 22000 | 0.6930 | - | - | |
| | 3.0403 | 23000 | 0.6543 | - | - | |
| | 3.1725 | 24000 | 0.6544 | - | - | |
| | 3.3047 | 25000 | 0.5769 | - | - | |
| | 3.4369 | 26000 | 0.5262 | - | - | |
| | 3.5691 | 27000 | 0.5684 | - | - | |
| | 3.7013 | 28000 | 0.5433 | - | - | |
| | 3.8334 | 29000 | 0.5481 | - | - | |
| | 3.9656 | 30000 | 0.5552 | - | - | |
| | 4.0978 | 31000 | 0.5399 | - | - | |
| | 4.2300 | 32000 | 0.5605 | - | - | |
| | 4.3622 | 33000 | 0.5385 | - | - | |
| | 4.4944 | 34000 | 0.4941 | - | - | |
| | 4.6266 | 35000 | 0.5287 | - | - | |
| | 4.7588 | 36000 | 0.5289 | - | - | |
| | 4.8909 | 37000 | 0.5502 | - | - | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.3.0 |
| - PyLate: 1.5.0 |
| - Transformers: 5.3.0 |
| - PyTorch: 2.9.0+cu130 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.22.2 |
|
|
|
|
| ## 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 |
| @inproceedings{DBLP:conf/cikm/ChaffinS25, |
| author = {Antoine Chaffin and |
| Rapha{"{e}}l Sourty}, |
| editor = {Meeyoung Cha and |
| Chanyoung Park and |
| Noseong Park and |
| Carl Yang and |
| Senjuti Basu Roy and |
| Jessie Li and |
| Jaap Kamps and |
| Kijung Shin and |
| Bryan Hooi and |
| Lifang He}, |
| title = {PyLate: Flexible Training and Retrieval for Late Interaction Models}, |
| booktitle = {Proceedings of the 34th {ACM} International Conference on Information |
| and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November |
| 10-14, 2025}, |
| pages = {6334--6339}, |
| publisher = {{ACM}}, |
| year = {2025}, |
| url = {https://github.com/lightonai/pylate}, |
| doi = {10.1145/3746252.3761608}, |
| } |
| ``` |
|
|
| <!-- |
| ## 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.* |
| --> |
