PyLate model based on jhu-clsp/ettin-encoder-17m

This is a PyLate model finetuned from jhu-clsp/ettin-encoder-17m. 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: jhu-clsp/ettin-encoder-17m
Document Length: 256 tokens
Query Length: 32 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim

Model Sources

Documentation: PyLate Documentation
Repository: PyLate on GitHub
Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 255, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Dense({'in_features': 256, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Usage

First install the PyLate library:

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:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path=yosefw/colbert-ettin-17m,
)

# 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:

# 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:

# 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:

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=yosefw/colbert-ettin-17m,
)

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,
)

Evaluation

Metrics

Col BERTTriplet

Evaluated with pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator

Metric	Value
accuracy	0.798

Training Details

Training Dataset

Unnamed Dataset

Size: 972,246 training samples
Columns: query, positive, negative_1, negative_2, and negative_3

Approximate statistics based on the first 1000 samples:

	query	positive	negative_1	negative_2	negative_3
type	string	string	string	string	string
details	min: 5 tokens mean: 10.04 tokens max: 22 tokens	min: 21 tokens mean: 31.92 tokens max: 32 tokens	min: 19 tokens mean: 31.92 tokens max: 32 tokens	min: 19 tokens mean: 31.92 tokens max: 32 tokens	min: 22 tokens mean: 31.94 tokens max: 32 tokens

Samples:

query	positive	negative_1	negative_2	negative_3
`what diseases does sugar cause`	Eating too much sugar raises your risk for gaining weight and the health problems that are associated with being overweight. You are more likely to suffer diabetes, heart disease, high blood pressure, cancer and many other health conditions when you indulge your sweet tooth too often.Table sugar isn’t the only culprit when it comes to sugar.ou are more likely to suffer diabetes, heart disease, high blood pressure, cancer and many other health conditions when you indulge your sweet tooth too often. Table sugar isn’t the only culprit when it comes to sugar.	`Sugar and Heart Disease. Lately, we’ve seen epidemiological data suggesting that increased intake of sugar-sweetened beverages increases the risk for metabolic syndrome, type 2 diabetes, coronary heart disease, and stroke (5).ugar and Heart Disease. Lately, we’ve seen epidemiological data suggesting that increased intake of sugar-sweetened beverages increases the risk for metabolic syndrome, type 2 diabetes, coronary heart disease, and stroke (5).`	`High intake of sugar and refined carbohydrates is associated with increased risk of diabetes, metabolic syndrome, non-alcoholic fatty liver disease, lipid disorders and high blood pressure.ugar and Heart Disease. Lately, we’ve seen epidemiological data suggesting that increased intake of sugar-sweetened beverages increases the risk for metabolic syndrome, type 2 diabetes, coronary heart disease, and stroke (5).`	`Sugar Disease is a problem that manifests in different ways in different individuals, of different ages and of different genetic susceptibility-but its three cardinal forms are:he glycemic index: key to diet for Sugar Disease. Some have proposed that persons with variants of Sugar Disease follow a diet that rigidly excludes carbohydrates, concentrating instead on meat and vegetables. In my opinion this is rarely necessary and results in dietary imbalances.`
`what diseases does sugar cause`	Eating too much sugar raises your risk for gaining weight and the health problems that are associated with being overweight. You are more likely to suffer diabetes, heart disease, high blood pressure, cancer and many other health conditions when you indulge your sweet tooth too often.Table sugar isn’t the only culprit when it comes to sugar.ou are more likely to suffer diabetes, heart disease, high blood pressure, cancer and many other health conditions when you indulge your sweet tooth too often. Table sugar isn’t the only culprit when it comes to sugar.	`Another mechanism whereby sugar consumption may increase the risk of cardiovascular disease is through its effects on blood pressure. It is well known that high blood pressure increases the risk for cardiovascular disease.ugar and Heart Disease. Lately, we’ve seen epidemiological data suggesting that increased intake of sugar-sweetened beverages increases the risk for metabolic syndrome, type 2 diabetes, coronary heart disease, and stroke (5).`	`The term Sugar Disease is a convenient catch-all for a host of modern conditions that result from an unbridled intake of sugar or refined carbohydrates coupled with a sedentary lifestyle.he glycemic index: key to diet for Sugar Disease. Some have proposed that persons with variants of Sugar Disease follow a diet that rigidly excludes carbohydrates, concentrating instead on meat and vegetables. In my opinion this is rarely necessary and results in dietary imbalances.`	Brown sugar is just sucrose with molasses – same basic composition. Glucose, or blood sugar, is the sugar that circulates in your blood. Fructose, or fruit sugar, is found in plants and honey. It’s the fructose in sugar that causes the problem, as you will see.That doesn’t mean you shouldn’t eat whole fruit; whole fruit contains fiber that slows digestion.It does mean that fruit juice poses a danger.t’s not a fact that sugar causes cancer, and Lustig does not become an absolute authority by virtue of his lecture going viral. It’s not the way medical science works, or should work: web virality implies popularity, not truth.
`average cost per square foot to build a house`	`Generally, turnkey costs will start at around $70 a square foot for a starter home in ideal conditions. An average level of finish will be more like $90-100. At $110-120 we are building a custom finish.`	`Home prices of $55, $66, $72, $80, $84, $92, $110, $118, and $328 per square foot combine to produce an average of $112 per square foot, which is probably a reasonable figure for many areas of the country. However, the difference between the lowest figure and the highest is very substantial.`	`An average commercial steel building costs between $16 and $20 per square foot, including building package (I-Beams, purlins, girts etc.) , delivery, foundation and the cost of construction.`	`At first glance, the average home building cost per square foot seems extremely high. People who do a lot of home improvement jobs are usually the first ones to question, because they know the cost of materials. The fact is that the materials are about 25-33 percent of the cost of a house.`

Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

Unnamed Dataset

Size: 20,000 evaluation samples
Columns: query, positive, negative_1, negative_2, and negative_3

Approximate statistics based on the first 1000 samples:

	query	positive	negative_1	negative_2	negative_3
type	string	string	string	string	string
details	min: 5 tokens mean: 10.12 tokens max: 20 tokens	min: 28 tokens mean: 31.99 tokens max: 32 tokens	min: 19 tokens mean: 31.9 tokens max: 32 tokens	min: 19 tokens mean: 31.95 tokens max: 32 tokens	min: 19 tokens mean: 31.92 tokens max: 32 tokens

Samples:

query	positive	negative_1	negative_2	negative_3
`what are the dimensions of a regulation nba backboard?`	`What is the size of a basketball backboard? Per NBA regulations, the dimensions of a basketball backboard are 6 feet wide by 3 ½ feet high. The backboard is marked with a 2-inch white rectangle that is centered behind the ring; the rectangle's outer dimensions are 24 inches wide by 18 inches high. The basket itself consists of a metal ring with an 18-inch inner diameter and a white cord net that is 15 to 18 inches long.`	`The Backboard and Rim: The regulation distance from the ground to the top of the rim is 10 feet for all levels of play. Regulation backboards are 6 feet wide (72 inches) by 42 inches tall. All basketball rims (hoops) are 18 inches in diameter. The inner square on the backboard is 24 inches wide by 18 inches tall. All line markings on the floor are 2 inches wide and can vary in color.`	`72 Backboard (Regulation) 72 x 42 backboard systems are the best choice if you're a serious basketball enthusiast or a former high school or college competitor. 1 The official, regulation size used in high school, college, and NBA competition. Ideal system for a dedicated backyard court or a large 3 car garage driveway.`	`Each basket ring shall be securely attached to the backboard with its upper edge 10' above and parallel to the floor and equidistant from the vertical edges of the board. The nearest point of the inside edge of the ring shall be 6 from the plane of the face of the board.`
`what are the dimensions of a regulation nba backboard?`	`What is the size of a basketball backboard? Per NBA regulations, the dimensions of a basketball backboard are 6 feet wide by 3 ½ feet high. The backboard is marked with a 2-inch white rectangle that is centered behind the ring; the rectangle's outer dimensions are 24 inches wide by 18 inches high. The basket itself consists of a metal ring with an 18-inch inner diameter and a white cord net that is 15 to 18 inches long.`	`72 Backboard (Regulation) 72 x 42 backboard systems are the best choice if you're a serious basketball enthusiast or a former high school or college competitor. The official, regulation size used in high school, college, and NBA competition.`	`What are the standard measurements for a basketball backboard? The standard size for a basketball backboard is 6 feet wide and 3 1/2 feet high. The surface should be flat and usually there is a 2-inch thick rectangle above the rim that is 24 inches wide and 18 inches high.`	`The areas identified by the lane space markings are 2 by 8 inches and the neutral zone marks are 12 by 8. c. A free throw line shall be drawn (2 wide) across each of the circles indicated in the court diagram. It shall be parallel to the end line and shall be 15' from the plane of the face of the backboard.`
`what is a usb receiver`	`The Logitech Unifying receiver is a miniaturised dedicated USB wireless receiver which permits up to 6 devices such as mice and keyboards (headphones are not compatible), which must be made by Logitech and of compatible design, to be linked to the same computer using 2.4 GHz band radio communication in a way very similar to, but incompatible with ...`	`Wireless USB. Wireless USB is a short-range, high-bandwidth wireless radio communication protocol created by the Wireless USB Promoter Group which intends to further increase the availability of general USB-based technologies. It is maintained by the WiMedia Alliance and (as of 2009) the current revision is 1.0, which was approved in 2005.`	`Replacement USB RF receiver for current Air Mouse Elite and Air Mouse... Replacement USB RF receiver for current Air Mouse Elite and Air Mouse GO Plus products. Enables a 100-foot (30-meter) wireless range. Before ordering for GO Plus, confirm that your mouse unit is labeled AS04130. Consider purchasing two to ensure uninterrupted Air Mouse operation.`	`VicTsing MM057 2.4G Wireless Portable Mobile Mouse Optical Mice with USB Receiver, 5 Adjustable DPI Levels, 6... by VicTsing. $ 9 99 $19.99Prime. Get it by Tomorrow, Apr 21. 50% off item with purchase of 1 items. 15% off item with purchase of 1 items. See Details.`

Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 8e-06
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.05
fp16: True
load_best_model_at_end: True
push_to_hub: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 8e-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: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
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: 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: True
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	accuracy
1.0	15192	1.3895	-	-
0	0	-	-	0.7882
1.0	15192	-	1.0425	-
2.0	30384	0.9597	-	-
0	0	-	-	0.7972
2.0	30384	-	1.0071	-
3.0	45576	0.8756	-	-
0	0	-	-	0.7979
3.0	45576	-	1.0083	-
4.0	60768	0.8355	-	-
0	0	-	-	0.7978
4.0	60768	-	1.0145	-
0	0	-	-	0.7980

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 4.0.2
PyLate: 1.2.0
Transformers: 4.48.2
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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

@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}
}

yosefw
/

colbert-ettin-17m

PyLate model based on jhu-clsp/ettin-encoder-17m

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Retrieval

Indexing documents

Retrieving top-k documents for queries

Reranking

Evaluation

Metrics

Col BERTTriplet

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

PyLate

Model tree for yosefw/colbert-ettin-17m

Evaluation results