This is a PyLate model finetuned from BAAI/bge-small-en-v1.5 on the msmarco-10m-triplets 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: BAAI/bge-small-en-v1.5
Document Length: 300 tokens
Query Length: 32 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim
Training Dataset:
- msmarco-10m-triplets

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': 300, 'do_lower_case': True, 'architecture': 'BertModel'})
  (1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID 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="pylate_model_id",
)

# 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="pylate_model_id",
)

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	0.991

Training Details

Training Dataset

msmarco-10m-triplets

Dataset: msmarco-10m-triplets at 8c5139a
Size: 9,998,000 training samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens

Samples:

query	positive	negative
`what kind of carbohydrates can i eat in a gluten free diet?`	`What Can I Eat That is Gluten-Free? Even though going gluten-free can be difficult, you still have many food choices! Focus on eating a variety of fruits, vegetables, low-fat dairy products (those that do not have gluten-containing additives), beans, eggs, nuts, and lean meat, poultry, and fish. There are still many healthy whole grains and starchy carbohydrate foods to choose from that do not contain gluten: Amaranth. Arrowroot.`	`Gluten-free crust option upon request. While we try hard to maintain the integrity of our gluten free crust, please be aware that it does run the risk of exposure to wheat-based products. Due to the risk of cross contamination, MOD DOES NOT RECOMMEND this pizza for those with celiac disease or other gluten allergies. Feeling Inspired? Express Yourself Through Pizza`
`remsen area code`	`Remsen, NY Area Codes are. Remsen, NY is currently using two area codes which are area codes 315 and 680. In addition to Remsen, NY area code information read more details about area code 315, area code 680 and New York area codes. Remsen, NY is located in Oneida County and observes the Eastern Time Zone.`	`313 Area Code. AreaCode.org is an area code finder with detailed information on the 313 area code including 313 area code map. Major cities like Dearborn within area code 313 are also listed on this page.`
`when was betsy ross born`	Early Life. Betsy Ross, best known for making the first American flag, was born Elizabeth Griscom in Philadelphia, Pennsylvania, on January 1, 1752. A fourth-generation American, and the great-granddaughter of a carpenter who had arrived in New Jersey in 1680 from England, Betsy was the eighth of 17 children.ynopsis. Betsy Ross, a fourth-generation America born in 1752 in Philadelphia, Pennsylvania, apprenticed with an upholsterer before irrevocably splitting with her family to marry outside the Quaker religion. She and her husband John Ross started their own upholstery business.	`Katharine Ross (I) Katharine Juliet Ross was born on January 29, 1940 in Hollywood, California, to Katharine W. (Hall) and Dudley T. Ross. Her father, who also worked for the Associated Press, was away in the US Navy when she was born.`

Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

msmarco-10m-triplets

Dataset: msmarco-10m-triplets at 8c5139a
Size: 2,000 evaluation samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens
min: 32 tokens
mean: 32.0 tokens
max: 32 tokens

Samples:

query	positive	negative
`pikas are closely related to which typeb of animal`	The pika is a small-sized mammal that is found across the Northern Hemisphere. Despite their rodent-like appearance, pikas are actually closely related to rabbits and hares.Pikas are most commonly identified by their small, rounded body and lack of tail. Pikas prefer the colder climates and are generally found in mountainous regions and rocky areas where there tend to be fewer predators.ikas defend their territory by whistling to one another, and their large, rounded ears come in useful to hear the calls from competing pikas. Pikas are herbivorous animals and the pika therefore has a diet based on vegetation.	`Alpacas are very closely related to llamas. They are both from a group of four species known as South American Camelids. The llama is approximately twice the size of an alpaca with banana shaped ears and is principally used as a pack animal. Alpacas are exclusively bred as fleece animals in Australia.`
`when can we see northern lights in norway`	`The Northern Lights can appear at any time, but they usually grace the sky between 6 oâclock in the evening and 1 oâclock in the morning. 1 It is rare to see the Northern Lights before 18. 00/6pm, even during the dark months. 2 The highest frequency is around 22. 00â23. 3 If you see the Northern Lights at 19.`	`Transfer points on the Northern lights & Norway in a nutshellÂ® trip. Oslo: Arrival/departure by plane to/from Oslo Airport Gardermoen, 28 mi./45 km north of city center. Transport by airport train or airport bus. TromsÃ¸: Arrival/departure by plane to/from TromsÃ¸ Airport, 1.8 mi./3 km west of city center.`
`what games do markiplier play`	`List of Games. Markiplier is a professional gamer, who is best known for playing horror-themed video games. Along with many other types of games, including, but not limited to: flash games, indie point-and-click games and adventure games.`	`Stop wasting your time for playing games when you can play games and be paid for it. Be the one of the game testers and start earning money from something that makes you happy. Visit [object Object], become a game tester today and get paid to play video games. Felisha Â· 1 year ago.`

Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 196
per_device_eval_batch_size: 196
learning_rate: 3e-05
max_grad_norm: 10.0
num_train_epochs: 0
max_steps: 50000
warmup_ratio: 0.01
bf16: True
torch_compile: True
torch_compile_backend: inductor
eval_on_start: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 196
per_device_eval_batch_size: 196
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-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 10.0
num_train_epochs: 0
max_steps: 50000
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.01
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}
parallelism_config: 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: None
hub_always_push: False
hub_revision: None
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: True
torch_compile_backend: inductor
torch_compile_mode: 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: True
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

	query	positive	negative
type	string	string	string
details	min: 32 tokens mean: 32.0 tokens max: 32 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens

xtr-replicability/bge_small_colbert_contrastive

PyLate model based on BAAI/bge-small-en-v1.5

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

msmarco-10m-triplets

Evaluation Dataset

msmarco-10m-triplets

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

xtr-replicability/
bge_small_colbert_contrastive