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	0.9925

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 can black mold do`	`Two of the better-known toxic molds include Stachybotrys chartarum (black mold), which can cause everything from headaches to cancer, and Aspergillus, which can cause severe lung infections, or progress to whole-body infections. Mold is particularly dangerous for infants and children.`	`Learn more about mold and health effects in a A Brief Guide to Mold, Moisture and Your Home The entire booklet: A Brief Guide to Mold, Moisture and Your Home (Web Version) A Brief Guide to Mold, Moisture and Your Home (Print Version) Top of Page`
`what factors increase a population growth`	`Quick Answer. Factors that cause population growth include increased food production, improved health care services, immigration and high birth rate. These factors have led to overpopulation, which has more negative effects than positive impacts.`	`The increase in crawfish size during molting, and the length of time between molts, can vary greatly and are affected by factors such as water temperature, water quality, food quality and quantity, population density, oxygen levels and to a lesser extent by genetic influences.`
`is herpes spread through saliva`	This type of herpes is transmittable through contact with the saliva or the herpes blisters (cold sores) of an infected person. This said â yes, it is entirely possible to get herpes from kissing.It is also possible, though less common, that herpes type 1 might spread to genital regions through oral sex.enital herpes can spread to the mouth through oral sex. Once you have contracted either type of herpes virus you will be a carrier for life. However, both types tend to become less severe with the passing of time and though they may still be contagious to others, many times people stop having breakouts at all.	Introduction. Herpes simplex virus (HSV) infections are very common worldwide. HSV-1 is the main cause of herpes infections on the mouth and lips, including cold sores and fever blisters. It is transmitted through kissing or sharing drinking glasses and utensils.HSV-1 can also cause genital herpes, although HSV-2 is the main cause of genital herpes.HSV-2 is spread through sexual contact.You may be infected with HSV-1 or HSV-2 but not show any symptoms. Often symptoms are triggered by exposure to the sun, fever, menstruation, emotional stress, a weakened immune system, or an illness. There is no cure for herpes, and once you have it, it is likely to come back.ntroduction. Herpes simplex virus (HSV) infections are very common worldwide. HSV-1 is the main cause of herpes infections on the mouth and lips, including cold sores and fever blisters. It is transmitted through kissing or sharing drinking glasses and utensils.

Loss: pylate.losses.xtr_primeqa.XTRPrimeQA

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
`what school district is siefert elementary`	`Siefert Elementary is a public elementary school located in Milwaukee, WI in the Milwaukee School District. It enrolls 307 students in grades 1st through 12th. Siefert Elementary is the 756th largest public school in Wisconsin and the 40,598th largest nationally. It has 17.5 students to every teacher.`	`Due to the hazardous road conditions, there will be a two hour delay today, Wednesday, March 2nd, 2016 for Black River HS/MS, Cavendish Town Elementary School, Chester-Andover Elementary School, Green Mountain Union HS, Ludlow Elementary School and Mount Holly School.`
`what is dst`	`Daylight Saving Time (DST) is the practice of turning the clock ahead as warmer weather approaches and back as it becomes colder again so that people will have one more hour of daylight in the afternoon and evening during the warmer season of the year.`	`Franklin Park time zone: UTC-05:00 or CDT. Daylight saving time is in effect in Franklin Park. See recent and expected DST changes in Franklin Park in the table below.`
`who played sister sister mom`	`The main cast of Sister, Sister (from left to right), Tia Mowry with JackÃ©e Harry as Tia and Lisa Landry and Tim Reid with Tamera Mowry as Ray and Tamera Campbell. Sister, Sister is an American television sitcom starring fraternal twins Tia and Tamera Mowry. It aired from 1994 to 1999.`	`The boy, 16, was killed by his sister, 15, on January 5 after she escaped the room her brother had locked her in and shot him in the neck. The older sister asked her younger sister to keep watch as she went outside and cut out the air conditioner of her parents' locked bedroom window to retrieve a pistol.`

Loss: pylate.losses.xtr_primeqa.XTRPrimeQA

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

robro612/bge_small_xtr_contrastive_k256

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

robro612/
bge_small_xtr_contrastive_k256