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	0.989

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 is the intent of the encomienda system`	`Encomienda system established. The encomienda system was created by the Spanish to control and regulate American Indian labor and behavior during the colonization of the Americas. Under the encomienda system, conquistadors and other leaders (encomenderos) received grants of a number of Indians, from whom they could exact âtributeâ in the form of gold or labor.`	`SearchNetworking. 1 Intent-based networking needed to run more complex networks Experts address why intent-based networking systems are needed to manage networks of the future that connect data center, public ...`
`current weather in el cajon ca`	`El Cajon, CA Weather Forecast. TODAY - Patchy fog in the morning. Cloudy with a chance of rain. Highs around 63 in the western valleys to 56 to 61 near the foothills. Areas of winds southwest 15 mph. Chance of measurable precipitation 50 percent.`	The two weather patterns alternate every five years and affect the weather of the entire continent. Summers in North America during the El Nino niÃ±o weather pattern are wetter than. Average winters In North america During El nino niÃ±o are warmer on average and there is less. snow falll Nino niÃ±o causes higher ocean water temperatures While La nina niÃ±a causes colder. Water temperatures the two weather patterns alternate every five years and affect the weather of the. Entire continent Summers In north america During The el nino niÃ±o weather pattern are. wetter than average
`what is meant by gathering natural gas`	`Our Gathering and Processing segment consists of gathering, compressing, dehydrating, treating, conditioning, processing, and marketing natural gas and gathering crude oil. The gathering of natural gas consists of aggregating natural gas produced from various wells through small diameter gathering lines to processing plants. Natural gas has a widely varying composition depending on the field, the formation and the reservoir from which it is produced.`	`Propane is a byproduct of the oil refining process and therefore fluctuates in price more so than natural gas. Since the USA is the Saudi Arabia of natural gas, it should be cheaper per BTU than propane.`

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
`vegetables good for eye blood flow`	A diet high in saturated fats and sugar lacks the antioxidants needed for good eye health. A diet high in saturated fats also creates substances that put the health of your eyes at risk, such as arterial plaque, which can cause restricted blood flow in the blood vessels in your eyes.ome Excellent Choices for Feeding Healthy Eyes. The following list of foods provides good to excellent antioxidant support for your eyes: 1 Green vegetables like broccoli, green beans, spinach, peas and Brussels sprouts. 2 Carrots, celery and corn. 3 Leafy lettuce. 4 Sweet potatoes and yams.	`For this reason, a bag of frozen vegetables should be wrapped in a towel if used on extremities with very low blood flow, such as toes or hands. If the injury occurs in an area with little fat or muscle beneath the skin, such as fingers, take the compress off after 10 minutes maximum, wait 5 minutes and reapply.`
`is battery fully charged`	`In both Living on 12 Volts with Ample Power, and Wiring 12 Volts for Ample Power the authors explain that a battery is fully charged when the voltage is about 14.4 Volts and current through the battery has declined to less than 2% of the capacity of the battery in Amp-hours ...2 Amps for a 100 Ah battery.`	`1 Do not fully charge or fully discharge your deviceâs battery â charge it to around 50%. 2 If you store a device when its battery is fully discharged, the battery could fall into a deep discharge state, which renders it incapable of holding a charge.`
`what does the name adalina represent`	`Adalina /ada-li-na/ [4 sylls.] as a girls' name is of Old German derivation, and the name Adalina means noble. Adalina is a version of Adeline (Old German): French version of Adela. Andolina is a popular surname. Kreatif forms: Adalipa, Adalira, Adilina.`	`However, licensing as a broker or salesperson authorizes the licensee to represent parties on either side of a transaction. The choice of which side to represent is a business decision for the licensee. In the U.S., real estate brokers and salespersons are licensed by each state, not by the federal government.`

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_k512

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_k512