PyLate model based on answerdotai/ModernBERT-base
This is a PyLate model finetuned from answerdotai/ModernBERT-base on the train dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
I finetuned the model with official script examples/train_pylate.py on a RTX 4090 GPU in 12 hours. See more details in trianing logs. The finetuned model performance is on par with numbers reported in the paper.
Model Details
Model Description
- Model Type: PyLate model
- Base model: answerdotai/ModernBERT-base
- Document Length: 180 tokens
- Query Length: 32 tokens
- Output Dimensionality: 128 tokens
- Similarity Function: MaxSim
- Training Dataset:
- Language: en
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': 179, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Dense({'in_features': 768, '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=pylate_model_id,
)
# 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=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
NDCG@10
Dataset | Score |
---|---|
FiQA | 0.3986 |
SciFact | 0.7367 |
nfcorpus | 0.3398 |
arguana | 0.3098 |
Training Details
Training Dataset
train
- Dataset: train at 11e6ffa
- Size: 808,728 training samples
- Columns:
query_id
,document_ids
, andscores
- Approximate statistics based on the first 1000 samples:
query_id document_ids scores type string list list details - min: 5 tokens
- mean: 5.59 tokens
- max: 6 tokens
- size: 32 elements
- size: 32 elements
- Samples:
query_id document_ids scores 121352
['2259784', '4923159', '40211', '1545154', '8527175', ...]
[0.2343463897705078, 0.639204204082489, 0.3806908428668976, 0.5623092651367188, 0.8051995635032654, ...]
634306
['7723525', '1874779', '379307', '2738583', '7599583', ...]
[0.7124203443527222, 0.7379189729690552, 0.5786551237106323, 0.6142299175262451, 0.6755089163780212, ...]
920825
['5976297', '2866112', '3560294', '3285659', '4706740', ...]
[0.6462352871894836, 0.7880821228027344, 0.791019856929779, 0.7709633111953735, 0.8284491300582886, ...]
- Loss:
pylate.losses.distillation.Distillation
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 4gradient_accumulation_steps
: 4learning_rate
: 8e-05num_train_epochs
: 1warmup_ratio
: 0.05bf16
: Truetf32
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 4per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 4eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 8e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.05warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss |
---|---|---|
0.0020 | 100 | 0.0524 |
0.0040 | 200 | 0.0482 |
0.0059 | 300 | 0.0464 |
0.0079 | 400 | 0.043 |
0.0099 | 500 | 0.0387 |
0.0119 | 600 | 0.0383 |
0.0138 | 700 | 0.0345 |
0.0158 | 800 | 0.0307 |
0.0178 | 900 | 0.0294 |
0.0198 | 1000 | 0.0275 |
0.0218 | 1100 | 0.0271 |
0.0237 | 1200 | 0.0264 |
0.0257 | 1300 | 0.0258 |
0.0277 | 1400 | 0.0246 |
0.0297 | 1500 | 0.0239 |
0.0317 | 1600 | 0.023 |
0.0336 | 1700 | 0.0216 |
0.0356 | 1800 | 0.0282 |
0.0376 | 1900 | 0.0211 |
0.0396 | 2000 | 0.0205 |
0.0415 | 2100 | 0.0197 |
0.0435 | 2200 | 0.0187 |
0.0455 | 2300 | 0.0184 |
0.0475 | 2400 | 0.0177 |
0.0495 | 2500 | 0.0179 |
0.0514 | 2600 | 0.0173 |
0.0534 | 2700 | 0.0169 |
0.0554 | 2800 | 0.0163 |
0.0574 | 2900 | 0.016 |
0.0594 | 3000 | 0.016 |
0.0613 | 3100 | 0.0147 |
0.0633 | 3200 | 0.0148 |
0.0653 | 3300 | 0.0155 |
0.0673 | 3400 | 0.0149 |
0.0692 | 3500 | 0.0149 |
0.0712 | 3600 | 0.0141 |
0.0732 | 3700 | 0.0145 |
0.0752 | 3800 | 0.0142 |
0.0772 | 3900 | 0.0143 |
0.0791 | 4000 | 0.0137 |
0.0811 | 4100 | 0.0134 |
0.0831 | 4200 | 0.0129 |
0.0851 | 4300 | 0.0133 |
0.0871 | 4400 | 0.0135 |
0.0890 | 4500 | 0.0128 |
0.0910 | 4600 | 0.0126 |
0.0930 | 4700 | 0.0126 |
0.0950 | 4800 | 0.0129 |
0.0969 | 4900 | 0.0127 |
0.0989 | 5000 | 0.0127 |
0.1009 | 5100 | 0.0125 |
0.1029 | 5200 | 0.0119 |
0.1049 | 5300 | 0.0124 |
0.1068 | 5400 | 0.012 |
0.1088 | 5500 | 0.013 |
0.1108 | 5600 | 0.0119 |
0.1128 | 5700 | 0.0118 |
0.1147 | 5800 | 0.0121 |
0.1167 | 5900 | 0.0119 |
0.1187 | 6000 | 0.0116 |
0.1207 | 6100 | 0.0112 |
0.1227 | 6200 | 0.0116 |
0.1246 | 6300 | 0.0115 |
0.1266 | 6400 | 0.0119 |
0.1286 | 6500 | 0.0115 |
0.1306 | 6600 | 0.0109 |
0.1326 | 6700 | 0.0114 |
0.1345 | 6800 | 0.0114 |
0.1365 | 6900 | 0.0109 |
0.1385 | 7000 | 0.011 |
0.1405 | 7100 | 0.0111 |
0.1424 | 7200 | 0.0109 |
0.1444 | 7300 | 0.0108 |
0.1464 | 7400 | 0.0112 |
0.1484 | 7500 | 0.0106 |
0.1504 | 7600 | 0.011 |
0.1523 | 7700 | 0.0106 |
0.1543 | 7800 | 0.0107 |
0.1563 | 7900 | 0.0108 |
0.1583 | 8000 | 0.0106 |
0.1603 | 8100 | 0.0107 |
0.1622 | 8200 | 0.0108 |
0.1642 | 8300 | 0.0103 |
0.1662 | 8400 | 0.0107 |
0.1682 | 8500 | 0.0104 |
0.1701 | 8600 | 0.011 |
0.1721 | 8700 | 0.0105 |
0.1741 | 8800 | 0.0105 |
0.1761 | 8900 | 0.01 |
0.1781 | 9000 | 0.0106 |
0.1800 | 9100 | 0.0105 |
0.1820 | 9200 | 0.0104 |
0.1840 | 9300 | 0.0104 |
0.1860 | 9400 | 0.0107 |
0.1879 | 9500 | 0.0102 |
0.1899 | 9600 | 0.0103 |
0.1919 | 9700 | 0.0105 |
0.1939 | 9800 | 0.01 |
0.1959 | 9900 | 0.0098 |
0.1978 | 10000 | 0.0099 |
0.1998 | 10100 | 0.0099 |
0.2018 | 10200 | 0.0099 |
0.2038 | 10300 | 0.0098 |
0.2058 | 10400 | 0.01 |
0.2077 | 10500 | 0.0101 |
0.2097 | 10600 | 0.0098 |
0.2117 | 10700 | 0.0101 |
0.2137 | 10800 | 0.0098 |
0.2156 | 10900 | 0.0101 |
0.2176 | 11000 | 0.01 |
0.2196 | 11100 | 0.01 |
0.2216 | 11200 | 0.0096 |
0.2236 | 11300 | 0.0096 |
0.2255 | 11400 | 0.0096 |
0.2275 | 11500 | 0.0098 |
0.2295 | 11600 | 0.0099 |
0.2315 | 11700 | 0.0094 |
0.2335 | 11800 | 0.0096 |
0.2354 | 11900 | 0.0094 |
0.2374 | 12000 | 0.0098 |
0.2394 | 12100 | 0.0095 |
0.2414 | 12200 | 0.0095 |
0.2433 | 12300 | 0.0098 |
0.2453 | 12400 | 0.0097 |
0.2473 | 12500 | 0.0094 |
0.2493 | 12600 | 0.0093 |
0.2513 | 12700 | 0.0093 |
0.2532 | 12800 | 0.0092 |
0.2552 | 12900 | 0.0094 |
0.2572 | 13000 | 0.0095 |
0.2592 | 13100 | 0.0093 |
0.2612 | 13200 | 0.009 |
0.2631 | 13300 | 0.0087 |
0.2651 | 13400 | 0.0089 |
0.2671 | 13500 | 0.009 |
0.2691 | 13600 | 0.0091 |
0.2710 | 13700 | 0.0092 |
0.2730 | 13800 | 0.0089 |
0.2750 | 13900 | 0.0091 |
0.2770 | 14000 | 0.0092 |
0.2790 | 14100 | 0.0088 |
0.2809 | 14200 | 0.009 |
0.2829 | 14300 | 0.0091 |
0.2849 | 14400 | 0.0086 |
0.2869 | 14500 | 0.009 |
0.2888 | 14600 | 0.0088 |
0.2908 | 14700 | 0.0092 |
0.2928 | 14800 | 0.009 |
0.2948 | 14900 | 0.0088 |
0.2968 | 15000 | 0.0087 |
0.2987 | 15100 | 0.0085 |
0.3007 | 15200 | 0.009 |
0.3027 | 15300 | 0.0088 |
0.3047 | 15400 | 0.0086 |
0.3067 | 15500 | 0.0087 |
0.3086 | 15600 | 0.0088 |
0.3106 | 15700 | 0.0085 |
0.3126 | 15800 | 0.0088 |
0.3146 | 15900 | 0.0085 |
0.3165 | 16000 | 0.0086 |
0.3185 | 16100 | 0.0086 |
0.3205 | 16200 | 0.0087 |
0.3225 | 16300 | 0.0088 |
0.3245 | 16400 | 0.0087 |
0.3264 | 16500 | 0.0087 |
0.3284 | 16600 | 0.0086 |
0.3304 | 16700 | 0.0087 |
0.3324 | 16800 | 0.0092 |
0.3344 | 16900 | 0.0085 |
0.3363 | 17000 | 0.0088 |
0.3383 | 17100 | 0.0084 |
0.3403 | 17200 | 0.0088 |
0.3423 | 17300 | 0.0083 |
0.3442 | 17400 | 0.0085 |
0.3462 | 17500 | 0.0083 |
0.3482 | 17600 | 0.0084 |
0.3502 | 17700 | 0.0084 |
0.3522 | 17800 | 0.0083 |
0.3541 | 17900 | 0.0087 |
0.3561 | 18000 | 0.0083 |
0.3581 | 18100 | 0.0085 |
0.3601 | 18200 | 0.0082 |
0.3621 | 18300 | 0.0079 |
0.3640 | 18400 | 0.0085 |
0.3660 | 18500 | 0.0084 |
0.3680 | 18600 | 0.0082 |
0.3700 | 18700 | 0.0083 |
0.3719 | 18800 | 0.0082 |
0.3739 | 18900 | 0.0082 |
0.3759 | 19000 | 0.0083 |
0.3779 | 19100 | 0.0081 |
0.3799 | 19200 | 0.0083 |
0.3818 | 19300 | 0.0079 |
0.3838 | 19400 | 0.0083 |
0.3858 | 19500 | 0.0082 |
0.3878 | 19600 | 0.0084 |
0.3897 | 19700 | 0.0084 |
0.3917 | 19800 | 0.008 |
0.3937 | 19900 | 0.0081 |
0.3957 | 20000 | 0.0083 |
0.3977 | 20100 | 0.0082 |
0.3996 | 20200 | 0.0078 |
0.4016 | 20300 | 0.0079 |
0.4036 | 20400 | 0.0081 |
0.4056 | 20500 | 0.0085 |
0.4076 | 20600 | 0.0082 |
0.4095 | 20700 | 0.008 |
0.4115 | 20800 | 0.0079 |
0.4135 | 20900 | 0.0081 |
0.4155 | 21000 | 0.008 |
0.4174 | 21100 | 0.0079 |
0.4194 | 21200 | 0.0077 |
0.4214 | 21300 | 0.0078 |
0.4234 | 21400 | 0.0082 |
0.4254 | 21500 | 0.008 |
0.4273 | 21600 | 0.0076 |
0.4293 | 21700 | 0.0075 |
0.4313 | 21800 | 0.0078 |
0.4333 | 21900 | 0.0081 |
0.4353 | 22000 | 0.0077 |
0.4372 | 22100 | 0.0079 |
0.4392 | 22200 | 0.0078 |
0.4412 | 22300 | 0.0078 |
0.4432 | 22400 | 0.0077 |
0.4451 | 22500 | 0.0078 |
0.4471 | 22600 | 0.0079 |
0.4491 | 22700 | 0.0078 |
0.4511 | 22800 | 0.0079 |
0.4531 | 22900 | 0.0075 |
0.4550 | 23000 | 0.0077 |
0.4570 | 23100 | 0.0076 |
0.4590 | 23200 | 0.0078 |
0.4610 | 23300 | 0.0075 |
0.4629 | 23400 | 0.0075 |
0.4649 | 23500 | 0.0078 |
0.4669 | 23600 | 0.0075 |
0.4689 | 23700 | 0.0076 |
0.4709 | 23800 | 0.0075 |
0.4728 | 23900 | 0.0075 |
0.4748 | 24000 | 0.0075 |
0.4768 | 24100 | 0.0076 |
0.4788 | 24200 | 0.0079 |
0.4808 | 24300 | 0.0076 |
0.4827 | 24400 | 0.0077 |
0.4847 | 24500 | 0.0077 |
0.4867 | 24600 | 0.0073 |
0.4887 | 24700 | 0.0077 |
0.4906 | 24800 | 0.0076 |
0.4926 | 24900 | 0.0075 |
0.4946 | 25000 | 0.0076 |
0.4966 | 25100 | 0.0078 |
0.4986 | 25200 | 0.0077 |
0.5005 | 25300 | 0.0076 |
0.5025 | 25400 | 0.0076 |
0.5045 | 25500 | 0.0076 |
0.5065 | 25600 | 0.0073 |
0.5085 | 25700 | 0.0075 |
0.5104 | 25800 | 0.0072 |
0.5124 | 25900 | 0.0074 |
0.5144 | 26000 | 0.0075 |
0.5164 | 26100 | 0.0075 |
0.5183 | 26200 | 0.0072 |
0.5203 | 26300 | 0.0073 |
0.5223 | 26400 | 0.0073 |
0.5243 | 26500 | 0.0073 |
0.5263 | 26600 | 0.0076 |
0.5282 | 26700 | 0.0075 |
0.5302 | 26800 | 0.0075 |
0.5322 | 26900 | 0.0071 |
0.5342 | 27000 | 0.0074 |
0.5362 | 27100 | 0.0073 |
0.5381 | 27200 | 0.0072 |
0.5401 | 27300 | 0.0071 |
0.5421 | 27400 | 0.0073 |
0.5441 | 27500 | 0.0072 |
0.5460 | 27600 | 0.0076 |
0.5480 | 27700 | 0.0072 |
0.5500 | 27800 | 0.0074 |
0.5520 | 27900 | 0.0072 |
0.5540 | 28000 | 0.0072 |
0.5559 | 28100 | 0.0071 |
0.5579 | 28200 | 0.0069 |
0.5599 | 28300 | 0.0071 |
0.5619 | 28400 | 0.0075 |
0.5638 | 28500 | 0.0074 |
0.5658 | 28600 | 0.0072 |
0.5678 | 28700 | 0.0074 |
0.5698 | 28800 | 0.0072 |
0.5718 | 28900 | 0.0072 |
0.5737 | 29000 | 0.0073 |
0.5757 | 29100 | 0.0072 |
0.5777 | 29200 | 0.0069 |
0.5797 | 29300 | 0.0069 |
0.5817 | 29400 | 0.007 |
0.5836 | 29500 | 0.0071 |
0.5856 | 29600 | 0.007 |
0.5876 | 29700 | 0.0069 |
0.5896 | 29800 | 0.0072 |
0.5915 | 29900 | 0.007 |
0.5935 | 30000 | 0.007 |
0.5955 | 30100 | 0.007 |
0.5975 | 30200 | 0.0069 |
0.5995 | 30300 | 0.0068 |
0.6014 | 30400 | 0.0071 |
0.6034 | 30500 | 0.007 |
0.6054 | 30600 | 0.0071 |
0.6074 | 30700 | 0.007 |
0.6094 | 30800 | 0.0069 |
0.6113 | 30900 | 0.007 |
0.6133 | 31000 | 0.0071 |
0.6153 | 31100 | 0.0069 |
0.6173 | 31200 | 0.007 |
0.6192 | 31300 | 0.0068 |
0.6212 | 31400 | 0.0069 |
0.6232 | 31500 | 0.0068 |
0.6252 | 31600 | 0.0068 |
0.6272 | 31700 | 0.007 |
0.6291 | 31800 | 0.0068 |
0.6311 | 31900 | 0.0069 |
0.6331 | 32000 | 0.0068 |
0.6351 | 32100 | 0.0069 |
0.6370 | 32200 | 0.0066 |
0.6390 | 32300 | 0.0068 |
0.6410 | 32400 | 0.0067 |
0.6430 | 32500 | 0.0068 |
0.6450 | 32600 | 0.0069 |
0.6469 | 32700 | 0.0068 |
0.6489 | 32800 | 0.0065 |
0.6509 | 32900 | 0.0068 |
0.6529 | 33000 | 0.0067 |
0.6549 | 33100 | 0.0066 |
0.6568 | 33200 | 0.0069 |
0.6588 | 33300 | 0.0067 |
0.6608 | 33400 | 0.0067 |
0.6628 | 33500 | 0.0068 |
0.6647 | 33600 | 0.0066 |
0.6667 | 33700 | 0.0069 |
0.6687 | 33800 | 0.0069 |
0.6707 | 33900 | 0.0064 |
0.6727 | 34000 | 0.0065 |
0.6746 | 34100 | 0.0067 |
0.6766 | 34200 | 0.0063 |
0.6786 | 34300 | 0.0067 |
0.6806 | 34400 | 0.0066 |
0.6826 | 34500 | 0.0065 |
0.6845 | 34600 | 0.0064 |
0.6865 | 34700 | 0.0066 |
0.6885 | 34800 | 0.0065 |
0.6905 | 34900 | 0.0064 |
0.6924 | 35000 | 0.0066 |
0.6944 | 35100 | 0.0064 |
0.6964 | 35200 | 0.0064 |
0.6984 | 35300 | 0.0066 |
0.7004 | 35400 | 0.0065 |
0.7023 | 35500 | 0.0067 |
0.7043 | 35600 | 0.0065 |
0.7063 | 35700 | 0.0064 |
0.7083 | 35800 | 0.0066 |
0.7103 | 35900 | 0.0065 |
0.7122 | 36000 | 0.0067 |
0.7142 | 36100 | 0.0069 |
0.7162 | 36200 | 0.0065 |
0.7182 | 36300 | 0.0064 |
0.7201 | 36400 | 0.0064 |
0.7221 | 36500 | 0.0066 |
0.7241 | 36600 | 0.0065 |
0.7261 | 36700 | 0.0062 |
0.7281 | 36800 | 0.0068 |
0.7300 | 36900 | 0.0064 |
0.7320 | 37000 | 0.0067 |
0.7340 | 37100 | 0.0063 |
0.7360 | 37200 | 0.0063 |
0.7379 | 37300 | 0.0064 |
0.7399 | 37400 | 0.0066 |
0.7419 | 37500 | 0.0065 |
0.7439 | 37600 | 0.0064 |
0.7459 | 37700 | 0.0065 |
0.7478 | 37800 | 0.0064 |
0.7498 | 37900 | 0.0063 |
0.7518 | 38000 | 0.0062 |
0.7538 | 38100 | 0.0064 |
0.7558 | 38200 | 0.0062 |
0.7577 | 38300 | 0.0064 |
0.7597 | 38400 | 0.0063 |
0.7617 | 38500 | 0.0063 |
0.7637 | 38600 | 0.0065 |
0.7656 | 38700 | 0.0063 |
0.7676 | 38800 | 0.0064 |
0.7696 | 38900 | 0.0062 |
0.7716 | 39000 | 0.0062 |
0.7736 | 39100 | 0.0062 |
0.7755 | 39200 | 0.0063 |
0.7775 | 39300 | 0.0065 |
0.7795 | 39400 | 0.0061 |
0.7815 | 39500 | 0.0062 |
0.7835 | 39600 | 0.0063 |
0.7854 | 39700 | 0.0062 |
0.7874 | 39800 | 0.0062 |
0.7894 | 39900 | 0.0063 |
0.7914 | 40000 | 0.0059 |
0.7933 | 40100 | 0.0063 |
0.7953 | 40200 | 0.0064 |
0.7973 | 40300 | 0.006 |
0.7993 | 40400 | 0.0063 |
0.8013 | 40500 | 0.0061 |
0.8032 | 40600 | 0.0061 |
0.8052 | 40700 | 0.0062 |
0.8072 | 40800 | 0.0062 |
0.8092 | 40900 | 0.006 |
0.8112 | 41000 | 0.0061 |
0.8131 | 41100 | 0.0063 |
0.8151 | 41200 | 0.0059 |
0.8171 | 41300 | 0.0062 |
0.8191 | 41400 | 0.0062 |
0.8210 | 41500 | 0.0062 |
0.8230 | 41600 | 0.0062 |
0.8250 | 41700 | 0.0061 |
0.8270 | 41800 | 0.0061 |
0.8290 | 41900 | 0.0061 |
0.8309 | 42000 | 0.0063 |
0.8329 | 42100 | 0.0064 |
0.8349 | 42200 | 0.0063 |
0.8369 | 42300 | 0.0063 |
0.8388 | 42400 | 0.0061 |
0.8408 | 42500 | 0.0062 |
0.8428 | 42600 | 0.0062 |
0.8448 | 42700 | 0.0061 |
0.8468 | 42800 | 0.0059 |
0.8487 | 42900 | 0.006 |
0.8507 | 43000 | 0.0061 |
0.8527 | 43100 | 0.0062 |
0.8547 | 43200 | 0.0058 |
0.8567 | 43300 | 0.0065 |
0.8586 | 43400 | 0.0064 |
0.8606 | 43500 | 0.006 |
0.8626 | 43600 | 0.0061 |
0.8646 | 43700 | 0.0059 |
0.8665 | 43800 | 0.0063 |
0.8685 | 43900 | 0.0061 |
0.8705 | 44000 | 0.006 |
0.8725 | 44100 | 0.0061 |
0.8745 | 44200 | 0.0061 |
0.8764 | 44300 | 0.0059 |
0.8784 | 44400 | 0.006 |
0.8804 | 44500 | 0.006 |
0.8824 | 44600 | 0.0059 |
0.8844 | 44700 | 0.0062 |
0.8863 | 44800 | 0.006 |
0.8883 | 44900 | 0.006 |
0.8903 | 45000 | 0.0058 |
0.8923 | 45100 | 0.006 |
0.8942 | 45200 | 0.0061 |
0.8962 | 45300 | 0.006 |
0.8982 | 45400 | 0.0059 |
0.9002 | 45500 | 0.0059 |
0.9022 | 45600 | 0.006 |
0.9041 | 45700 | 0.0062 |
0.9061 | 45800 | 0.0056 |
0.9081 | 45900 | 0.0057 |
0.9101 | 46000 | 0.006 |
0.9120 | 46100 | 0.0059 |
0.9140 | 46200 | 0.006 |
0.9160 | 46300 | 0.0059 |
0.9180 | 46400 | 0.0062 |
0.9200 | 46500 | 0.0059 |
0.9219 | 46600 | 0.0059 |
0.9239 | 46700 | 0.006 |
0.9259 | 46800 | 0.0059 |
0.9279 | 46900 | 0.0058 |
0.9299 | 47000 | 0.0057 |
0.9318 | 47100 | 0.0058 |
0.9338 | 47200 | 0.0058 |
0.9358 | 47300 | 0.0059 |
0.9378 | 47400 | 0.0059 |
0.9397 | 47500 | 0.0058 |
0.9417 | 47600 | 0.006 |
0.9437 | 47700 | 0.0058 |
0.9457 | 47800 | 0.006 |
0.9477 | 47900 | 0.0059 |
0.9496 | 48000 | 0.0058 |
0.9516 | 48100 | 0.0057 |
0.9536 | 48200 | 0.006 |
0.9556 | 48300 | 0.0057 |
0.9576 | 48400 | 0.006 |
0.9595 | 48500 | 0.0058 |
0.9615 | 48600 | 0.0058 |
0.9635 | 48700 | 0.0058 |
0.9655 | 48800 | 0.0057 |
0.9674 | 48900 | 0.0058 |
0.9694 | 49000 | 0.006 |
0.9714 | 49100 | 0.0055 |
0.9734 | 49200 | 0.0058 |
0.9754 | 49300 | 0.0059 |
0.9773 | 49400 | 0.0057 |
0.9793 | 49500 | 0.0055 |
0.9813 | 49600 | 0.0059 |
0.9833 | 49700 | 0.0058 |
0.9853 | 49800 | 0.0059 |
0.9872 | 49900 | 0.0058 |
0.9892 | 50000 | 0.0056 |
0.9912 | 50100 | 0.0058 |
0.9932 | 50200 | 0.0058 |
0.9951 | 50300 | 0.0059 |
0.9971 | 50400 | 0.0059 |
0.9991 | 50500 | 0.006 |
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.3.0
- PyLate: 1.1.4
- Transformers: 4.48.0.dev0
- PyTorch: 2.4.0
- Accelerate: 1.2.1
- Datasets: 2.21.0
- Tokenizers: 0.21.0
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}
}
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Model tree for joe32140/ColModernBERT-base-msmarco-en-bge
Base model
answerdotai/ModernBERT-baseDataset used to train joe32140/ColModernBERT-base-msmarco-en-bge
Collection including joe32140/ColModernBERT-base-msmarco-en-bge
Evaluation results
- ndcg_at_10 on FiQAtest set self-reported39.860
- ndcg_at_10 on SciFacttest set self-reported73.670
- ndcg_at_10 on nfcorpustest set self-reported33.980
- ndcg_at_10 on arguanatest set self-reported30.980