ayushexel/colbert-ModernBERT-base-2-neg-4-epoch-gooaq-1995000

PyLate model based on answerdotai/ModernBERT-base

This is a PyLate model finetuned from answerdotai/ModernBERT-base. 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: answerdotai/ModernBERT-base
• Document Length: 180 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': 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

Metrics

Col BERTTriplet

• Evaluated with pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator

Metric	Value
accuracy	0.5332

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,787,118 training samples
• Columns: question, answer, and negative
• Approximate statistics based on the first 1000 samples:

  	question	answer	negative
  type	string	string	string
  details	min: 9 tokens mean: 13.17 tokens max: 21 tokens	min: 17 tokens mean: 31.79 tokens max: 32 tokens	min: 15 tokens mean: 31.69 tokens max: 32 tokens

• Samples:

  question	answer	negative
  what does it mean when you dream you are a vampire?	Dream Bible - Dream Interpretation of Vampires. To see a vampire in your dream symbolizes an aspect of your personality that is parasitic or selfishly feeds off others. A person or situation that drains you of time, energy, or resources. ... To dream of being a vampire represents a selfish need to use or feed off others.	Many people believe that witches, warlocks, vampires, werewolves are necessarily seen as a sign of something bad. ... If you dream that a vampire is chasing you, but it is not able to catch you, it means that you are afraid of someone, but your fear is groundless, this person will not hurt you.
  what does it mean when you dream you are a vampire?	Dream Bible - Dream Interpretation of Vampires. To see a vampire in your dream symbolizes an aspect of your personality that is parasitic or selfishly feeds off others. A person or situation that drains you of time, energy, or resources. ... To dream of being a vampire represents a selfish need to use or feed off others.	Blood on the ground in a dream indicates that the dreamer should be careful of unusual or new friendships. ... To lose blood in your dream represents that you may be tired in your waking life and that you feel emotional. If you dream you are in the hospital and you see blood, it means that past actions may haunt you.
  what is the difference between thrombosis and atherosclerosis?	Arterial thrombosis usually affects people whose arteries are clogged with fatty deposits. This is known as atherosclerosis. These deposits cause the arteries to harden and narrow over time and increase the risk of blood clots.	Advertisement. Atherosclerosis is a specific type of arteriosclerosis, but the terms are sometimes used interchangeably. Atherosclerosis refers to the buildup of fats, cholesterol and other substances in and on your artery walls (plaque), which can restrict blood flow.

• Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

Unnamed Dataset

• Size: 5,000 evaluation samples
• Columns: question, answer, and negative_1
• Approximate statistics based on the first 1000 samples:

  	question	answer	negative_1
  type	string	string	string
  details	min: 9 tokens mean: 12.96 tokens max: 24 tokens	min: 16 tokens mean: 31.73 tokens max: 32 tokens	min: 14 tokens mean: 31.43 tokens max: 32 tokens

• Samples:

  question	answer	negative_1
  are upc codes and barcodes the same?	The UPC-A barcode was the original format for product barcodes. ... The only major difference is the placement of the numbers below (human readable numbers) which are there only as a back-up in case the barcode doesn't scan properly and the information has to be manually entered into the point of sale system.	Barcodes do not support any of the mentioned attributes. Thus, QR Codes will not be replaced by barcodes.
  what does it mean when it says your application is under review?	"Under review" is a phrase that typically means your application is being screened by human resources or the hiring manager. "Applicants being selected" indicates that hiring managers are selecting candidates for interviews. "Referred to hiring manager" means your application has passed initial HR screening.	There are two different types of review: In Review and Under Review. If your verification status is In Review, this means that your ID uploaded successfully and your submission is being reviewed automatically. Automated reviews typically take anywhere from 5 minutes to 2 hours.
  when was the last time the kansas city chiefs won a playoff game?	The Chiefs were overwhelmed by the Bills and lost the game by a score of 30–13. The Chiefs' victory on January 16, 1994, against the Oilers remained the franchise's last post-season victory for 21 years until their 30–0 victory over the Houston Texans on January 9, 2016.	The Broncos have not beaten the Chiefs since a Week 2 game in Kansas City in 2015. Kansas City's streak is the second longest in the series' history. Only the Chiefs' 11-game winning streak from 1964 to 1969 lasted longer.

• Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 3e-06
• num_train_epochs: 5
• warmup_ratio: 0.1
• seed: 12
• bf16: True
• dataloader_num_workers: 12
• load_best_model_at_end: 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: 128
• per_device_eval_batch_size: 128
• 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-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: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: 12
• 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: 12
• 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: False
• 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

Click to expand

Epoch	Step	Training Loss	Validation Loss	accuracy
0	0	-	-	0.4392
0.0000	1	22.1457	-	-
0.0068	200	19.4909	-	-
0.0135	400	13.7828	-	-
0.0203	600	9.9129	-	-
0.0270	800	7.6419	-	-
0.0338	1000	6.3482	-	-
0.0406	1200	5.7019	-	-
0.0473	1400	5.2986	-	-
0.0541	1600	4.741	-	-
0.0608	1800	3.7375	-	-
0.0676	2000	2.7031	-	-
0.0744	2200	2.0178	-	-
0.0811	2400	1.6433	-	-
0.0879	2600	1.3954	-	-
0.0946	2800	1.2457	-	-
0.1014	3000	1.1331	-	-
0.1082	3200	1.0455	-	-
0.1149	3400	0.9814	-	-
0.1217	3600	0.9191	-	-
0.1284	3800	0.8788	-	-
0.1352	4000	0.8551	-	-
0.1420	4200	0.8079	-	-
0.1487	4400	0.7789	-	-
0.1555	4600	0.7448	-	-
0.1622	4800	0.7439	-	-
0.1690	5000	0.7255	-	-
0.1758	5200	0.6902	-	-
0.1825	5400	0.6737	-	-
0.1893	5600	0.6621	-	-
0.1960	5800	0.6438	-	-
0.2028	6000	0.6099	-	-
0.2096	6200	0.6171	-	-
0.2163	6400	0.5861	-	-
0.2231	6600	0.5788	-	-
0.2298	6800	0.5736	-	-
0.2366	7000	0.5608	-	-
0.2434	7200	0.5457	-	-
0.2501	7400	0.5339	-	-
0.2569	7600	0.5046	-	-
0.2636	7800	0.513	-	-
0.2704	8000	0.5087	-	-
0.2771	8200	0.4826	-	-
0.2839	8400	0.4836	-	-
0.2907	8600	0.4761	-	-
0.2974	8800	0.4655	-	-
0.3042	9000	0.4421	-	-
0.3109	9200	0.4436	-	-
0.3177	9400	0.4418	-	-
0.3245	9600	0.4272	-	-
0.3312	9800	0.4334	-	-
0.3380	10000	0.417	-	-
0.3447	10200	0.4033	-	-
0.3515	10400	0.4008	-	-
0.3583	10600	0.3986	-	-
0.3650	10800	0.3865	-	-
0.3718	11000	0.3846	-	-
0.3785	11200	0.3803	-	-
0.3853	11400	0.373	-	-
0.3921	11600	0.3741	-	-
0.3988	11800	0.3588	-	-
0.4056	12000	0.3568	-	-
0.4123	12200	0.3486	-	-
0.4191	12400	0.3439	-	-
0.4259	12600	0.3373	-	-
0.4326	12800	0.3396	-	-
0.4394	13000	0.3283	-	-
0.4461	13200	0.3267	-	-
0.4529	13400	0.3235	-	-
0.4597	13600	0.3058	-	-
0.4664	13800	0.3059	-	-
0.4732	14000	0.3071	-	-
0.4799	14200	0.299	-	-
0.4867	14400	0.2963	-	-
0.4935	14600	0.2941	-	-
0.5002	14800	0.286	-	-
0.5070	15000	0.2894	-	-
0.5137	15200	0.276	-	-
0.5205	15400	0.2741	-	-
0.5273	15600	0.2772	-	-
0.5340	15800	0.2751	-	-
0.5408	16000	0.2613	-	-
0.5475	16200	0.2621	-	-
0.5543	16400	0.2615	-	-
0.5611	16600	0.2642	-	-
0.5678	16800	0.2527	-	-
0.5746	17000	0.248	-	-
0.5813	17200	0.2453	-	-
0.5881	17400	0.2478	-	-
0.5949	17600	0.2469	-	-
0.6016	17800	0.2478	-	-
0.6084	18000	0.2443	-	-
0.6151	18200	0.2473	-	-
0.6219	18400	0.2394	-	-
0.6287	18600	0.2343	-	-
0.6354	18800	0.2368	-	-
0.6422	19000	0.2317	-	-
0.6489	19200	0.2337	-	-
0.6557	19400	0.2325	-	-
0.6625	19600	0.2272	-	-
0.6692	19800	0.2303	-	-
0.6760	20000	0.2191	-	-
0	0	-	-	0.5022
0.6760	20000	-	1.0284	-
0.6827	20200	0.2258	-	-
0.6895	20400	0.2173	-	-
0.6963	20600	0.2183	-	-
0.7030	20800	0.2117	-	-
0.7098	21000	0.208	-	-
0.7165	21200	0.2173	-	-
0.7233	21400	0.2129	-	-
0.7301	21600	0.2092	-	-
0.7368	21800	0.212	-	-
0.7436	22000	0.2076	-	-
0.7503	22200	0.2113	-	-
0.7571	22400	0.2027	-	-
0.7638	22600	0.2069	-	-
0.7706	22800	0.2015	-	-
0.7774	23000	0.2041	-	-
0.7841	23200	0.2059	-	-
0.7909	23400	0.2073	-	-
0.7976	23600	0.2018	-	-
0.8044	23800	0.2006	-	-
0.8112	24000	0.1998	-	-
0.8179	24200	0.2009	-	-
0.8247	24400	0.1971	-	-
0.8314	24600	0.1956	-	-
0.8382	24800	0.1943	-	-
0.8450	25000	0.1876	-	-
0.8517	25200	0.2	-	-
0.8585	25400	0.1892	-	-
0.8652	25600	0.1923	-	-
0.8720	25800	0.1879	-	-
0.8788	26000	0.1874	-	-
0.8855	26200	0.1868	-	-
0.8923	26400	0.1853	-	-
0.8990	26600	0.1869	-	-
0.9058	26800	0.1879	-	-
0.9126	27000	0.1862	-	-
0.9193	27200	0.1891	-	-
0.9261	27400	0.1793	-	-
0.9328	27600	0.1786	-	-
0.9396	27800	0.1736	-	-
0.9464	28000	0.1787	-	-
0.9531	28200	0.1874	-	-
0.9599	28400	0.1798	-	-
0.9666	28600	0.1818	-	-
0.9734	28800	0.1799	-	-
0.9802	29000	0.1763	-	-
0.9869	29200	0.1758	-	-
0.9937	29400	0.1799	-	-
1.0004	29600	0.1737	-	-
1.0072	29800	0.17	-	-
1.0140	30000	0.1671	-	-
1.0207	30200	0.168	-	-
1.0275	30400	0.1703	-	-
1.0342	30600	0.1611	-	-
1.0410	30800	0.1629	-	-
1.0478	31000	0.1621	-	-
1.0545	31200	0.1612	-	-
1.0613	31400	0.1607	-	-
1.0680	31600	0.1623	-	-
1.0748	31800	0.1659	-	-
1.0816	32000	0.1619	-	-
1.0883	32200	0.1595	-	-
1.0951	32400	0.1649	-	-
1.1018	32600	0.16	-	-
1.1086	32800	0.1573	-	-
1.1154	33000	0.1624	-	-
1.1221	33200	0.159	-	-
1.1289	33400	0.1596	-	-
1.1356	33600	0.1573	-	-
1.1424	33800	0.1544	-	-
1.1492	34000	0.1512	-	-
1.1559	34200	0.1592	-	-
1.1627	34400	0.1602	-	-
1.1694	34600	0.1562	-	-
1.1762	34800	0.1531	-	-
1.1830	35000	0.1504	-	-
1.1897	35200	0.1533	-	-
1.1965	35400	0.1578	-	-
1.2032	35600	0.155	-	-
1.2100	35800	0.1539	-	-
1.2168	36000	0.1514	-	-
1.2235	36200	0.157	-	-
1.2303	36400	0.1586	-	-
1.2370	36600	0.151	-	-
1.2438	36800	0.1522	-	-
1.2505	37000	0.1521	-	-
1.2573	37200	0.1457	-	-
1.2641	37400	0.1465	-	-
1.2708	37600	0.1513	-	-
1.2776	37800	0.1478	-	-
1.2843	38000	0.1529	-	-
1.2911	38200	0.1486	-	-
1.2979	38400	0.149	-	-
1.3046	38600	0.1516	-	-
1.3114	38800	0.1486	-	-
1.3181	39000	0.1423	-	-
1.3249	39200	0.1479	-	-
1.3317	39400	0.1499	-	-
1.3384	39600	0.1455	-	-
1.3452	39800	0.1482	-	-
1.3519	40000	0.1471	-	-
0	0	-	-	0.5316
1.3519	40000	-	0.9581	-
1.3587	40200	0.1504	-	-
1.3655	40400	0.1494	-	-
1.3722	40600	0.147	-	-
1.3790	40800	0.1457	-	-
1.3857	41000	0.1494	-	-
1.3925	41200	0.1428	-	-
1.3993	41400	0.146	-	-
1.4060	41600	0.1487	-	-
1.4128	41800	0.1406	-	-
1.4195	42000	0.1483	-	-
1.4263	42200	0.1417	-	-
1.4331	42400	0.1408	-	-
1.4398	42600	0.1493	-	-
1.4466	42800	0.1465	-	-
1.4533	43000	0.1423	-	-
1.4601	43200	0.1438	-	-
1.4669	43400	0.1432	-	-
1.4736	43600	0.1426	-	-
1.4804	43800	0.1387	-	-
1.4871	44000	0.1455	-	-
1.4939	44200	0.1405	-	-
1.5007	44400	0.1391	-	-
1.5074	44600	0.1433	-	-
1.5142	44800	0.1424	-	-
1.5209	45000	0.1397	-	-
1.5277	45200	0.1391	-	-
1.5345	45400	0.1464	-	-
1.5412	45600	0.1351	-	-
1.5480	45800	0.1362	-	-
1.5547	46000	0.1366	-	-
1.5615	46200	0.1402	-	-
1.5683	46400	0.1339	-	-
1.5750	46600	0.1348	-	-
1.5818	46800	0.1394	-	-
1.5885	47000	0.1413	-	-
1.5953	47200	0.1394	-	-
1.6021	47400	0.14	-	-
1.6088	47600	0.138	-	-
1.6156	47800	0.1387	-	-
1.6223	48000	0.14	-	-
1.6291	48200	0.1372	-	-
1.6359	48400	0.1379	-	-
1.6426	48600	0.1332	-	-
1.6494	48800	0.1341	-	-
1.6561	49000	0.1319	-	-
1.6629	49200	0.1363	-	-
1.6697	49400	0.1327	-	-
1.6764	49600	0.1297	-	-
1.6832	49800	0.1345	-	-
1.6899	50000	0.1362	-	-
1.6967	50200	0.1323	-	-
1.7035	50400	0.1351	-	-
1.7102	50600	0.1334	-	-
1.7170	50800	0.136	-	-
1.7237	51000	0.1353	-	-
1.7305	51200	0.1271	-	-
1.7372	51400	0.1345	-	-
1.7440	51600	0.1347	-	-
1.7508	51800	0.1254	-	-
1.7575	52000	0.1308	-	-
1.7643	52200	0.132	-	-
1.7710	52400	0.1275	-	-
1.7778	52600	0.1306	-	-
1.7846	52800	0.1277	-	-
1.7913	53000	0.1304	-	-
1.7981	53200	0.1319	-	-
1.8048	53400	0.1339	-	-
1.8116	53600	0.1332	-	-
1.8184	53800	0.1293	-	-
1.8251	54000	0.13	-	-
1.8319	54200	0.1333	-	-
1.8386	54400	0.1273	-	-
1.8454	54600	0.1296	-	-
1.8522	54800	0.125	-	-
1.8589	55000	0.1332	-	-
1.8657	55200	0.1288	-	-
1.8724	55400	0.1233	-	-
1.8792	55600	0.1282	-	-
1.8860	55800	0.1341	-	-
1.8927	56000	0.1275	-	-
1.8995	56200	0.1286	-	-
1.9062	56400	0.1279	-	-
1.9130	56600	0.126	-	-
1.9198	56800	0.1306	-	-
1.9265	57000	0.1289	-	-
1.9333	57200	0.1219	-	-
1.9400	57400	0.1271	-	-
1.9468	57600	0.1262	-	-
1.9536	57800	0.1253	-	-
1.9603	58000	0.1283	-	-
1.9671	58200	0.1314	-	-
1.9738	58400	0.1237	-	-
1.9806	58600	0.1239	-	-
1.9874	58800	0.1242	-	-
1.9941	59000	0.1257	-	-
2.0009	59200	0.1228	-	-
2.0076	59400	0.1106	-	-
2.0144	59600	0.1143	-	-
2.0212	59800	0.1132	-	-
2.0279	60000	0.1122	-	-
0	0	-	-	0.5386
2.0279	60000	-	1.0542	-
2.0347	60200	0.116	-	-
2.0414	60400	0.1081	-	-
2.0482	60600	0.1134	-	-
2.0550	60800	0.1139	-	-
2.0617	61000	0.106	-	-
2.0685	61200	0.1131	-	-
2.0752	61400	0.1121	-	-
2.0820	61600	0.1077	-	-
2.0888	61800	0.112	-	-
2.0955	62000	0.1087	-	-
2.1023	62200	0.1155	-	-
2.1090	62400	0.1118	-	-
2.1158	62600	0.1107	-	-
2.1226	62800	0.1067	-	-
2.1293	63000	0.1079	-	-
2.1361	63200	0.1094	-	-
2.1428	63400	0.1114	-	-
2.1496	63600	0.1094	-	-
2.1564	63800	0.1056	-	-
2.1631	64000	0.1095	-	-
2.1699	64200	0.1074	-	-
2.1766	64400	0.107	-	-
2.1834	64600	0.1124	-	-
2.1902	64800	0.1121	-	-
2.1969	65000	0.1115	-	-
2.2037	65200	0.109	-	-
2.2104	65400	0.1158	-	-
2.2172	65600	0.1068	-	-
2.2239	65800	0.1095	-	-
2.2307	66000	0.1062	-	-
2.2375	66200	0.1063	-	-
2.2442	66400	0.108	-	-
2.2510	66600	0.1128	-	-
2.2577	66800	0.1082	-	-
2.2645	67000	0.1066	-	-
2.2713	67200	0.1101	-	-
2.2780	67400	0.1072	-	-
2.2848	67600	0.109	-	-
2.2915	67800	0.1069	-	-
2.2983	68000	0.1136	-	-
2.3051	68200	0.1108	-	-
2.3118	68400	0.109	-	-
2.3186	68600	0.1139	-	-
2.3253	68800	0.1081	-	-
2.3321	69000	0.1082	-	-
2.3389	69200	0.1064	-	-
2.3456	69400	0.1064	-	-
2.3524	69600	0.1084	-	-
2.3591	69800	0.1058	-	-
2.3659	70000	0.1109	-	-
2.3727	70200	0.105	-	-
2.3794	70400	0.1096	-	-
2.3862	70600	0.1064	-	-
2.3929	70800	0.1079	-	-
2.3997	71000	0.1038	-	-
2.4065	71200	0.1072	-	-
2.4132	71400	0.1097	-	-
2.4200	71600	0.1098	-	-
2.4267	71800	0.1067	-	-
2.4335	72000	0.1098	-	-
2.4403	72200	0.1088	-	-
2.4470	72400	0.1031	-	-
2.4538	72600	0.1034	-	-
2.4605	72800	0.1046	-	-
2.4673	73000	0.1065	-	-
2.4741	73200	0.1062	-	-
2.4808	73400	0.1064	-	-
2.4876	73600	0.1053	-	-
2.4943	73800	0.1055	-	-
2.5011	74000	0.1057	-	-
2.5079	74200	0.1095	-	-
2.5146	74400	0.1038	-	-
2.5214	74600	0.1025	-	-
2.5281	74800	0.1032	-	-
2.5349	75000	0.1036	-	-
2.5417	75200	0.1021	-	-
2.5484	75400	0.1066	-	-
2.5552	75600	0.1054	-	-
2.5619	75800	0.1028	-	-
2.5687	76000	0.1066	-	-
2.5755	76200	0.1079	-	-
2.5822	76400	0.1083	-	-
2.5890	76600	0.1065	-	-
2.5957	76800	0.1072	-	-
2.6025	77000	0.1031	-	-
2.6093	77200	0.1022	-	-
2.6160	77400	0.1084	-	-
2.6228	77600	0.1068	-	-
2.6295	77800	0.1009	-	-
2.6363	78000	0.098	-	-
2.6431	78200	0.1019	-	-
2.6498	78400	0.1038	-	-
2.6566	78600	0.1032	-	-
2.6633	78800	0.1059	-	-
2.6701	79000	0.1058	-	-
2.6769	79200	0.1049	-	-
2.6836	79400	0.1023	-	-
2.6904	79600	0.1079	-	-
2.6971	79800	0.1061	-	-
2.7039	80000	0.1078	-	-
0	0	-	-	0.5402
2.7039	80000	-	1.0363	-
2.7106	80200	0.1075	-	-
2.7174	80400	0.105	-	-
2.7242	80600	0.1053	-	-
2.7309	80800	0.1007	-	-
2.7377	81000	0.1009	-	-
2.7444	81200	0.1022	-	-
2.7512	81400	0.1017	-	-
2.7580	81600	0.1013	-	-
2.7647	81800	0.1044	-	-
2.7715	82000	0.1045	-	-
2.7782	82200	0.1003	-	-
2.7850	82400	0.1038	-	-
2.7918	82600	0.1049	-	-
2.7985	82800	0.1026	-	-
2.8053	83000	0.1082	-	-
2.8120	83200	0.1033	-	-
2.8188	83400	0.1044	-	-
2.8256	83600	0.1013	-	-
2.8323	83800	0.1057	-	-
2.8391	84000	0.0992	-	-
2.8458	84200	0.0973	-	-
2.8526	84400	0.102	-	-
2.8594	84600	0.1048	-	-
2.8661	84800	0.1013	-	-
2.8729	85000	0.1038	-	-
2.8796	85200	0.1027	-	-
2.8864	85400	0.1037	-	-
2.8932	85600	0.1007	-	-
2.8999	85800	0.1033	-	-
2.9067	86000	0.1016	-	-
2.9134	86200	0.1051	-	-
2.9202	86400	0.1001	-	-
2.9270	86600	0.1003	-	-
2.9337	86800	0.0969	-	-
2.9405	87000	0.1056	-	-
2.9472	87200	0.0997	-	-
2.9540	87400	0.1	-	-
2.9608	87600	0.1008	-	-
2.9675	87800	0.1003	-	-
2.9743	88000	0.0991	-	-
2.9810	88200	0.1022	-	-
2.9878	88400	0.1012	-	-
2.9946	88600	0.0992	-	-
3.0013	88800	0.1002	-	-
3.0081	89000	0.0885	-	-
3.0148	89200	0.0918	-	-
3.0216	89400	0.0888	-	-
3.0284	89600	0.088	-	-
3.0351	89800	0.0893	-	-
3.0419	90000	0.0896	-	-
3.0486	90200	0.0899	-	-
3.0554	90400	0.0918	-	-
3.0622	90600	0.0888	-	-
3.0689	90800	0.0961	-	-
3.0757	91000	0.0867	-	-
3.0824	91200	0.0927	-	-
3.0892	91400	0.0891	-	-
3.0960	91600	0.0864	-	-
3.1027	91800	0.0884	-	-
3.1095	92000	0.0879	-	-
3.1162	92200	0.088	-	-
3.1230	92400	0.086	-	-
3.1298	92600	0.0885	-	-
3.1365	92800	0.0918	-	-
3.1433	93000	0.0885	-	-
3.1500	93200	0.0873	-	-
3.1568	93400	0.0922	-	-
3.1636	93600	0.0878	-	-
3.1703	93800	0.0893	-	-
3.1771	94000	0.0865	-	-
3.1838	94200	0.0888	-	-
3.1906	94400	0.0871	-	-
3.1974	94600	0.0884	-	-
3.2041	94800	0.0858	-	-
3.2109	95000	0.0872	-	-
3.2176	95200	0.0871	-	-
3.2244	95400	0.0887	-	-
3.2311	95600	0.0903	-	-
3.2379	95800	0.0865	-	-
3.2447	96000	0.0901	-	-
3.2514	96200	0.0894	-	-
3.2582	96400	0.0945	-	-
3.2649	96600	0.0851	-	-
3.2717	96800	0.086	-	-
3.2785	97000	0.0884	-	-
3.2852	97200	0.0889	-	-
3.2920	97400	0.0878	-	-
3.2987	97600	0.0872	-	-
3.3055	97800	0.0885	-	-
3.3123	98000	0.0861	-	-
3.3190	98200	0.0854	-	-
3.3258	98400	0.0871	-	-
3.3325	98600	0.0884	-	-
3.3393	98800	0.0833	-	-
3.3461	99000	0.0912	-	-
3.3528	99200	0.0859	-	-
3.3596	99400	0.088	-	-
3.3663	99600	0.0861	-	-
3.3731	99800	0.0908	-	-
3.3799	100000	0.0852	-	-
0	0	-	-	0.5332
3.3799	100000	-	1.1603	-

Framework Versions

• Python: 3.11.0
• Sentence Transformers: 4.0.1
• PyLate: 1.1.7
• Transformers: 4.48.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.6.0
• Datasets: 3.5.0
• 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}
}