raul-delarosa99/bge-small-en-v1.5-RIRAG_ObliQA

SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-small-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 512 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("raul-delarosa99/bge-small-en-v1.5-RIRAG_ObliQA")
# Run inference
sentences = [
    'Could you provide detailed guidance on the acceptable threshold for "significant loss" to clients in the context of IT infrastructure resilience for virtual asset service providers?',
    '60) REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS Security measures and procedures IT infrastructures should be strong enough to resist, without significant loss to Clients, a number of scenarios, including but not limited to: accidental destruction or breach of data, collusion or leakage of information by employees/former employees, successful hack of a cryptographic and hardware security module or server, or access by hackers of any single set of encryption/decryption keys that could result in a complete system breach.',
    'APP8.A8.11.1 An Insurer must calculate the asset management risk component in respect of a Long Term Insurance Fund according to the method set out in Rule A4.13, applied as though all references in that Rule to an Insurer were instead references to that fund.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: qa_eval
• Evaluated with InformationRetrievalEvaluator

Metric	Fine Tunned	Base Model
dot_score_accuracy@10	0.8902	0.7909
dot_score_precision@10	0.0984	0.0842
dot_score_recall@10	0.8142	0.7135
dot_score_ndcg@10	0.6921	0.6009
dot_score_mrr@10	0.6943	0.6015
dot_score_map@10	0.6313	0.5462

Training Details

Training Dataset

Unnamed Dataset

• Size: 29,547 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 15 tokens mean: 34.89 tokens max: 96 tokens	min: 25 tokens mean: 122.21 tokens max: 512 tokens

• Samples:

  anchor	positive
  Under Rules 7.3.2 and 7.3.3, what are the two specific conditions related to the maturity of a financial instrument that would trigger a disclosure requirement?	7.3.4 Events that trigger a disclosure. For the purposes of Rules 7.3.2 and 7.3.3, a Person is taken to hold Financial Instruments in or relating to a Reporting Entity, if the Person holds a Financial Instrument that on its maturity will confer on him: (1) an unconditional right to acquire the Financial Instrument; or (2) the discretion as to his right to acquire the Financial Instrument.
  Best Execution and Transaction Handling: What constitutes 'Best Execution' under Rule 6.5 in the context of virtual assets, and how should Authorised Persons document and demonstrate this?	17.1.4 The following COBS Rules should be read as applying to all Transactions undertaken by an Authorised Person conducting a Regulated Activity in relation to Virtual Assets, irrespective of any restrictions on application or any exception to these Rules elsewhere in COBS - (a) Rule 3.4 (Suitability); (b) Rule 6.5 (Best Execution); (c) Rule 6.7 (Aggregation and Allocation); (d) Rule 6.10 (Confirmation Notes); (e) Rule 6.11 (Periodic Statements); and (f) Chapter 12 (Key Information and Client Agreement).
  How does the FSRA define and evaluate "principal risks and uncertainties" for a Petroleum Reporting Entity, particularly for the remaining six months of the financial year?	10.1.7.(2) A Reporting Entity must: (a) prepare such report: (i) for the first six months of each financial year or period, and if there is a change to the accounting reference date, prepare such report in respect of the period up to the old accounting reference date; and (ii) in accordance with the applicable IFRS standards or other standards acceptable to the Regulator; (b) ensure the financial statements have either been audited or reviewed by auditors, and the audit or review by the auditor is included within the report; and (c) ensure that the report includes: (i) except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, an indication of important events that have occurred during the first six months of the financial year, and their impact on the financial statements; (ii) except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, a description of the principal risks and uncertainties for the remaining six months of the financial year; and (iii) a condensed set of financial statements, an interim management report and associated responsibility statements.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "dot_score"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 512
• gradient_accumulation_steps: 4
• learning_rate: 2e-05
• num_train_epochs: 15
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 512
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 15
• max_steps: -1
• lr_scheduler_type: cosine
• 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: 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: 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_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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	qa_eval_dot_score_map@10
0.0087	1	1.9006	-
0.0173	2	1.9774	-
0.0260	3	1.8898	-
0.0346	4	1.7339	-
0.0433	5	1.7743	-
0.0519	6	1.8456	-
0.0606	7	1.8348	-
0.0693	8	1.7493	-
0.0779	9	1.6397	-
0.0866	10	1.6803	-
0.0952	11	1.6503	-
0.1039	12	1.7065	-
0.1126	13	1.864	-
0.1212	14	1.7811	-
0.1299	15	1.6896	-
0.1385	16	1.7036	-
0.1472	17	1.769	-
0.1558	18	1.8405	-
0.1645	19	1.8686	-
0.1732	20	1.7136	-
0.1818	21	1.9135	-
0.1905	22	1.9542	-
0.1991	23	1.7373	-
0.2078	24	1.7295	-
0.2165	25	1.6764	-
0.2251	26	1.6606	-
0.2338	27	1.5261	-
0.2424	28	1.6864	-
0.2511	29	1.6683	-
0.2597	30	1.6443	-
0.2684	31	1.587	-
0.2771	32	1.5673	-
0.2857	33	1.575	-
0.2944	34	1.5552	-
0.3030	35	1.6614	-
0.3117	36	1.5392	-
0.3203	37	1.5684	-
0.3290	38	1.3392	-
0.3377	39	1.3909	-
0.3463	40	1.3444	-
0.3550	41	1.3642	-
0.3636	42	1.3242	-
0.3723	43	1.4779	-
0.3810	44	1.2574	-
0.3896	45	1.3639	-
0.3983	46	1.2723	-
0.4069	47	1.2563	-
0.4156	48	1.1769	-
0.4242	49	1.2596	-
0.4329	50	1.2703	-
0.4416	51	1.3731	-
0.4502	52	1.163	-
0.4589	53	1.4455	-
0.4675	54	1.0285	-
0.4762	55	1.1616	-
0.4848	56	1.1341	-
0.4935	57	1.163	-
0.5022	58	1.187	-
0.5108	59	1.0557	-
0.5195	60	1.1115	-
0.5281	61	1.0469	-
0.5368	62	1.2311	-
0.5455	63	0.9378	-
0.5541	64	1.0355	-
0.5628	65	1.4079	-
0.5714	66	1.1047	-
0.5801	67	0.9298	-
0.5887	68	1.2011	-
0.5974	69	1.0149	-
0.6061	70	1.1661	-
0.6147	71	0.8674	-
0.6234	72	1.0458	-
0.6320	73	0.9858	-
0.6407	74	1.2019	-
0.6494	75	0.9794	-
0.6580	76	0.9604	-
0.6667	77	1.0405	-
0.6753	78	1.1605	-
0.6840	79	1.0779	-
0.6926	80	1.16	-
0.7013	81	1.2139	-
0.7100	82	1.0696	-
0.7186	83	1.1806	-
0.7273	84	1.1762	-
0.7359	85	0.975	-
0.7446	86	1.1304	-
0.7532	87	1.1431	-
0.7619	88	1.0669	-
0.7706	89	1.0858	-
0.7792	90	1.0494	-
0.7879	91	1.0815	-
0.7965	92	0.9833	-
0.8052	93	1.1571	-
0.8139	94	0.9771	-
0.8225	95	0.88	-
0.8312	96	0.9501	-
0.8398	97	1.2409	-
0.8485	98	1.1644	-
0.8571	99	1.1176	-
0.8658	100	0.8971	-
0.8745	101	1.032	-
0.8831	102	1.0456	-
0.8918	103	0.878	-
0.9004	104	0.9627	-
0.9091	105	0.7828	-
0.9177	106	0.9415	-
0.9264	107	0.9118	-
0.9351	108	1.0132	-
0.9437	109	0.9399	-
0.9524	110	0.9332	-
0.9610	111	0.9197	-
0.9697	112	0.9294	-
0.9784	113	1.1636	-
0.9870	114	0.9484	-
0.9957	115	1.1682	-
1.0043	116	0.6774	0.6055
1.0065	117	0.5901	-
1.0152	118	0.9436	-
1.0238	119	1.04	-
1.0325	120	0.8038	-
1.0411	121	0.8559	-
1.0498	122	1.1118	-
1.0584	123	0.8408	-
1.0671	124	0.9209	-
1.0758	125	0.7588	-
1.0844	126	0.8141	-
1.0931	127	0.7161	-
1.1017	128	0.7939	-
1.1104	129	1.0256	-
1.1190	130	0.8305	-
1.1277	131	0.8177	-
1.1364	132	0.8632	-
1.1450	133	0.9084	-
1.1537	134	1.0142	-
1.1623	135	1.2425	-
1.1710	136	0.8622	-
1.1797	137	1.0148	-
1.1883	138	1.1104	-
1.1970	139	1.0222	-
1.2056	140	0.9621	-
1.2143	141	0.8216	-
1.2229	142	0.8159	-
1.2316	143	0.7278	-
1.2403	144	0.9867	-
1.2489	145	1.0389	-
1.2576	146	0.8307	-
1.2662	147	0.943	-
1.2749	148	0.8681	-
1.2835	149	0.9374	-
1.2922	150	0.9335	-
1.3009	151	1.0568	-
1.3095	152	0.8154	-
1.3182	153	1.1342	-
1.3268	154	0.9235	-
1.3355	155	0.8155	-
1.3442	156	0.7959	-
1.3528	157	0.9586	-
1.3615	158	0.811	-
1.3701	159	0.8997	-
1.3788	160	0.94	-
1.3874	161	0.7088	-
1.3961	162	0.9682	-
1.4048	163	0.8308	-
1.4134	164	0.7775	-
1.4221	165	0.9181	-
1.4307	166	0.9467	-
1.4394	167	0.9216	-
1.4481	168	0.877	-
1.4567	169	0.9788	-
1.4654	170	0.9094	-
1.4740	171	0.8269	-
1.4827	172	0.6962	-
1.4913	173	0.9412	-
1.5	174	1.1315	-
1.5087	175	0.7554	-
1.5173	176	0.9191	-
1.5260	177	0.8115	-
1.5346	178	0.8798	-
1.5433	179	0.8248	-
1.5519	180	0.712	-
1.5606	181	1.0581	-
1.5693	182	0.9755	-
1.5779	183	0.8808	-
1.5866	184	0.8207	-
1.5952	185	0.8627	-
1.6039	186	0.9566	-
1.6126	187	0.6412	-
1.6212	188	0.7806	-
1.6299	189	0.8012	-
1.6385	190	0.9907	-
1.6472	191	0.8961	-
1.6558	192	0.7042	-
1.6645	193	0.8451	-
1.6732	194	1.0143	-
1.6818	195	0.9189	-
1.6905	196	0.8588	-
1.6991	197	0.9458	-
1.7078	198	0.9716	-
1.7165	199	0.9424	-
1.7251	200	0.9257	-
1.7338	201	0.838	-
1.7424	202	1.0036	-
1.7511	203	0.8486	-
1.7597	204	1.0168	-
1.7684	205	0.8885	-
1.7771	206	0.8234	-
1.7857	207	1.0461	-
1.7944	208	0.9209	-
1.8030	209	0.9177	-
1.8117	210	0.8683	-
1.8203	211	0.799	-
1.8290	212	0.8423	-
1.8377	213	1.0716	-
1.8463	214	1.0086	-
1.8550	215	0.9491	-
1.8636	216	0.876	-
1.8723	217	0.753	-
1.8810	218	1.0414	-
1.8896	219	0.7402	-
1.8983	220	0.7265	-
1.9069	221	0.7436	-
1.9156	222	0.8061	-
1.9242	223	0.6365	-
1.9329	224	0.8791	-
1.9416	225	0.8423	-
1.9502	226	0.7883	-
1.9589	227	0.7574	-
1.9675	228	0.8446	-
1.9762	229	0.8368	-
1.9848	230	0.9048	-
1.9935	231	0.973	-
2.0022	232	0.8729	0.6196
2.0043	233	0.2569	-
2.0130	234	0.8799	-
2.0216	235	0.8955	-
2.0303	236	0.7625	-
2.0390	237	0.6385	-
2.0476	238	0.8365	-
2.0563	239	0.9766	-
2.0649	240	0.685	-
2.0736	241	0.7521	-
2.0823	242	0.6045	-
2.0909	243	0.5866	-
2.0996	244	0.6631	-
2.1082	245	0.8788	-
2.1169	246	0.7642	-
2.1255	247	0.6549	-
2.1342	248	0.748	-
2.1429	249	0.8265	-
2.1515	250	0.7587	-
2.1602	251	1.1161	-
2.1688	252	0.858	-
2.1775	253	0.8394	-
2.1861	254	0.9658	-
2.1948	255	0.9162	-
2.2035	256	0.8561	-
2.2121	257	0.7099	-
2.2208	258	0.7261	-
2.2294	259	0.7174	-
2.2381	260	0.6752	-
2.2468	261	0.9701	-
2.2554	262	0.7685	-
2.2641	263	0.845	-
2.2727	264	0.7365	-
2.2814	265	0.7538	-
2.2900	266	0.7401	-
2.2987	267	0.9054	-
2.3074	268	0.7733	-
2.3160	269	0.9947	-
2.3247	270	0.7427	-
2.3333	271	0.757	-
2.3420	272	0.731	-
2.3506	273	0.719	-
2.3593	274	0.7435	-
2.3680	275	0.7489	-
2.3766	276	0.9108	-
2.3853	277	0.6027	-
2.3939	278	0.8813	-
2.4026	279	0.7801	-
2.4113	280	0.6287	-
2.4199	281	0.7375	-
2.4286	282	0.824	-
2.4372	283	0.8206	-
2.4459	284	0.7805	-
2.4545	285	0.8443	-
2.4632	286	0.8887	-
2.4719	287	0.6984	-
2.4805	288	0.6718	-
2.4892	289	0.8184	-
2.4978	290	1.0259	-
2.5065	291	0.7422	-
2.5152	292	0.7539	-
2.5238	293	0.7422	-
2.5325	294	0.81	-
2.5411	295	0.8015	-
2.5498	296	0.613	-
2.5584	297	0.8623	-
2.5671	298	1.0711	-
2.5758	299	0.76	-
2.5844	300	0.7372	-
2.5931	301	0.7346	-
2.6017	302	0.8559	-
2.6104	303	0.5926	-
2.6190	304	0.6945	-
2.6277	305	0.6985	-
2.6364	306	0.8489	-
2.6450	307	0.8123	-
2.6537	308	0.745	-
2.6623	309	0.6543	-
2.6710	310	0.9022	-
2.6797	311	0.862	-
2.6883	312	0.757	-
2.6970	313	0.8539	-
2.7056	314	0.9205	-
2.7143	315	0.8702	-
2.7229	316	0.8144	-
2.7316	317	0.8202	-
2.7403	318	0.9092	-
2.7489	319	0.7811	-
2.7576	320	0.8792	-
2.7662	321	0.8316	-
2.7749	322	0.8053	-
2.7835	323	0.9033	-
2.7922	324	0.8172	-
2.8009	325	0.7934	-
2.8095	326	0.8106	-
2.8182	327	0.7183	-
2.8268	328	0.7571	-
2.8355	329	0.9051	-
2.8442	330	0.8819	-
2.8528	331	0.96	-
2.8615	332	0.78	-
2.8701	333	0.6932	-
2.8788	334	0.8553	-
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3.0	348	0.8918	0.6255
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6.0022	697	0.5433	0.6325
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11.9978	1394	0.6069	-
12.0065	1395	0.0766	0.6308
12.0087	1396	0.4194	-
12.0173	1397	0.3977	-
12.0260	1398	0.575	-
12.0346	1399	0.3988	-
12.0433	1400	0.4292	-
12.0519	1401	0.5677	-
12.0606	1402	0.5057	-
12.0693	1403	0.447	-
12.0779	1404	0.3676	-
12.0866	1405	0.3618	-
12.0952	1406	0.3369	-
12.1039	1407	0.4366	-
12.1126	1408	0.4554	-
12.1212	1409	0.438	-
12.1299	1410	0.4623	-
12.1385	1411	0.4519	-
12.1472	1412	0.4792	-
12.1558	1413	0.6448	-
12.1645	1414	0.5919	-
12.1732	1415	0.4439	-
12.1818	1416	0.6254	-
12.1905	1417	0.6867	-
12.1991	1418	0.4858	-
12.2078	1419	0.5247	-
12.2165	1420	0.5206	-
12.2251	1421	0.3791	-
12.2338	1422	0.3486	-
12.2424	1423	0.5623	-
12.2511	1424	0.5472	-
12.2597	1425	0.501	-
12.2684	1426	0.3658	-
12.2771	1427	0.427	-
12.2857	1428	0.55	-
12.2944	1429	0.5389	-
12.3030	1430	0.601	-
12.3117	1431	0.4281	-
12.3203	1432	0.5827	-
12.3290	1433	0.4799	-
12.3377	1434	0.4664	-
12.3463	1435	0.4134	-
12.3550	1436	0.5237	-
12.3636	1437	0.4357	-
12.3723	1438	0.5842	-
12.3810	1439	0.4751	-
12.3896	1440	0.4842	-
12.3983	1441	0.4643	-
12.4069	1442	0.4726	-
12.4156	1443	0.3779	-
12.4242	1444	0.5046	-
12.4329	1445	0.4202	-
12.4416	1446	0.4742	-
12.4502	1447	0.5148	-
12.4589	1448	0.6015	-
12.4675	1449	0.3856	-
12.4762	1450	0.4316	-
12.4848	1451	0.4171	-
12.4935	1452	0.5762	-
12.5022	1453	0.6035	-
12.5108	1454	0.5113	-
12.5195	1455	0.5098	-
12.5281	1456	0.468	-
12.5368	1457	0.5314	-
12.5455	1458	0.3761	-
12.5541	1459	0.5039	-
12.5628	1460	0.7329	-
12.5714	1461	0.6484	-
12.5801	1462	0.3605	-
12.5887	1463	0.5505	-
12.5974	1464	0.4275	-
12.6061	1465	0.5235	-
12.6147	1466	0.2997	-
12.6234	1467	0.5186	-
12.6320	1468	0.434	-
12.6407	1469	0.6209	-
12.6494	1470	0.5143	-
12.6580	1471	0.4516	-
12.6667	1472	0.4884	-
12.6753	1473	0.5524	-
12.6840	1474	0.504	-
12.6926	1475	0.521	-
12.7013	1476	0.6132	-
12.7100	1477	0.5367	-
12.7186	1478	0.588	-
12.7273	1479	0.588	-
12.7359	1480	0.4314	-
12.7446	1481	0.5411	-
12.7532	1482	0.5413	-
12.7619	1483	0.5773	-
12.7706	1484	0.6148	-
12.7792	1485	0.5527	-
12.7879	1486	0.591	-
12.7965	1487	0.5036	-
12.8052	1488	0.5067	-
12.8139	1489	0.5065	-
12.8225	1490	0.3709	-
12.8312	1491	0.449	-
12.8398	1492	0.7186	-
12.8485	1493	0.6695	-
12.8571	1494	0.5495	-
12.8658	1495	0.4541	-
12.8745	1496	0.5049	-
12.8831	1497	0.5705	-
12.8918	1498	0.4004	-
12.9004	1499	0.4942	-
12.9091	1500	0.3692	-
12.9177	1501	0.4546	-
12.9264	1502	0.3945	-
12.9351	1503	0.4457	-
12.9437	1504	0.4465	-
12.9524	1505	0.4991	-
12.9610	1506	0.4168	-
12.9697	1507	0.4398	-
12.9784	1508	0.6129	-
12.9870	1509	0.5171	-
12.9957	1510	0.68	-
13.0043	1511	0.257	0.6320
13.0065	1512	0.2875	-
13.0152	1513	0.4426	-
13.0238	1514	0.5968	-
13.0325	1515	0.4575	-
13.0411	1516	0.4208	-
13.0498	1517	0.6111	-
13.0584	1518	0.4491	-
13.0671	1519	0.4238	-
13.0758	1520	0.3598	-
13.0844	1521	0.3611	-
13.0931	1522	0.3166	-
13.1017	1523	0.3495	-
13.1104	1524	0.504	-
13.1190	1525	0.3953	-
13.1277	1526	0.3837	-
13.1364	1527	0.4702	-
13.1450	1528	0.4842	-
13.1537	1529	0.5316	-
13.1623	1530	0.6843	-
13.1710	1531	0.4119	-
13.1797	1532	0.566	-
13.1883	1533	0.6289	-
13.1970	1534	0.5133	-
13.2056	1535	0.4918	-
13.2143	1536	0.4334	-
13.2229	1537	0.448	-
13.2316	1538	0.3901	-
13.2403	1539	0.4498	-
13.2489	1540	0.5765	-
13.2576	1541	0.4494	-
13.2662	1542	0.4161	-
13.2749	1543	0.4489	-
13.2835	1544	0.4961	-
13.2922	1545	0.566	-
13.3009	1546	0.5464	-
13.3095	1547	0.4544	-
13.3182	1548	0.6022	-
13.3268	1549	0.5352	-
13.3355	1550	0.4455	-
13.3442	1551	0.3528	-
13.3528	1552	0.5256	-
13.3615	1553	0.4336	-
13.3701	1554	0.5276	-
13.3788	1555	0.5581	-
13.3874	1556	0.3553	-
13.3961	1557	0.4821	-
13.4048	1558	0.4381	-
13.4134	1559	0.4295	-
13.4221	1560	0.4617	-
13.4307	1561	0.4944	-
13.4394	1562	0.4638	-
13.4481	1563	0.4674	-
13.4567	1564	0.5274	-
13.4654	1565	0.5064	-
13.4740	1566	0.418	-
13.4827	1567	0.3655	-
13.4913	1568	0.5358	-
13.5	1569	0.6803	-
13.5087	1570	0.4324	-
13.5173	1571	0.5466	-
13.5260	1572	0.4046	-
13.5346	1573	0.5154	-
13.5433	1574	0.4974	-
13.5519	1575	0.4288	-
13.5606	1576	0.6139	-
13.5693	1577	0.639	-
13.5779	1578	0.4972	-
13.5866	1579	0.4443	-
13.5952	1580	0.5152	-
13.6039	1581	0.5252	-
13.6126	1582	0.3276	-
13.6212	1583	0.4941	-
13.6299	1584	0.4694	-
13.6385	1585	0.5771	-
13.6472	1586	0.5345	-
13.6558	1587	0.393	-
13.6645	1588	0.4361	-
13.6732	1589	0.6306	-
13.6818	1590	0.4791	-
13.6905	1591	0.4615	-
13.6991	1592	0.5878	-
13.7078	1593	0.6018	-
13.7165	1594	0.6244	-
13.7251	1595	0.4856	-
13.7338	1596	0.5106	-
13.7424	1597	0.6139	-
13.7511	1598	0.5438	-
13.7597	1599	0.6439	-
13.7684	1600	0.5334	-
13.7771	1601	0.4414	-
13.7857	1602	0.6284	-
13.7944	1603	0.5911	-
13.8030	1604	0.5587	-
13.8117	1605	0.443	-
13.8203	1606	0.4535	-
13.8290	1607	0.4474	-
13.8377	1608	0.6536	-
13.8463	1609	0.6585	-
13.8550	1610	0.5697	-
13.8636	1611	0.4965	-
13.8723	1612	0.3808	-
13.8810	1613	0.6171	-
13.8896	1614	0.4141	-
13.8983	1615	0.3974	-
13.9069	1616	0.4146	-
13.9156	1617	0.4761	-
13.9242	1618	0.3364	-
13.9329	1619	0.4532	-
13.9416	1620	0.4791	-
13.9502	1621	0.506	-
13.9589	1622	0.441	-
13.9675	1623	0.5164	-
13.9762	1624	0.4755	-
13.9848	1625	0.5755	-
13.9935	1626	0.5869	-
14.0022	1627	0.3876	0.6315
14.0043	1628	0.1324	-
14.0130	1629	0.4829	-
14.0216	1630	0.5558	-
14.0303	1631	0.458	-
14.0390	1632	0.3926	-
14.0476	1633	0.5138	-
14.0563	1634	0.6099	-
14.0649	1635	0.3593	-
14.0736	1636	0.4445	-
14.0823	1637	0.349	-
14.0909	1638	0.3149	-
14.0996	1639	0.3561	-
14.1082	1640	0.5296	-
14.1169	1641	0.4211	-
14.1255	1642	0.4022	-
14.1342	1643	0.5006	-
14.1429	1644	0.5073	-
14.1515	1645	0.451	-
14.1602	1646	0.6265	-
14.1688	1647	0.5661	-
14.1775	1648	0.5828	-
14.1861	1649	0.6311	-
14.1948	1650	0.6223	-
14.2035	1651	0.4654	-
14.2121	1652	0.4027	-
14.2208	1653	0.4681	-
14.2294	1654	0.405	-
14.2381	1655	0.3793	-
14.2468	1656	0.5966	-
14.2554	1657	0.4515	-
14.2641	1658	0.4629	-
14.2727	1659	0.4519	-
14.2814	1660	0.4831	-
14.2900	1661	0.4968	-
14.2987	1662	0.5746	-
14.3074	1663	0.5295	-
14.3160	1664	0.6171	-
14.3247	1665	0.4421	-
14.3333	1666	0.4878	-
14.3420	1667	0.3967	-
14.3506	1668	0.4288	-
14.3593	1669	0.4503	-
14.3680	1670	0.4961	-
14.3766	1671	0.5604	-
14.3853	1672	0.3602	-
14.3939	1673	0.5758	-
14.4026	1674	0.4466	-
14.4113	1675	0.3795	-
14.4199	1676	0.4125	-
14.4286	1677	0.4842	-
14.4372	1678	0.4994	-
14.4459	1679	0.4349	-
14.4545	1680	0.5829	-
14.4632	1681	0.4991	-
14.4719	1682	0.436	-
14.4805	1683	0.3637	-
14.4892	1684	0.4688	-
14.4978	1685	0.6854	-
14.5065	1686	0.4746	-
14.5152	1687	0.4519	-
14.5238	1688	0.4684	-
14.5325	1689	0.5273	-
14.5411	1690	0.5172	-
14.5498	1691	0.3482	-
14.5584	1692	0.5758	-
14.5671	1693	0.7693	-
14.5758	1694	0.4892	-
14.5844	1695	0.4738	-
14.5931	1696	0.5148	-
14.6017	1697	0.564	-
14.6104	1698	0.3945	-
14.6190	1699	0.4066	-
14.6277	1700	0.4445	-
14.6364	1701	0.569	-
14.6450	1702	0.4889	-
14.6537	1703	0.4877	-
14.6623	1704	0.383	-
14.6710	1705	0.5956	-
14.6797	1706	0.5391	-
14.6883	1707	0.4022	-
14.6970	1708	0.5472	-
14.7056	1709	0.6352	-
14.7143	1710	0.5842	-
14.7229	1711	0.4791	-
14.7316	1712	0.5339	-
14.7403	1713	0.6199	-
14.7489	1714	0.5332	-
14.7576	1715	0.5798	-
14.7662	1716	0.5493	-
14.7749	1717	0.5171	-
14.7835	1718	0.5666	-
14.7922	1719	0.527	-
14.8009	1720	0.5072	-
14.8095	1721	0.4886	-
14.8182	1722	0.4516	-
14.8268	1723	0.4257	-
14.8355	1724	0.6247	-
14.8442	1725	0.6085	0.6313

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.1
• Transformers: 4.45.2
• PyTorch: 2.2.1+cu121
• Accelerate: 1.0.1
• Datasets: 3.0.1
• Tokenizers: 0.20.1

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

ObliQA: Obligation-based Question-Answering Dataset for Regulatory Compliance

@misc{gokhan2024regnlpactionfacilitatingcompliance,
      title={RegNLP in Action: Facilitating Compliance Through Automated Information Retrieval and Answer Generation}, 
      author={Tuba Gokhan and Kexin Wang and Iryna Gurevych and Ted Briscoe},
      year={2024},
      eprint={2409.05677},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.05677}, 
}