Tejasw1/votum-case-law-v1

GTE-base Votum Case Law

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 768-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: Alibaba-NLP/gte-base-en-v1.5
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: en
• License: apache-2.0

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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("Tejasw1/votum-case-law-v1")
# Run inference
sentences = [
    'What role does the liquidator play in verifying the claims and charges of secured creditors during the liquidation of a corporate debtor?',
    "**1. Key Legal Issues and Holdings:**\n\n* **Priority of Charges:** The main legal issue is the priority of charges on the secured assets of the corporate debtor, Reid and Taylor India Ltd.\n* **Insolvency and Bankruptcy Code, 2016:** The court considered the provisions of the Insolvency and Bankruptcy Code, 2016, particularly Section 52 and Regulation 37 of the Insolvency and Bankruptcy Board of India (Liquidation Process) Regulations, 2016.\n* **Security Interest:** The court examined the security interest held by the applicant, Finquest Financial Solutions P. Ltd., and other financial creditors, including Edelweiss Asset Reconstruction Co. Ltd.\n* **Entitlement to Realize Security Interest:** The court held that the applicant is entitled to realize their security interest in the manner specified under Section 52(1)(b) read with Regulation 37 of the IBBI (Liquidation Process) Regulations, 2016.\n\n**2. Significant Facts of the Case:**\n\n* The applicant, Finquest Financial Solutions P. Ltd., is a secured creditor with a first pari passu charge on the immovable fixed assets of the corporate debtor.\n* Edelweiss Asset Reconstruction Co. Ltd. is also a secured creditor with a claim on the same assets.\n* The corporate debtor, Reid and Taylor India Ltd., has been under liquidation.\n* Suit No. 84 of 2013 is pending in the Civil Judge (Senior Division), Nanjangud, challenging the first charge created by IDM.\n* The liquidator has verified the documents and found that the applicant is the sole first charge holder of the immovable property of the corporate debtor at Mysore.\n* The Edelweiss had not obtained an NOC from the IDM and had not ventilated their grievance or enforced their rights before any forum.\n\n**3. Court's Ruling:**\n\n* The court ruled that the applicant, Finquest Financial Solutions P. Ltd., is entitled to realize their security interest in the manner specified under Section 52(1)(b) read with Regulation 37 of the IBBI (Liquidation Process) Regulations, 2016.\n* The court held that the applicant is the first charge holder of the immovable fixed assets of the corporate debtor.\n* The court dismissed the objection of Edelweiss Asset Reconstruction Co. Ltd. regarding the priority of charges.\n* The court directed the liquidator to hand over the symbolic possession of the fixed assets of the corporate debtor to the applicant to enable them to proceed with the sale of the assets.\n* The court directed the liquidator to inform the Tribunal about the manner and progress of the sale of assets from time-to-time for further directions/instructions.\n\n**4. Citations:**\n\n* **Insolvency and Bankruptcy Code, 2016**\n* **Regulation 37 of the Insolvency and Bankruptcy Board of India (Liquidation Process) Regulations, 2016**\n* **Suit No. 84 of 2013 filed with the Court of Civil Judge (Senior Division), Nanjangud, Karnataka**",
    "**1. Key Legal Issues and Holdings:**\n\n* **Dowry and Cruelty:** The case revolves around allegations of dowry demands and cruelty by the husband (petitioner) towards his wife.\n* **Section 498-A IPC:** The main legal issue is the application of Section 498-A of the Indian Penal Code, 1860, which deals with cruelty by the husband or his relatives towards a married woman.\n* **Sentencing:** The court considered the appropriateness of the sentence awarded to the petitioner under Section 498-A IPC.\n\n**2. Significant Facts of the Case:**\n\n* The petitioner, Mangat Ram, was convicted under Section 498-A IPC.\n* He was sentenced to one year imprisonment and a fine.\n* He appealed the conviction and sentence, which was dismissed.\n* He then filed a revision petition, seeking a reduction in sentence.\n* The petitioner had already served over two months in prison.\n* The complainant (wife) had obtained an ex-parte divorce decree.\n\n**3. Court's Ruling:**\n\n* The High Court upheld the conviction of the petitioner under Section 498-A IPC.\n* The court reduced the sentence to the period already undergone by the petitioner.\n* The court enhanced the fine to Rs. 5000/-.\n\n**4. Citations:**\n\n* **Yogendra Yadav v. State of Jharkhand**, Criminal Appeal No. 1205 of 2014\n* **Lajpat Rai v. State of Haryana**, Criminal Revision No. 1380 of 1999\n\n**Refined Summary (Updated):**\n\n**1. Key Legal Issues and Holdings:**\n\n* **Default Bail under Section 167(2) Cr.P.C.:** The court considered the applicability of default bail under Section 167(2) Cr.P.C. in cases where the investigating agency fails to file the final report within the prescribed time limit.\n* **Investigation and Filing of Challan:** The court held that the investigation is not considered incomplete merely because the investigating officer awaits reports of experts or fails to append certain documents to the police report.\n* **Role of the Court:** The court emphasized its role in determining whether to permit the prosecutor to adduce evidence of experts and to balance the interest of the accused with the interest of justice.\n\n**2. Significant Facts of the Case:**\n\n* The petitioners, Sukhwinder Kumar @ Sukha, Harpreet Singh @ Bahadur, Navjit Singh, and Rakesh Kumar @ Kesha, were accused of offenses under the Narcotic Drugs and Psychotropic Substances (NDPS) Act, 1985.\n* They filed revision petitions seeking default bail under Section 167(2) Cr.P.C.\n* The prosecution opposed their claims, arguing that the investigating agency had not failed to file the final report within the prescribed time limit.\n* The court considered the rival contentions and held that the petitioners were entitled to default bail.\n\n**3. Court's Ruling:**\n\n* The court disposed of the revision petitions, releasing the petitioners on interim bail till the filing of the report under Section 173 Cr.P.C.\n* The court emphasized the importance of the investigating agency and the prosecuting agency complying with statutory provisions to avoid delay in completing investigations and filing challans.\n* The court noted that the respondent-State had failed to comply with statutory provisions, resulting in the accused getting benefit of default bail.\n\n**4. Citations:**\n\n* **Abdul Azeez P.V. v. National Investigation Agency**, 2015 (1) RCR (Criminal) 239\n* **Mehal Singh v. State of Haryana**, 1978 PLR 480",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Information Retrieval

• Datasets: dim_768 and dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512
cosine_accuracy@1	0.0824	0.0778
cosine_accuracy@3	0.2484	0.235
cosine_accuracy@5	0.3394	0.3275
cosine_accuracy@10	0.4761	0.4656
cosine_precision@1	0.0824	0.0778
cosine_precision@3	0.0828	0.0783
cosine_precision@5	0.0679	0.0655
cosine_precision@10	0.0476	0.0466
cosine_recall@1	0.0824	0.0778
cosine_recall@3	0.2484	0.235
cosine_recall@5	0.3394	0.3275
cosine_recall@10	0.4761	0.4656
cosine_ndcg@10	0.2582	0.2502
cosine_mrr@10	0.1909	0.1837
cosine_map@100	0.2018	0.1947

Training Details

Training Dataset

json

• Dataset: json
• Size: 132,576 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 2 tokens mean: 26.94 tokens max: 199 tokens	min: 298 tokens mean: 543.71 tokens max: 1266 tokens

• Samples:

  anchor	positive
  What are the legal implications of a court setting aside an order related to the initiation of a Corporate Insolvency Resolution Process due to a pre-existing dispute?	1. Key Legal Issues and Holdings: * Existence of Dispute: The main legal issue is whether there was an existence of dispute prior to the issuance of the Demand Notice dated 11.04.2019. * Section 8 of IBC: The court considered the application of Section 8 of the Insolvency and Bankruptcy Code, 2016, which deals with the requirement of a dispute to be raised by the corporate debtor in response to a demand notice. * Admissibility of Corporate Insolvency Resolution Process (CIRP): The court's ruling affected the admissibility of the CIRP against the corporate debtor. 2. Significant Facts of the Case: * The corporate debtor, Triumph Realty Pvt. Ltd., had a pre-existing dispute with the operational creditor, Tech India Engineers Pvt. Ltd. * The operational creditor issued a demand notice dated 11.04.2019, which was received by the corporate debtor on 16.04.2019. * The corporate debtor raised disputes through e-mails dated 04.10.2018, 01.11.2018, and 04.12.2018, among o...
  How does the court assess whether a dispute is genuine or merely spurious, hypothetical, or illusory?	1. Key Legal Issues and Holdings: * Existence of Dispute: The court considered whether a dispute existed between the parties before the issuance of the Demand Notice under Section 9 of the Insolvency and Bankruptcy Code, 2016. * Pre-existing Dispute: The court relied on the principle laid down by the Hon'ble Supreme Court in "Mobilox Innovations Private Limited v. KIRUSA Software Pvt. Ltd." that a dispute must be pre-existing before the receipt of the Demand Notice. * Section 8 of the Code: The court analyzed the provisions of Section 8 of the Code, which deals with the procedure for an operational creditor to initiate insolvency proceedings against a corporate debtor. * Nature of Dispute: The court held that the dispute was genuine and not spurious, hypothetical, or illusory, and that the corporate debtor had raised a plausible contention that required further investigation. 2. Significant Facts of the Case: * The operational creditor, Nirmal K. Dhiran, supp...
  What are the legal implications of dowry demands and cruelty under Indian law, particularly in the context of Section 498-A IPC?	1. Key Legal Issues and Holdings: * Dowry and Cruelty: The case revolves around allegations of dowry demands and cruelty by the husband (petitioner) towards his wife. * Section 498-A IPC: The main legal issue is the application of Section 498-A of the Indian Penal Code, 1860, which deals with cruelty by the husband or his relatives towards a married woman. * Rent Control and Eviction: The case also involves a dispute over rent control and eviction under the Uttar Pradesh Urban Buildings (Regulation of Letting, Rent and Eviction) Act, 1972. 2. Significant Facts of the Case: * The petitioner, Mangat Ram, was convicted under Section 498-A IPC. * He was sentenced to one year imprisonment and a fine. * He appealed the conviction and sentence, which was dismissed. * He then filed a revision petition, seeking a reduction in sentence. * The petitioner had already served over two months in prison. * The complainant (wife) had obtained an ex-parte divorce decree. **3. Cou...

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• gradient_accumulation_steps: 8
• learning_rate: 2e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: 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: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 8
• 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: 4
• 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: True
• 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
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10
0.0048	10	0.4645	-	-
0.0097	20	0.4746	-	-
0.0145	30	0.4692	-	-
0.0193	40	0.4603	-	-
0.0241	50	0.3954	-	-
0.0290	60	0.4071	-	-
0.0338	70	0.4232	-	-
0.0386	80	0.374	-	-
0.0434	90	0.3748	-	-
0.0483	100	0.3046	-	-
0.0531	110	0.3648	-	-
0.0579	120	0.2515	-	-
0.0628	130	0.3437	-	-
0.0676	140	0.298	-	-
0.0724	150	0.2658	-	-
0.0772	160	0.2989	-	-
0.0821	170	0.2322	-	-
0.0869	180	0.2816	-	-
0.0917	190	0.2436	-	-
0.0965	200	0.2335	-	-
0.1014	210	0.2156	-	-
0.1062	220	0.2305	-	-
0.1110	230	0.228	-	-
0.1159	240	0.2192	-	-
0.1207	250	0.2337	-	-
0.1255	260	0.2594	-	-
0.1303	270	0.1794	-	-
0.1352	280	0.1701	-	-
0.1400	290	0.1981	-	-
0.1448	300	0.2264	-	-
0.1497	310	0.2418	-	-
0.1545	320	0.292	-	-
0.1593	330	0.2112	-	-
0.1641	340	0.1933	-	-
0.1690	350	0.1779	-	-
0.1738	360	0.2294	-	-
0.1786	370	0.2104	-	-
0.1834	380	0.2286	-	-
0.1883	390	0.2752	-	-
0.1931	400	0.1852	-	-
0.1979	410	0.2052	-	-
0.2028	420	0.1893	-	-
0.2076	430	0.2466	-	-
0.2124	440	0.2177	-	-
0.2172	450	0.2506	-	-
0.2221	460	0.1974	-	-
0.2269	470	0.197	-	-
0.2317	480	0.1777	-	-
0.2365	490	0.1848	-	-
0.2414	500	0.1661	-	-
0.2462	510	0.2093	-	-
0.2510	520	0.1178	-	-
0.2559	530	0.2085	-	-
0.2607	540	0.1609	-	-
0.2655	550	0.1736	-	-
0.2703	560	0.1503	-	-
0.2752	570	0.1808	-	-
0.2800	580	0.1614	-	-
0.2848	590	0.2057	-	-
0.2896	600	0.1916	-	-
0.2945	610	0.1569	-	-
0.2993	620	0.184	-	-
0.3041	630	0.2615	-	-
0.3090	640	0.2152	-	-
0.3138	650	0.1426	-	-
0.3186	660	0.145	-	-
0.3234	670	0.1484	-	-
0.3283	680	0.1567	-	-
0.3331	690	0.1365	-	-
0.3379	700	0.1594	-	-
0.3427	710	0.1486	-	-
0.3476	720	0.1663	-	-
0.3524	730	0.2052	-	-
0.3572	740	0.1777	-	-
0.3621	750	0.1728	-	-
0.3669	760	0.1669	-	-
0.3717	770	0.1356	-	-
0.3765	780	0.1706	-	-
0.3814	790	0.1916	-	-
0.3862	800	0.1365	-	-
0.3910	810	0.1392	-	-
0.3958	820	0.1708	-	-
0.4007	830	0.1971	-	-
0.4055	840	0.1363	-	-
0.4103	850	0.1411	-	-
0.4152	860	0.1484	-	-
0.4200	870	0.1767	-	-
0.4248	880	0.1871	-	-
0.4296	890	0.1393	-	-
0.4345	900	0.2113	-	-
0.4393	910	0.1614	-	-
0.4441	920	0.1309	-	-
0.4490	930	0.1329	-	-
0.4538	940	0.2125	-	-
0.4586	950	0.1929	-	-
0.4634	960	0.1777	-	-
0.4683	970	0.1813	-	-
0.4731	980	0.1341	-	-
0.4779	990	0.1025	-	-
0.4827	1000	0.2471	-	-
0.4876	1010	0.1696	-	-
0.4924	1020	0.1144	-	-
0.4972	1030	0.1537	-	-
0.5021	1040	0.1389	-	-
0.5069	1050	0.2184	-	-
0.5117	1060	0.1473	-	-
0.5165	1070	0.1494	-	-
0.5214	1080	0.1568	-	-
0.5262	1090	0.1656	-	-
0.5310	1100	0.1555	-	-
0.5358	1110	0.1108	-	-
0.5407	1120	0.1163	-	-
0.5455	1130	0.1549	-	-
0.5503	1140	0.1638	-	-
0.5552	1150	0.1575	-	-
0.5600	1160	0.1294	-	-
0.5648	1170	0.1402	-	-
0.5696	1180	0.1539	-	-
0.5745	1190	0.1249	-	-
0.5793	1200	0.1042	-	-
0.5841	1210	0.1681	-	-
0.5889	1220	0.1744	-	-
0.5938	1230	0.1144	-	-
0.5986	1240	0.1183	-	-
0.6034	1250	0.1397	-	-
0.6083	1260	0.1938	-	-
0.6131	1270	0.1194	-	-
0.6179	1280	0.1374	-	-
0.6227	1290	0.1203	-	-
0.6276	1300	0.0766	-	-
0.6324	1310	0.1337	-	-
0.6372	1320	0.1695	-	-
0.6420	1330	0.1179	-	-
0.6469	1340	0.1316	-	-
0.6517	1350	0.1294	-	-
0.6565	1360	0.1125	-	-
0.6614	1370	0.1629	-	-
0.6662	1380	0.1094	-	-
0.6710	1390	0.1479	-	-
0.6758	1400	0.1479	-	-
0.6807	1410	0.1608	-	-
0.6855	1420	0.1422	-	-
0.6903	1430	0.1735	-	-
0.6951	1440	0.1403	-	-
0.7000	1450	0.1306	-	-
0.7048	1460	0.1497	-	-
0.7096	1470	0.1154	-	-
0.7145	1480	0.1308	-	-
0.7193	1490	0.1514	-	-
0.7241	1500	0.139	-	-
0.7289	1510	0.1139	-	-
0.7338	1520	0.1313	-	-
0.7386	1530	0.1844	-	-
0.7434	1540	0.1195	-	-
0.7483	1550	0.1102	-	-
0.7531	1560	0.1482	-	-
0.7579	1570	0.1232	-	-
0.7627	1580	0.1408	-	-
0.7676	1590	0.1575	-	-
0.7724	1600	0.1415	-	-
0.7772	1610	0.1344	-	-
0.7820	1620	0.1009	-	-
0.7869	1630	0.1192	-	-
0.7917	1640	0.1528	-	-
0.7965	1650	0.1006	-	-
0.8014	1660	0.0748	-	-
0.8062	1670	0.1278	-	-
0.8110	1680	0.1493	-	-
0.8158	1690	0.1751	-	-
0.8207	1700	0.1357	-	-
0.8255	1710	0.1187	-	-
0.8303	1720	0.1024	-	-
0.8351	1730	0.1238	-	-
0.8400	1740	0.1182	-	-
0.8448	1750	0.0882	-	-
0.8496	1760	0.1575	-	-
0.8545	1770	0.1378	-	-
0.8593	1780	0.1437	-	-
0.8641	1790	0.1121	-	-
0.8689	1800	0.1132	-	-
0.8738	1810	0.136	-	-
0.8786	1820	0.1421	-	-
0.8834	1830	0.1226	-	-
0.8882	1840	0.1345	-	-
0.8931	1850	0.132	-	-
0.8979	1860	0.1698	-	-
0.9027	1870	0.1307	-	-
0.9076	1880	0.0975	-	-
0.9124	1890	0.1166	-	-
0.9172	1900	0.1228	-	-
0.9220	1910	0.1339	-	-
0.9269	1920	0.1015	-	-
0.9317	1930	0.1037	-	-
0.9365	1940	0.1246	-	-
0.9413	1950	0.1302	-	-
0.9462	1960	0.144	-	-
0.9510	1970	0.128	-	-
0.9558	1980	0.1592	-	-
0.9607	1990	0.1218	-	-
0.9655	2000	0.136	-	-
0.9703	2010	0.1093	-	-
0.9751	2020	0.1364	-	-
0.9800	2030	0.1534	-	-
0.9848	2040	0.1066	-	-
0.9896	2050	0.0906	-	-
0.9944	2060	0.1656	-	-
0.9993	2070	0.1304	-	-
0.9998	2071	-	0.2679	0.2559
1.0041	2080	0.0858	-	-
1.0089	2090	0.1428	-	-
1.0138	2100	0.1223	-	-
1.0186	2110	0.1171	-	-
1.0234	2120	0.1148	-	-
1.0282	2130	0.1135	-	-
1.0331	2140	0.1257	-	-
1.0379	2150	0.1401	-	-
1.0427	2160	0.116	-	-
1.0476	2170	0.0878	-	-
1.0524	2180	0.1154	-	-
1.0572	2190	0.0801	-	-
1.0620	2200	0.118	-	-
1.0669	2210	0.127	-	-
1.0717	2220	0.125	-	-
1.0765	2230	0.1178	-	-
1.0813	2240	0.0835	-	-
1.0862	2250	0.0968	-	-
1.0910	2260	0.1122	-	-
1.0958	2270	0.1019	-	-
1.1007	2280	0.1086	-	-
1.1055	2290	0.0991	-	-
1.1103	2300	0.1141	-	-
1.1151	2310	0.1424	-	-
1.1200	2320	0.104	-	-
1.1248	2330	0.1239	-	-
1.1296	2340	0.0829	-	-
1.1344	2350	0.0706	-	-
1.1393	2360	0.0813	-	-
1.1441	2370	0.0796	-	-
1.1489	2380	0.1472	-	-
1.1538	2390	0.1315	-	-
1.1586	2400	0.1264	-	-
1.1634	2410	0.0706	-	-
1.1682	2420	0.0857	-	-
1.1731	2430	0.1078	-	-
1.1779	2440	0.0851	-	-
1.1827	2450	0.1095	-	-
1.1875	2460	0.1406	-	-
1.1924	2470	0.0932	-	-
1.1972	2480	0.1107	-	-
1.2020	2490	0.0941	-	-
1.2069	2500	0.0846	-	-
1.2117	2510	0.0785	-	-
1.2165	2520	0.0877	-	-
1.2213	2530	0.0871	-	-
1.2262	2540	0.0905	-	-
1.2310	2550	0.0769	-	-
1.2358	2560	0.0788	-	-
1.2406	2570	0.066	-	-
1.2455	2580	0.1077	-	-
1.2503	2590	0.0717	-	-
1.2551	2600	0.0902	-	-
1.2600	2610	0.0779	-	-
1.2648	2620	0.0735	-	-
1.2696	2630	0.0475	-	-
1.2744	2640	0.0549	-	-
1.2793	2650	0.0699	-	-
1.2841	2660	0.0804	-	-
1.2889	2670	0.095	-	-
1.2937	2680	0.0787	-	-
1.2986	2690	0.0708	-	-
1.3034	2700	0.1206	-	-
1.3082	2710	0.0582	-	-
1.3131	2720	0.0859	-	-
1.3179	2730	0.0553	-	-
1.3227	2740	0.0433	-	-
1.3275	2750	0.0725	-	-
1.3324	2760	0.0798	-	-
1.3372	2770	0.0683	-	-
1.3420	2780	0.0489	-	-
1.3469	2790	0.0685	-	-
1.3517	2800	0.0951	-	-
1.3565	2810	0.073	-	-
1.3613	2820	0.0687	-	-
1.3662	2830	0.0897	-	-
1.3710	2840	0.0509	-	-
1.3758	2850	0.0554	-	-
1.3806	2860	0.0736	-	-
1.3855	2870	0.0547	-	-
1.3903	2880	0.046	-	-
1.3951	2890	0.0553	-	-
1.4000	2900	0.0888	-	-
1.4048	2910	0.0487	-	-
1.4096	2920	0.0358	-	-
1.4144	2930	0.0434	-	-
1.4193	2940	0.0402	-	-
1.4241	2950	0.0581	-	-
1.4289	2960	0.0761	-	-
1.4337	2970	0.0766	-	-
1.4386	2980	0.0662	-	-
1.4434	2990	0.0434	-	-
1.4482	3000	0.0437	-	-
1.4531	3010	0.0777	-	-
1.4579	3020	0.0766	-	-
1.4627	3030	0.0455	-	-
1.4675	3040	0.0894	-	-
1.4724	3050	0.0532	-	-
1.4772	3060	0.039	-	-
1.4820	3070	0.1039	-	-
1.4868	3080	0.0757	-	-
1.4917	3090	0.0516	-	-
1.4965	3100	0.0661	-	-
1.5013	3110	0.0482	-	-
1.5062	3120	0.0707	-	-
1.5110	3130	0.0529	-	-
1.5158	3140	0.0539	-	-
1.5206	3150	0.0593	-	-
1.5255	3160	0.0825	-	-
1.5303	3170	0.0608	-	-
1.5351	3180	0.0428	-	-
1.5399	3190	0.0426	-	-
1.5448	3200	0.0515	-	-
1.5496	3210	0.0605	-	-
1.5544	3220	0.092	-	-
1.5593	3230	0.0382	-	-
1.5641	3240	0.0543	-	-
1.5689	3250	0.0624	-	-
1.5737	3260	0.0483	-	-
1.5786	3270	0.0454	-	-
1.5834	3280	0.0584	-	-
1.5882	3290	0.0745	-	-
1.5930	3300	0.04	-	-
1.5979	3310	0.0434	-	-
1.6027	3320	0.0483	-	-
1.6075	3330	0.0928	-	-
1.6124	3340	0.0532	-	-
1.6172	3350	0.0498	-	-
1.6220	3360	0.0469	-	-
1.6268	3370	0.0274	-	-
1.6317	3380	0.0379	-	-
1.6365	3390	0.0478	-	-
1.6413	3400	0.0506	-	-
1.6462	3410	0.057	-	-
1.6510	3420	0.0471	-	-
1.6558	3430	0.0541	-	-
1.6606	3440	0.0726	-	-
1.6655	3450	0.0389	-	-
1.6703	3460	0.0679	-	-
1.6751	3470	0.0584	-	-
1.6799	3480	0.0653	-	-
1.6848	3490	0.06	-	-
1.6896	3500	0.0592	-	-
1.6944	3510	0.059	-	-
1.6993	3520	0.0517	-	-
1.7041	3530	0.0495	-	-
1.7089	3540	0.0455	-	-
1.7137	3550	0.0377	-	-
1.7186	3560	0.0539	-	-
1.7234	3570	0.0401	-	-
1.7282	3580	0.0389	-	-
1.7330	3590	0.0482	-	-
1.7379	3600	0.0671	-	-
1.7427	3610	0.057	-	-
1.7475	3620	0.0389	-	-
1.7524	3630	0.0515	-	-
1.7572	3640	0.0356	-	-
1.7620	3650	0.0537	-	-
1.7668	3660	0.0617	-	-
1.7717	3670	0.0465	-	-
1.7765	3680	0.0538	-	-
1.7813	3690	0.0445	-	-
1.7861	3700	0.0417	-	-
1.7910	3710	0.0543	-	-
1.7958	3720	0.0387	-	-
1.8006	3730	0.0319	-	-
1.8055	3740	0.0518	-	-
1.8103	3750	0.0572	-	-
1.8151	3760	0.0815	-	-
1.8199	3770	0.0609	-	-
1.8248	3780	0.0428	-	-
1.8296	3790	0.0271	-	-
1.8344	3800	0.0296	-	-
1.8392	3810	0.047	-	-
1.8441	3820	0.031	-	-
1.8489	3830	0.0596	-	-
1.8537	3840	0.0615	-	-
1.8586	3850	0.0467	-	-
1.8634	3860	0.0516	-	-
1.8682	3870	0.0555	-	-
1.8730	3880	0.0446	-	-
1.8779	3890	0.0872	-	-
1.8827	3900	0.0408	-	-
1.8875	3910	0.0607	-	-
1.8923	3920	0.0415	-	-
1.8972	3930	0.0586	-	-
1.9020	3940	0.0526	-	-
1.9068	3950	0.0447	-	-
1.9117	3960	0.0565	-	-
1.9165	3970	0.0663	-	-
1.9213	3980	0.0476	-	-
1.9261	3990	0.0393	-	-
1.9310	4000	0.0407	-	-
1.9358	4010	0.0403	-	-
1.9406	4020	0.0413	-	-
1.9455	4030	0.0484	-	-
1.9503	4040	0.0581	-	-
1.9551	4050	0.0633	-	-
1.9599	4060	0.0444	-	-
1.9648	4070	0.0529	-	-
1.9696	4080	0.0423	-	-
1.9744	4090	0.0527	-	-
1.9792	4100	0.0719	-	-
1.9841	4110	0.0479	-	-
1.9889	4120	0.0478	-	-
1.9937	4130	0.0708	-	-
1.9986	4140	0.058	-	-
2.0	4143	-	0.2672	0.2575
2.0034	4150	0.0274	-	-
2.0082	4160	0.0384	-	-
2.0130	4170	0.0639	-	-
2.0179	4180	0.0462	-	-
2.0227	4190	0.0438	-	-
2.0275	4200	0.0395	-	-
2.0323	4210	0.0591	-	-
2.0372	4220	0.0519	-	-
2.0420	4230	0.0543	-	-
2.0468	4240	0.0292	-	-
2.0517	4250	0.0449	-	-
2.0565	4260	0.0552	-	-
2.0613	4270	0.0398	-	-
2.0661	4280	0.0647	-	-
2.0710	4290	0.0401	-	-
2.0758	4300	0.0419	-	-
2.0806	4310	0.0369	-	-
2.0854	4320	0.0271	-	-
2.0903	4330	0.074	-	-
2.0951	4340	0.0454	-	-
2.0999	4350	0.0439	-	-
2.1048	4360	0.0509	-	-
2.1096	4370	0.0677	-	-
2.1144	4380	0.0514	-	-
2.1192	4390	0.0437	-	-
2.1241	4400	0.069	-	-
2.1289	4410	0.0288	-	-
2.1337	4420	0.0323	-	-
2.1385	4430	0.0233	-	-
2.1434	4440	0.0322	-	-
2.1482	4450	0.0627	-	-
2.1530	4460	0.0557	-	-
2.1579	4470	0.0649	-	-
2.1627	4480	0.0305	-	-
2.1675	4490	0.0267	-	-
2.1723	4500	0.0325	-	-
2.1772	4510	0.034	-	-
2.1820	4520	0.0461	-	-
2.1868	4530	0.0679	-	-
2.1916	4540	0.033	-	-
2.1965	4550	0.0483	-	-
2.2013	4560	0.0425	-	-
2.2061	4570	0.0336	-	-
2.2110	4580	0.034	-	-
2.2158	4590	0.0382	-	-
2.2206	4600	0.0372	-	-
2.2254	4610	0.0396	-	-
2.2303	4620	0.0299	-	-
2.2351	4630	0.0258	-	-
2.2399	4640	0.0322	-	-
2.2448	4650	0.0392	-	-
2.2496	4660	0.0396	-	-
2.2544	4670	0.0406	-	-
2.2592	4680	0.0285	-	-
2.2641	4690	0.0337	-	-
2.2689	4700	0.0238	-	-
2.2737	4710	0.02	-	-
2.2785	4720	0.0347	-	-
2.2834	4730	0.0238	-	-
2.2882	4740	0.045	-	-
2.2930	4750	0.0297	-	-
2.2979	4760	0.0319	-	-
2.3027	4770	0.0502	-	-
2.3075	4780	0.0362	-	-
2.3123	4790	0.0329	-	-
2.3172	4800	0.0219	-	-
2.3220	4810	0.0176	-	-
2.3268	4820	0.0282	-	-
2.3316	4830	0.0374	-	-
2.3365	4840	0.0429	-	-
2.3413	4850	0.0164	-	-
2.3461	4860	0.0404	-	-
2.3510	4870	0.0287	-	-
2.3558	4880	0.0239	-	-
2.3606	4890	0.0402	-	-
2.3654	4900	0.0341	-	-
2.3703	4910	0.0204	-	-
2.3751	4920	0.0328	-	-
2.3799	4930	0.0388	-	-
2.3847	4940	0.0222	-	-
2.3896	4950	0.0221	-	-
2.3944	4960	0.0318	-	-
2.3992	4970	0.0401	-	-
2.4041	4980	0.0171	-	-
2.4089	4990	0.0195	-	-
2.4137	5000	0.019	-	-
2.4185	5010	0.0163	-	-
2.4234	5020	0.0278	-	-
2.4282	5030	0.0399	-	-
2.4330	5040	0.0412	-	-
2.4378	5050	0.0254	-	-
2.4427	5060	0.0175	-	-
2.4475	5070	0.0251	-	-
2.4523	5080	0.0256	-	-
2.4572	5090	0.0294	-	-
2.4620	5100	0.0278	-	-
2.4668	5110	0.0435	-	-
2.4716	5120	0.0189	-	-
2.4765	5130	0.0195	-	-
2.4813	5140	0.045	-	-
2.4861	5150	0.0614	-	-
2.4909	5160	0.0234	-	-
2.4958	5170	0.0267	-	-
2.5006	5180	0.0294	-	-
2.5054	5190	0.0232	-	-
2.5103	5200	0.026	-	-
2.5151	5210	0.0292	-	-
2.5199	5220	0.0335	-	-
2.5247	5230	0.0311	-	-
2.5296	5240	0.0248	-	-
2.5344	5250	0.0223	-	-
2.5392	5260	0.0188	-	-
2.5441	5270	0.0206	-	-
2.5489	5280	0.0264	-	-
2.5537	5290	0.0479	-	-
2.5585	5300	0.0181	-	-
2.5634	5310	0.0212	-	-
2.5682	5320	0.0207	-	-
2.5730	5330	0.0233	-	-
2.5778	5340	0.0227	-	-
2.5827	5350	0.0239	-	-
2.5875	5360	0.0267	-	-
2.5923	5370	0.0215	-	-
2.5972	5380	0.0164	-	-
2.6020	5390	0.021	-	-
2.6068	5400	0.0392	-	-
2.6116	5410	0.0277	-	-
2.6165	5420	0.0219	-	-
2.6213	5430	0.0221	-	-
2.6261	5440	0.018	-	-
2.6309	5450	0.0159	-	-
2.6358	5460	0.0213	-	-
2.6406	5470	0.0239	-	-
2.6454	5480	0.0289	-	-
2.6503	5490	0.0229	-	-
2.6551	5500	0.0307	-	-
2.6599	5510	0.0416	-	-
2.6647	5520	0.0191	-	-
2.6696	5530	0.0335	-	-
2.6744	5540	0.0402	-	-
2.6792	5550	0.0294	-	-
2.6840	5560	0.0222	-	-
2.6889	5570	0.0296	-	-
2.6937	5580	0.0347	-	-
2.6985	5590	0.0217	-	-
2.7034	5600	0.0163	-	-
2.7082	5610	0.0209	-	-
2.7130	5620	0.0195	-	-
2.7178	5630	0.0273	-	-
2.7227	5640	0.0169	-	-
2.7275	5650	0.0191	-	-
2.7323	5660	0.0166	-	-
2.7371	5670	0.0265	-	-
2.7420	5680	0.0313	-	-
2.7468	5690	0.0215	-	-
2.7516	5700	0.0228	-	-
2.7565	5710	0.0208	-	-
2.7613	5720	0.0206	-	-
2.7661	5730	0.0208	-	-
2.7709	5740	0.0317	-	-
2.7758	5750	0.0283	-	-
2.7806	5760	0.0206	-	-
2.7854	5770	0.0145	-	-
2.7902	5780	0.0238	-	-
2.7951	5790	0.0228	-	-
2.7999	5800	0.0133	-	-
2.8047	5810	0.0194	-	-
2.8096	5820	0.0398	-	-
2.8144	5830	0.025	-	-
2.8192	5840	0.0309	-	-
2.8240	5850	0.0355	-	-
2.8289	5860	0.0123	-	-
2.8337	5870	0.0182	-	-
2.8385	5880	0.023	-	-
2.8434	5890	0.0191	-	-
2.8482	5900	0.023	-	-
2.8530	5910	0.0356	-	-
2.8578	5920	0.0239	-	-
2.8627	5930	0.0203	-	-
2.8675	5940	0.0154	-	-
2.8723	5950	0.025	-	-
2.8771	5960	0.0491	-	-
2.8820	5970	0.0205	-	-
2.8868	5980	0.03	-	-
2.8916	5990	0.0249	-	-
2.8965	6000	0.0355	-	-
2.9013	6010	0.0277	-	-
2.9061	6020	0.0231	-	-
2.9109	6030	0.0202	-	-
2.9158	6040	0.0294	-	-
2.9206	6050	0.0181	-	-
2.9254	6060	0.0179	-	-
2.9302	6070	0.0275	-	-
2.9351	6080	0.0211	-	-
2.9399	6090	0.0191	-	-
2.9447	6100	0.0233	-	-
2.9496	6110	0.0302	-	-
2.9544	6120	0.0344	-	-
2.9592	6130	0.0391	-	-
2.9640	6140	0.0242	-	-
2.9689	6150	0.0212	-	-
2.9737	6160	0.0404	-	-
2.9785	6170	0.0428	-	-
2.9833	6180	0.0206	-	-
2.9882	6190	0.0265	-	-
2.9930	6200	0.0378	-	-
2.9978	6210	0.0255	-	-
2.9998	6214	-	0.2628	0.2557
3.0027	6220	0.024	-	-
3.0075	6230	0.0198	-	-
3.0123	6240	0.0234	-	-
3.0171	6250	0.0424	-	-
3.0220	6260	0.0297	-	-
3.0268	6270	0.0209	-	-
3.0316	6280	0.0344	-	-
3.0364	6290	0.0273	-	-
3.0413	6300	0.0247	-	-
3.0461	6310	0.0206	-	-
3.0509	6320	0.0231	-	-
3.0558	6330	0.0265	-	-
3.0606	6340	0.0198	-	-
3.0654	6350	0.0389	-	-
3.0702	6360	0.0171	-	-
3.0751	6370	0.0235	-	-
3.0799	6380	0.0228	-	-
3.0847	6390	0.0184	-	-
3.0895	6400	0.0459	-	-
3.0944	6410	0.0222	-	-
3.0992	6420	0.0186	-	-
3.1040	6430	0.0246	-	-
3.1089	6440	0.0446	-	-
3.1137	6450	0.0333	-	-
3.1185	6460	0.0205	-	-
3.1233	6470	0.0228	-	-
3.1282	6480	0.0287	-	-
3.1330	6490	0.0205	-	-
3.1378	6500	0.0143	-	-
3.1427	6510	0.0159	-	-
3.1475	6520	0.0367	-	-
3.1523	6530	0.0327	-	-
3.1571	6540	0.0355	-	-
3.1620	6550	0.0202	-	-
3.1668	6560	0.0133	-	-
3.1716	6570	0.0143	-	-
3.1764	6580	0.0171	-	-
3.1813	6590	0.0208	-	-
3.1861	6600	0.0368	-	-
3.1909	6610	0.0238	-	-
3.1958	6620	0.0276	-	-
3.2006	6630	0.0269	-	-
3.2054	6640	0.0152	-	-
3.2102	6650	0.0229	-	-
3.2151	6660	0.0189	-	-
3.2199	6670	0.0206	-	-
3.2247	6680	0.0206	-	-
3.2295	6690	0.0164	-	-
3.2344	6700	0.0121	-	-
3.2392	6710	0.0224	-	-
3.2440	6720	0.0193	-	-
3.2489	6730	0.0213	-	-
3.2537	6740	0.0216	-	-
3.2585	6750	0.0155	-	-
3.2633	6760	0.0185	-	-
3.2682	6770	0.018	-	-
3.2730	6780	0.0107	-	-
3.2778	6790	0.0218	-	-
3.2826	6800	0.0161	-	-
3.2875	6810	0.0256	-	-
3.2923	6820	0.015	-	-
3.2971	6830	0.0132	-	-
3.3020	6840	0.0228	-	-
3.3068	6850	0.0274	-	-
3.3116	6860	0.0232	-	-
3.3164	6870	0.0122	-	-
3.3213	6880	0.0101	-	-
3.3261	6890	0.0138	-	-
3.3309	6900	0.0223	-	-
3.3357	6910	0.018	-	-
3.3406	6920	0.0105	-	-
3.3454	6930	0.0212	-	-
3.3502	6940	0.0189	-	-
3.3551	6950	0.0115	-	-
3.3599	6960	0.0187	-	-
3.3647	6970	0.0237	-	-
3.3695	6980	0.0172	-	-
3.3744	6990	0.0148	-	-
3.3792	7000	0.0234	-	-
3.3840	7010	0.0139	-	-
3.3888	7020	0.012	-	-
3.3937	7030	0.0181	-	-
3.3985	7040	0.0247	-	-
3.4033	7050	0.0114	-	-
3.4082	7060	0.0107	-	-
3.4130	7070	0.0133	-	-
3.4178	7080	0.0092	-	-
3.4226	7090	0.0168	-	-
3.4275	7100	0.0225	-	-
3.4323	7110	0.0127	-	-
3.4371	7120	0.0231	-	-
3.4420	7130	0.0104	-	-
3.4468	7140	0.0114	-	-
3.4516	7150	0.0084	-	-
3.4564	7160	0.0261	-	-
3.4613	7170	0.0201	-	-
3.4661	7180	0.0251	-	-
3.4709	7190	0.0135	-	-
3.4757	7200	0.0126	-	-
3.4806	7210	0.0257	-	-
3.4854	7220	0.0369	-	-
3.4902	7230	0.0137	-	-
3.4951	7240	0.016	-	-
3.4999	7250	0.0187	-	-
3.5047	7260	0.0156	-	-
3.5095	7270	0.0141	-	-
3.5144	7280	0.0258	-	-
3.5192	7290	0.0283	-	-
3.5240	7300	0.02	-	-
3.5288	7310	0.0283	-	-
3.5337	7320	0.0142	-	-
3.5385	7330	0.0107	-	-
3.5433	7340	0.0144	-	-
3.5482	7350	0.0146	-	-
3.5530	7360	0.0321	-	-
3.5578	7370	0.0101	-	-
3.5626	7380	0.0145	-	-
3.5675	7390	0.0132	-	-
3.5723	7400	0.0159	-	-
3.5771	7410	0.0167	-	-
3.5819	7420	0.0116	-	-
3.5868	7430	0.0175	-	-
3.5916	7440	0.0156	-	-
3.5964	7450	0.0096	-	-
3.6013	7460	0.0156	-	-
3.6061	7470	0.0251	-	-
3.6109	7480	0.0163	-	-
3.6157	7490	0.0118	-	-
3.6206	7500	0.0161	-	-
3.6254	7510	0.0131	-	-
3.6302	7520	0.0091	-	-
3.6350	7530	0.0136	-	-
3.6399	7540	0.0175	-	-
3.6447	7550	0.0213	-	-
3.6495	7560	0.0168	-	-
3.6544	7570	0.02	-	-
3.6592	7580	0.0204	-	-
3.6640	7590	0.0132	-	-
3.6688	7600	0.0254	-	-
3.6737	7610	0.0313	-	-
3.6785	7620	0.0107	-	-
3.6833	7630	0.0241	-	-
3.6881	7640	0.0188	-	-
3.6930	7650	0.0166	-	-
3.6978	7660	0.021	-	-
3.7026	7670	0.0126	-	-
3.7075	7680	0.0148	-	-
3.7123	7690	0.0155	-	-
3.7171	7700	0.0117	-	-
3.7219	7710	0.0124	-	-
3.7268	7720	0.0121	-	-
3.7316	7730	0.0118	-	-
3.7364	7740	0.0182	-	-
3.7413	7750	0.0168	-	-
3.7461	7760	0.0146	-	-
3.7509	7770	0.0199	-	-
3.7557	7780	0.0109	-	-
3.7606	7790	0.0192	-	-
3.7654	7800	0.014	-	-
3.7702	7810	0.0261	-	-
3.7750	7820	0.0176	-	-
3.7799	7830	0.0156	-	-
3.7847	7840	0.0112	-	-
3.7895	7850	0.0136	-	-
3.7944	7860	0.0174	-	-
3.7992	7870	0.0082	-	-
3.8040	7880	0.0111	-	-
3.8088	7890	0.0279	-	-
3.8137	7900	0.0206	-	-
3.8185	7910	0.0174	-	-
3.8233	7920	0.0263	-	-
3.8281	7930	0.0091	-	-
3.8330	7940	0.0127	-	-
3.8378	7950	0.0138	-	-
3.8426	7960	0.0168	-	-
3.8475	7970	0.0141	-	-
3.8523	7980	0.0317	-	-
3.8571	7990	0.0167	-	-
3.8619	8000	0.0151	-	-
3.8668	8010	0.0122	-	-
3.8716	8020	0.0167	-	-
3.8764	8030	0.0382	-	-
3.8812	8040	0.0128	-	-
3.8861	8050	0.0232	-	-
3.8909	8060	0.0222	-	-
3.8957	8070	0.0194	-	-
3.9006	8080	0.0191	-	-
3.9054	8090	0.0136	-	-
3.9102	8100	0.0106	-	-
3.9150	8110	0.0216	-	-
3.9199	8120	0.0178	-	-
3.9247	8130	0.0126	-	-
3.9295	8140	0.0158	-	-
3.9343	8150	0.0186	-	-
3.9392	8160	0.0167	-	-
3.9440	8170	0.0159	-	-
3.9488	8180	0.0174	-	-
3.9537	8190	0.0211	-	-
3.9585	8200	0.0245	-	-
3.9633	8210	0.0186	-	-
3.9681	8220	0.0162	-	-
3.9730	8230	0.0312	-	-
3.9778	8240	0.033	-	-
3.9826	8250	0.0147	-	-
3.9874	8260	0.0224	-	-
3.9923	8270	0.0215	-	-
3.9971	8280	0.0275	-	-
3.9990	8284	-	0.2582	0.2502

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.5
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.20.3

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}