diff --git "a/checkpoint-1145/README.md" "b/checkpoint-1145/README.md" new file mode 100644--- /dev/null +++ "b/checkpoint-1145/README.md" @@ -0,0 +1,1931 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:111000 +- loss:Matryoshka2dLoss +- loss:MatryoshkaLoss +- loss:GISTEmbedLoss +base_model: BAAI/bge-m3 +widget: +- source_sentence: An 80-year-old woman found murdered at an allotment in north-west + London has been named as Lea Adri-Soejoko. + sentences: + - 'Her body was found in a lock-up store on the allotments on Sheaveshill Avenue + in Colindale. + + Ms Adri-Soejoko, who was secretary of the Colindale Allotment Association, was + reported missing at 01:40 GMT on Tuesday and found 20 minutes later. + + Police have not released the cause of death but said she lost her life "in the + most tragic way possible". + + Det Ch Insp Noel McHugh of the Met Police said: "At this stage we are keeping + an open mind about any possible motive and would ask any witnesses or anyone with + information to come forward. + + "An elderly woman lost her life in the most tragic way possible and was left inside + a storage unit similar to a garage." + + According to police, Ms Adri-Soejoko was last seen at 18:30 on Sunday and spoke + to a friend on the phone on Monday afternoon. + + Det Ch Insp McHugh said a friend "became concerned and alerted family members" + after the 80 year old did not turn up to a meeting in the Barnet area. + + Cindy Matthews, who gardened in the allotment, described Ms Adri-Soejoko as "a + lovely lady". + + Another neighbour, Danila Catruta said the murder had left her "scared" and "very + surprised". + + Extra police patrols have been added in the area, Scotland Yard said.' + - This genus is currently classified in the family of lizards , known as Iguanidae + , subfamily Polychrotinae , and is no longer classified in the now invalid family + , polychrotidae . + - over 188,000 cases have been reported worldwide ; more than 7,400 people have + died ; and over 80,000 have recovered . +- source_sentence: A land rover is being driven across a river. + sentences: + - As of 24 March , more than 396,000 cases of COVID-19 have been reported in more + than 190 countries and territories , resulting in more than 17,300 deaths and + more than 103,000 recoveries . + - In July 2015 , it was announced that Johns and Ben Affleck will co-write the screenplay + for a standalone Batman film , starring Affleck , set in the DC Comics shared + film universe. + - A vehicle is crossing a river. +- source_sentence: More than 531,500 cases of COVID-19 have been reported in over + 190 countries and territories after March 26 . + sentences: + - As of 27 March , more than 532,000 cases of COVID-19 have been reported in over + 190 countries and territories , resulting in approximately 24,000 deaths and more + than 123,000 recoveries . + - 'The stake in the French luxury group will give Peta the right to attend shareholder + meetings and question the board in front of other shareholders. + + Peta has not said how many shares it has bought. + + Such a move is common among pressure groups. + + The move by Peta - People for the Ethical Treatment of Animals - follows its investigation + into the way that crocodile skin, which is often used for luxury goods, is harvested. + + Peta said: "In the wake of an exposé revealing that reptiles on crocodile farms + in Vietnam - including two that have supplied skins to a tannery owned by Louis + Vuitton''s parent company, LVMH - are confined to tiny pits and sometimes hacked + into while they''re still alive and thrashing, Peta has become a shareholder of + LVMH on the Euronext Paris to put pressure on the company to stop selling exotic + skins merchandise." + + Commenting when Peta''s video was released last month, LVMH''s director of environment, + Sylvie Bernard, said that its tannery, Heng Long, had not bought crocodile skins + from any Vietnamese farms since 2014. + + "We have no knowledge of a partner that would practice the method you referred + to ... any cruel method involving the suffering of the animal is in clear contradiction + with our principles and rules," she said. + + LVMH is the world''s biggest luxury group. Its brands include Louis Vuitton handbags, + several Champagne brands, Hennessy cognac and fashion labels Fendi and Marc Jacobs. + + The company also sells perfumes, cosmetics, watches and jewellery.' + - A toilet and trashcan in a white bathroom. +- source_sentence: Ray Bloch was the announcer , and John Reed King led the orchestra + . + sentences: + - over 227,000 cases have been reported worldwide ; more than 9,200 people have + died ; and over 85,000 have recovered . + - 'Walk the Line (soundtrack) Walk the Line: Original Motion Picture Soundtrack + is the soundtrack album to the 2005 biographical drama film of the same name released + November 15, 2005 by Wind-Up Records. There are nine songs performed by Joaquin + Phoenix (as Johnny Cash), four songs by Reese Witherspoon (as June Carter Cash), + one song by Waylon Payne (as Jerry Lee Lewis), one song by Johnathan Rice (as + Roy Orbison), two songs by Tyler Hilton (as Elvis Presley), and one song by Shooter + Jennings (as Waylon Jennings). At the Golden Globe Awards Joaquin Phoenix was + awarded the Best Actor - Musical or Comedy and Reese Witherspoon was awarded the + Best Actress - Musical or Comedy, as well as the film won the Best Picture - Musical + or Comedy. Joaquin Phoenix and Reese Witherspoon were also nominated for the Academy + Award for Best Actor and Best Actress, which Witherspoon won.' + - The announcer was Ray Ray Bloch , John Reed King led the orchestra . +- source_sentence: Three men have been arrested after two journalists were killed + during a live radio broadcast in the Dominican Republic. + sentences: + - 'One of the journalists was filming his show on Facebook Live when he was shot. + + The footage showed his broadcast interrupted by gunfire and a panicked woman shouting + "Shots! Shots! Shots!". + + Police said the shooting happened on Tuesday in San Pedro de Macoris, east of + the capital Santo Domingo. + + The two journalists killed were presenter Luis Manuel Medina and radio producer + Leo Martinez. + + A woman was also injured and is undergoing surgery, said local authorities. + + The arrested men have not been charged and police say they do not know the motive. + + "The investigations have begun and we will try, with all the means in our powers, + to reach the truth," Attorney General Jean Rodriguez told the local press. + + In August 2015, two US journalists were shot dead during a live TV report in Virginia.' + - The Cover of Rolling Stone "The Cover of 'Rolling Stone'" is a song written by + Shel Silverstein and first recorded by American rock group Dr. Hook & the Medicine + Show. Produced by Ron Haffkine and released in 1972, it was the band's third single + and peaked at number six on the U.S. pop chart for two weeks on March 17–24, + 1973. + - I'll Be There (The Jackson 5 song) The song was recorded by The Jackson 5 and + released by Motown Records on August 28, 1970 as the first single from their Third + Album on the same date. Produced by the songwriters, "I'll Be There" was The Jackson + 5's fourth number one hit in a row (after "I Want You Back" in 1969, "ABC" and + "The Love You Save" earlier in 1970), making them the first black male group to + achieve four consecutive number one pop hits. "I'll Be There" is also notable + as the most successful single released by Motown during its "Detroit era" (1959–72). +pipeline_tag: sentence-similarity +library_name: sentence-transformers +metrics: +- pearson_cosine +- spearman_cosine +- cosine_accuracy +- cosine_accuracy_threshold +- cosine_f1 +- cosine_f1_threshold +- cosine_precision +- cosine_recall +- cosine_ap +- cosine_mcc +model-index: +- name: SentenceTransformer based on BAAI/bge-m3 + results: + - task: + type: semantic-similarity + name: Semantic Similarity + dataset: + name: sts test + type: sts-test + metrics: + - type: pearson_cosine + value: 0.9084511864603124 + name: Pearson Cosine + - type: spearman_cosine + value: 0.9222536610997011 + name: Spearman Cosine + - task: + type: semantic-similarity + name: Semantic Similarity + dataset: + name: sts test 1024 + type: sts-test-1024 + metrics: + - type: pearson_cosine + value: 0.8797952712975998 + name: Pearson Cosine + - type: spearman_cosine + value: 0.9135158587840699 + name: Spearman Cosine + - task: + type: binary-classification + name: Binary Classification + dataset: + name: Qnli dev + type: Qnli-dev + metrics: + - type: cosine_accuracy + value: 0.71875 + name: Cosine Accuracy + - type: cosine_accuracy_threshold + value: 0.7880457639694214 + name: Cosine Accuracy Threshold + - type: cosine_f1 + value: 0.7238095238095237 + name: Cosine F1 + - type: cosine_f1_threshold + value: 0.703315019607544 + name: Cosine F1 Threshold + - type: cosine_precision + value: 0.6333333333333333 + name: Cosine Precision + - type: cosine_recall + value: 0.8444444444444444 + name: Cosine Recall + - type: cosine_ap + value: 0.7340095878922616 + name: Cosine Ap + - type: cosine_mcc + value: 0.42578476395267345 + name: Cosine Mcc + - task: + type: binary-classification + name: Binary Classification + dataset: + name: Qnli dev 1024 + type: Qnli-dev-1024 + metrics: + - type: cosine_accuracy + value: 0.7083333333333334 + name: Cosine Accuracy + - type: cosine_accuracy_threshold + value: 0.8668882846832275 + name: Cosine Accuracy Threshold + - type: cosine_f1 + value: 0.7090909090909091 + name: Cosine F1 + - type: cosine_f1_threshold + value: 0.7944933772087097 + name: Cosine F1 Threshold + - type: cosine_precision + value: 0.6 + name: Cosine Precision + - type: cosine_recall + value: 0.8666666666666667 + name: Cosine Recall + - type: cosine_ap + value: 0.6999822477767415 + name: Cosine Ap + - type: cosine_mcc + value: 0.3808509397785054 + name: Cosine Mcc + - task: + type: triplet + name: Triplet + dataset: + name: allNLI triplets + type: allNLI-triplets + metrics: + - type: cosine_accuracy + value: 0.9791666865348816 + name: Cosine Accuracy + - task: + type: triplet + name: Triplet + dataset: + name: allNLI triplets 1024 + type: allNLI--triplets-1024 + metrics: + - type: cosine_accuracy + value: 0.9375 + name: Cosine Accuracy +--- + +# SentenceTransformer based on BAAI/bge-m3 + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the global_dataset dataset. It maps sentences & paragraphs to a 2048-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-m3](https://huggingface.co/BAAI/bge-m3) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 2048 dimensions +- **Similarity Function:** Cosine Similarity +- **Training Dataset:** + - global_dataset + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel + (1): AdvancedWeightedPooling( + (mha): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) + ) + (MLP): Sequential( + (0): SwiGLUBlock( + (in_proj_w): Linear(in_features=1024, out_features=2048, bias=True) + (in_proj_v): Linear(in_features=1024, out_features=2048, bias=True) + (dropout): Dropout(p=0.05, inplace=False) + ) + (1): SwiGLUBlock( + (in_proj_w): Linear(in_features=2048, out_features=1024, bias=True) + (in_proj_v): Linear(in_features=2048, out_features=1024, bias=True) + (dropout): Dropout(p=0.025, inplace=False) + ) + (2): Linear(in_features=1024, out_features=1024, bias=True) + ) + (layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True) + ) +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("bobox/XLMRoBERTaM3-CustomPoolin-v1.06-1024conc1024-MLP-s1-adaptivelayersReg-checkpoints-tmp") +# Run inference +sentences = [ + 'Three men have been arrested after two journalists were killed during a live radio broadcast in the Dominican Republic.', + 'One of the journalists was filming his show on Facebook Live when he was shot.\nThe footage showed his broadcast interrupted by gunfire and a panicked woman shouting "Shots! Shots! Shots!".\nPolice said the shooting happened on Tuesday in San Pedro de Macoris, east of the capital Santo Domingo.\nThe two journalists killed were presenter Luis Manuel Medina and radio producer Leo Martinez.\nA woman was also injured and is undergoing surgery, said local authorities.\nThe arrested men have not been charged and police say they do not know the motive.\n"The investigations have begun and we will try, with all the means in our powers, to reach the truth," Attorney General Jean Rodriguez told the local press.\nIn August 2015, two US journalists were shot dead during a live TV report in Virginia.', + 'I\'ll Be There (The Jackson 5 song) The song was recorded by The Jackson 5 and released by Motown Records on August 28, 1970 as the first single from their Third Album on the same date. Produced by the songwriters, "I\'ll Be There" was The Jackson 5\'s fourth number one hit in a row (after "I Want You Back" in 1969, "ABC" and "The Love You Save" earlier in 1970), making them the first black male group to achieve four consecutive number one pop hits. "I\'ll Be There" is also notable as the most successful single released by Motown during its "Detroit era" (1959–72).', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 2048] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Semantic Similarity + +* Dataset: `sts-test` +* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| pearson_cosine | 0.9085 | +| **spearman_cosine** | **0.9223** | + +#### Semantic Similarity + +* Dataset: `sts-test-1024` +* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters: + ```json + { + "truncate_dim": 1024 + } + ``` + +| Metric | Value | +|:--------------------|:-----------| +| pearson_cosine | 0.8798 | +| **spearman_cosine** | **0.9135** | + +#### Binary Classification + +* Dataset: `Qnli-dev` +* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) + +| Metric | Value | +|:--------------------------|:----------| +| cosine_accuracy | 0.7188 | +| cosine_accuracy_threshold | 0.788 | +| cosine_f1 | 0.7238 | +| cosine_f1_threshold | 0.7033 | +| cosine_precision | 0.6333 | +| cosine_recall | 0.8444 | +| **cosine_ap** | **0.734** | +| cosine_mcc | 0.4258 | + +#### Binary Classification + +* Dataset: `Qnli-dev-1024` +* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) with these parameters: + ```json + { + "truncate_dim": 1024 + } + ``` + +| Metric | Value | +|:--------------------------|:--------| +| cosine_accuracy | 0.7083 | +| cosine_accuracy_threshold | 0.8669 | +| cosine_f1 | 0.7091 | +| cosine_f1_threshold | 0.7945 | +| cosine_precision | 0.6 | +| cosine_recall | 0.8667 | +| **cosine_ap** | **0.7** | +| cosine_mcc | 0.3809 | + +#### Triplet + +* Dataset: `allNLI-triplets` +* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| **cosine_accuracy** | **0.9792** | + +#### Triplet + +* Dataset: `allNLI--triplets-1024` +* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) with these parameters: + ```json + { + "truncate_dim": 1024 + } + ``` + +| Metric | Value | +|:--------------------|:-----------| +| **cosine_accuracy** | **0.9375** | + + + + + +## Training Details + +### Training Dataset + +#### global_dataset + +* Dataset: global_dataset +* Size: 111,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | well um i seem to always be in the the next to the last generation of word processing software when everyone was in Samna i was still in PFM and now that i finally become proficient in Samna everybody's going to WordPerfect so i can never quite stay current with that | I really struggle to keep up with the latest software that everyone uses. | + | when will the man who killed don quixote be released | The Man Who Killed Don Quixote The film premiered on 19 May 2018, simultaneously acting as the closing film at the 2018 Cannes Film Festival and being released in French theaters. | + | Lenovo is a Chinese company . | Lenovo 's corporate culture differs from other Chinese companies . | +* Loss: [Matryoshka2dLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": 2, + "last_layer_weight": 1.0, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.75, + "kl_temperature": 0.75, + "matryoshka_dims": [ + 2048, + 1024 + ], + "matryoshka_weights": [ + 1, + 0.25 + ], + "n_dims_per_step": -1 + } + ``` + +### Evaluation Dataset + +#### global_dataset + +* Dataset: global_dataset +* Size: 972 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 972 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | What was the name of the imperialistic policy in China? | The Age of Imperialism, a time period beginning around 1700, saw (generally European) industrializing nations engaging in the process of colonizing, influencing, and annexing other parts of the world in order to gain political power.[citation needed] | + | where does the monarch butterfly go in the winter | Monarch butterfly migration Starting in September and October, eastern and northeastern populations migrate from southern Canada and the United States to overwintering sites in central Mexico where they arrive around November. They start the return trip in March, arriving around July. No individual butterfly completes the entire round trip; female monarchs lay eggs for the next generation during the northward migration[2] and at least four generations are involved in the annual cycle. | + | In 2012 , Ned Evett released `` Treehouse '' , his sixth solo record , produced in Nashville Tennessee by musician Adrian Belew . | In 2012 , Ned Evett `` Treehouse '' released his sixth solo record , produced in Nashville Tennessee by musician Adrian Belew . | +* Loss: [Matryoshka2dLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshka2dloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": 2, + "last_layer_weight": 1.0, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.75, + "kl_temperature": 0.75, + "matryoshka_dims": [ + 2048, + 1024 + ], + "matryoshka_weights": [ + 1, + 0.25 + ], + "n_dims_per_step": -1 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 32 +- `per_device_eval_batch_size`: 48 +- `gradient_accumulation_steps`: 3 +- `learning_rate`: 0.0001 +- `weight_decay`: 0.001 +- `lr_scheduler_type`: cosine_with_restarts +- `lr_scheduler_kwargs`: {'num_cycles': 3} +- `warmup_ratio`: 0.15 +- `save_safetensors`: False +- `fp16`: True +- `remove_unused_columns`: False +- `push_to_hub`: True +- `hub_model_id`: bobox/XLMRoBERTaM3-CustomPoolin-v1.06-1024conc1024-MLP-s1-adaptivelayersReg-checkpoints-tmp +- `hub_strategy`: all_checkpoints +- `hub_private_repo`: False +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 32 +- `per_device_eval_batch_size`: 48 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 3 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0001 +- `weight_decay`: 0.001 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 3 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine_with_restarts +- `lr_scheduler_kwargs`: {'num_cycles': 3} +- `warmup_ratio`: 0.15 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `save_safetensors`: False +- `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`: False +- `fp16`: True +- `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`: False +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `tp_size`: 0 +- `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`: True +- `resume_from_checkpoint`: None +- `hub_model_id`: bobox/XLMRoBERTaM3-CustomPoolin-v1.06-1024conc1024-MLP-s1-adaptivelayersReg-checkpoints-tmp +- `hub_strategy`: all_checkpoints +- `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 +- `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 | global dataset loss | sts-test_spearman_cosine | sts-test-1024_spearman_cosine | Qnli-dev_cosine_ap | Qnli-dev-1024_cosine_ap | allNLI-triplets_cosine_accuracy | allNLI--triplets-1024_cosine_accuracy | +|:------:|:----:|:-------------:|:-------------------:|:------------------------:|:-----------------------------:|:------------------:|:-----------------------:|:-------------------------------:|:-------------------------------------:| +| 0.0009 | 1 | 8.1889 | - | - | - | - | - | - | - | +| 0.0017 | 2 | 9.3962 | - | - | - | - | - | - | - | +| 0.0026 | 3 | 9.4132 | - | - | - | - | - | - | - | +| 0.0035 | 4 | 9.1807 | - | - | - | - | - | - | - | +| 0.0043 | 5 | 8.1559 | - | - | - | - | - | - | - | +| 0.0052 | 6 | 9.9007 | - | - | - | - | - | - | - | +| 0.0061 | 7 | 9.634 | - | - | - | - | - | - | - | +| 0.0069 | 8 | 10.8722 | - | - | - | - | - | - | - | +| 0.0078 | 9 | 8.6969 | - | - | - | - | - | - | - | +| 0.0086 | 10 | 8.9552 | - | - | - | - | - | - | - | +| 0.0095 | 11 | 7.5245 | - | - | - | - | - | - | - | +| 0.0104 | 12 | 9.1573 | - | - | - | - | - | - | - | +| 0.0112 | 13 | 9.2615 | - | - | - | - | - | - | - | +| 0.0121 | 14 | 9.1465 | - | - | - | - | - | - | - | +| 0.0130 | 15 | 9.3845 | - | - | - | - | - | - | - | +| 0.0138 | 16 | 9.3638 | - | - | - | - | - | - | - | +| 0.0147 | 17 | 9.3365 | - | - | - | - | - | - | - | +| 0.0156 | 18 | 8.267 | - | - | - | - | - | - | - | +| 0.0164 | 19 | 9.8727 | - | - | - | - | - | - | - | +| 0.0173 | 20 | 9.6234 | - | - | - | - | - | - | - | +| 0.0182 | 21 | 9.0452 | - | - | - | - | - | - | - | +| 0.0190 | 22 | 8.5295 | - | - | - | - | - | - | - | +| 0.0199 | 23 | 7.8237 | - | - | - | - | - | - | - | +| 0.0208 | 24 | 8.5162 | - | - | - | - | - | - | - | +| 0.0216 | 25 | 8.312 | - | - | - | - | - | - | - | +| 0.0225 | 26 | 9.2816 | - | - | - | - | - | - | - | +| 0.0233 | 27 | 7.9128 | - | - | - | - | - | - | - | +| 0.0242 | 28 | 7.6773 | - | - | - | - | - | - | - | +| 0.0251 | 29 | 7.4793 | - | - | - | - | - | - | - | +| 0.0259 | 30 | 8.2437 | - | - | - | - | - | - | - | +| 0.0268 | 31 | 8.6053 | - | - | - | - | - | - | - | +| 0.0277 | 32 | 7.4762 | - | - | - | - | - | - | - | +| 0.0285 | 33 | 10.5289 | - | - | - | - | - | - | - | +| 0.0294 | 34 | 7.8911 | - | - | - | - | - | - | - | +| 0.0303 | 35 | 7.9708 | - | - | - | - | - | - | - | +| 0.0311 | 36 | 7.2581 | - | - | - | - | - | - | - | +| 0.0320 | 37 | 8.6747 | - | - | - | - | - | - | - | +| 0.0329 | 38 | 7.308 | - | - | - | - | - | - | - | +| 0.0337 | 39 | 8.6224 | - | - | - | - | - | - | - | +| 0.0346 | 40 | 12.3613 | - | - | - | - | - | - | - | +| 0.0355 | 41 | 7.2108 | - | - | - | - | - | - | - | +| 0.0363 | 42 | 7.831 | - | - | - | - | - | - | - | +| 0.0372 | 43 | 6.9282 | - | - | - | - | - | - | - | +| 0.0381 | 44 | 6.9092 | - | - | - | - | - | - | - | +| 0.0389 | 45 | 8.9053 | - | - | - | - | - | - | - | +| 0.0398 | 46 | 7.5276 | - | - | - | - | - | - | - | +| 0.0406 | 47 | 7.4793 | - | - | - | - | - | - | - | +| 0.0415 | 48 | 7.4401 | - | - | - | - | - | - | - | +| 0.0424 | 49 | 6.6742 | - | - | - | - | - | - | - | +| 0.0432 | 50 | 8.89 | - | - | - | - | - | - | - | +| 0.0441 | 51 | 8.6311 | - | - | - | - | - | - | - | +| 0.0450 | 52 | 6.9648 | - | - | - | - | - | - | - | +| 0.0458 | 53 | 7.0633 | - | - | - | - | - | - | - | +| 0.0467 | 54 | 8.1331 | - | - | - | - | - | - | - | +| 0.0476 | 55 | 8.4931 | - | - | - | - | - | - | - | +| 0.0484 | 56 | 6.9534 | - | - | - | - | - | - | - | +| 0.0493 | 57 | 6.9585 | - | - | - | - | - | - | - | +| 0.0502 | 58 | 7.6699 | - | - | - | - | - | - | - | +| 0.0510 | 59 | 6.5076 | - | - | - | - | - | - | - | +| 0.0519 | 60 | 9.5443 | - | - | - | - | - | - | - | +| 0.0528 | 61 | 6.7481 | - | - | - | - | - | - | - | +| 0.0536 | 62 | 6.4521 | - | - | - | - | - | - | - | +| 0.0545 | 63 | 8.1839 | - | - | - | - | - | - | - | +| 0.0553 | 64 | 7.1924 | - | - | - | - | - | - | - | +| 0.0562 | 65 | 7.5767 | - | - | - | - | - | - | - | +| 0.0571 | 66 | 7.2645 | - | - | - | - | - | - | - | +| 0.0579 | 67 | 6.175 | - | - | - | - | - | - | - | +| 0.0588 | 68 | 6.5491 | - | - | - | - | - | - | - | +| 0.0597 | 69 | 6.7543 | - | - | - | - | - | - | - | +| 0.0605 | 70 | 6.98 | - | - | - | - | - | - | - | +| 0.0614 | 71 | 6.76 | - | - | - | - | - | - | - | +| 0.0623 | 72 | 7.2167 | - | - | - | - | - | - | - | +| 0.0631 | 73 | 9.219 | - | - | - | - | - | - | - | +| 0.0640 | 74 | 7.3113 | - | - | - | - | - | - | - | +| 0.0649 | 75 | 6.4098 | - | - | - | - | - | - | - | +| 0.0657 | 76 | 8.3399 | - | - | - | - | - | - | - | +| 0.0666 | 77 | 8.6991 | - | - | - | - | - | - | - | +| 0.0675 | 78 | 9.5087 | - | - | - | - | - | - | - | +| 0.0683 | 79 | 7.3491 | - | - | - | - | - | - | - | +| 0.0692 | 80 | 6.8214 | - | - | - | - | - | - | - | +| 0.0700 | 81 | 6.5489 | - | - | - | - | - | - | - | +| 0.0709 | 82 | 6.7692 | - | - | - | - | - | - | - | +| 0.0718 | 83 | 8.8059 | - | - | - | - | - | - | - | +| 0.0726 | 84 | 6.4811 | - | - | - | - | - | - | - | +| 0.0735 | 85 | 7.0495 | - | - | - | - | - | - | - | +| 0.0744 | 86 | 5.8256 | - | - | - | - | - | - | - | +| 0.0752 | 87 | 5.7203 | - | - | - | - | - | - | - | +| 0.0761 | 88 | 6.5094 | - | - | - | - | - | - | - | +| 0.0770 | 89 | 5.7523 | - | - | - | - | - | - | - | +| 0.0778 | 90 | 6.0464 | - | - | - | - | - | - | - | +| 0.0787 | 91 | 6.5233 | - | - | - | - | - | - | - | +| 0.0796 | 92 | 6.8966 | - | - | - | - | - | - | - | +| 0.0804 | 93 | 6.3621 | - | - | - | - | - | - | - | +| 0.0813 | 94 | 6.0194 | - | - | - | - | - | - | - | +| 0.0822 | 95 | 6.6797 | - | - | - | - | - | - | - | +| 0.0830 | 96 | 5.6214 | - | - | - | - | - | - | - | +| 0.0839 | 97 | 6.4946 | - | - | - | - | - | - | - | +| 0.0848 | 98 | 5.9069 | - | - | - | - | - | - | - | +| 0.0856 | 99 | 6.8848 | - | - | - | - | - | - | - | +| 0.0865 | 100 | 7.7248 | - | - | - | - | - | - | - | +| 0.0873 | 101 | 6.9069 | - | - | - | - | - | - | - | +| 0.0882 | 102 | 6.3612 | - | - | - | - | - | - | - | +| 0.0891 | 103 | 7.6664 | - | - | - | - | - | - | - | +| 0.0899 | 104 | 5.4654 | - | - | - | - | - | - | - | +| 0.0908 | 105 | 6.7583 | - | - | - | - | - | - | - | +| 0.0917 | 106 | 6.354 | - | - | - | - | - | - | - | +| 0.0925 | 107 | 5.0476 | - | - | - | - | - | - | - | +| 0.0934 | 108 | 5.535 | - | - | - | - | - | - | - | +| 0.0943 | 109 | 5.125 | - | - | - | - | - | - | - | +| 0.0951 | 110 | 5.3941 | - | - | - | - | - | - | - | +| 0.0960 | 111 | 7.6313 | - | - | - | - | - | - | - | +| 0.0969 | 112 | 5.584 | - | - | - | - | - | - | - | +| 0.0977 | 113 | 6.7148 | - | - | - | - | - | - | - | +| 0.0986 | 114 | 4.3739 | - | - | - | - | - | - | - | +| 0.0995 | 115 | 7.8202 | 1.9403 | 0.9156 | 0.8310 | 0.7609 | 0.6890 | 0.9792 | 0.8021 | +| 0.1003 | 116 | 4.42 | - | - | - | - | - | - | - | +| 0.1012 | 117 | 4.5047 | - | - | - | - | - | - | - | +| 0.1020 | 118 | 4.427 | - | - | - | - | - | - | - | +| 0.1029 | 119 | 3.1201 | - | - | - | - | - | - | - | +| 0.1038 | 120 | 4.3632 | - | - | - | - | - | - | - | +| 0.1046 | 121 | 3.5101 | - | - | - | - | - | - | - | +| 0.1055 | 122 | 4.7499 | - | - | - | - | - | - | - | +| 0.1064 | 123 | 4.5242 | - | - | - | - | - | - | - | +| 0.1072 | 124 | 3.379 | - | - | - | - | - | - | - | +| 0.1081 | 125 | 2.8004 | - | - | - | - | - | - | - | +| 0.1090 | 126 | 2.8855 | - | - | - | - | - | - | - | +| 0.1098 | 127 | 3.937 | - | - | - | - | - | - | - | +| 0.1107 | 128 | 3.5914 | - | - | - | - | - | - | - | +| 0.1116 | 129 | 2.6459 | - | - | - | - | - | - | - | +| 0.1124 | 130 | 2.3887 | - | - | - | - | - | - | - | +| 0.1133 | 131 | 4.1043 | - | - | - | - | - | - | - | +| 0.1142 | 132 | 2.2758 | - | - | - | - | - | - | - | +| 0.1150 | 133 | 2.859 | - | - | - | - | - | - | - | +| 0.1159 | 134 | 2.0162 | - | - | - | - | - | - | - | +| 0.1167 | 135 | 2.7069 | - | - | - | - | - | - | - | +| 0.1176 | 136 | 1.9536 | - | - | - | - | - | - | - | +| 0.1185 | 137 | 2.3063 | - | - | - | - | - | - | - | +| 0.1193 | 138 | 3.3783 | - | - | - | - | - | - | - | +| 0.1202 | 139 | 1.9538 | - | - | - | - | - | - | - | +| 0.1211 | 140 | 2.8959 | - | - | - | - | - | - | - | +| 0.1219 | 141 | 2.3064 | - | - | - | - | - | - | - | +| 0.1228 | 142 | 2.4625 | - | - | - | - | - | - | - | +| 0.1237 | 143 | 2.4083 | - | - | - | - | - | - | - | +| 0.1245 | 144 | 1.5914 | - | - | - | - | - | - | - | +| 0.1254 | 145 | 1.5798 | - | - | - | - | - | - | - | +| 0.1263 | 146 | 1.5194 | - | - | - | - | - | - | - | +| 0.1271 | 147 | 1.5924 | - | - | - | - | - | - | - | +| 0.1280 | 148 | 3.1143 | - | - | - | - | - | - | - | +| 0.1289 | 149 | 1.8632 | - | - | - | - | - | - | - | +| 0.1297 | 150 | 2.0489 | - | - | - | - | - | - | - | +| 0.1306 | 151 | 1.5573 | - | - | - | - | - | - | - | +| 0.1314 | 152 | 2.8024 | - | - | - | - | - | - | - | +| 0.1323 | 153 | 1.9423 | - | - | - | - | - | - | - | +| 0.1332 | 154 | 3.1444 | - | - | - | - | - | - | - | +| 0.1340 | 155 | 2.5482 | - | - | - | - | - | - | - | +| 0.1349 | 156 | 1.0428 | - | - | - | - | - | - | - | +| 0.1358 | 157 | 5.8267 | - | - | - | - | - | - | - | +| 0.1366 | 158 | 1.9785 | - | - | - | - | - | - | - | +| 0.1375 | 159 | 2.5306 | - | - | - | - | - | - | - | +| 0.1384 | 160 | 1.8271 | - | - | - | - | - | - | - | +| 0.1392 | 161 | 2.6579 | - | - | - | - | - | - | - | +| 0.1401 | 162 | 3.0193 | - | - | - | - | - | - | - | +| 0.1410 | 163 | 1.5596 | - | - | - | - | - | - | - | +| 0.1418 | 164 | 1.4505 | - | - | - | - | - | - | - | +| 0.1427 | 165 | 2.5331 | - | - | - | - | - | - | - | +| 0.1436 | 166 | 1.384 | - | - | - | - | - | - | - | +| 0.1444 | 167 | 3.3553 | - | - | - | - | - | - | - | +| 0.1453 | 168 | 2.2541 | - | - | - | - | - | - | - | +| 0.1462 | 169 | 1.1827 | - | - | - | - | - | - | - | +| 0.1470 | 170 | 1.3643 | - | - | - | - | - | - | - | +| 0.1479 | 171 | 1.9817 | - | - | - | - | - | - | - | +| 0.1487 | 172 | 2.3332 | - | - | - | - | - | - | - | +| 0.1496 | 173 | 1.4638 | - | - | - | - | - | - | - | +| 0.1505 | 174 | 1.7929 | - | - | - | - | - | - | - | +| 0.1513 | 175 | 1.0457 | - | - | - | - | - | - | - | +| 0.1522 | 176 | 1.4866 | - | - | - | - | - | - | - | +| 0.1531 | 177 | 1.7834 | - | - | - | - | - | - | - | +| 0.1539 | 178 | 1.1396 | - | - | - | - | - | - | - | +| 0.1548 | 179 | 1.9811 | - | - | - | - | - | - | - | +| 0.1557 | 180 | 1.1859 | - | - | - | - | - | - | - | +| 0.1565 | 181 | 1.5481 | - | - | - | - | - | - | - | +| 0.1574 | 182 | 1.5954 | - | - | - | - | - | - | - | +| 0.1583 | 183 | 2.2163 | - | - | - | - | - | - | - | +| 0.1591 | 184 | 1.475 | - | - | - | - | - | - | - | +| 0.1600 | 185 | 1.2394 | - | - | - | - | - | - | - | +| 0.1609 | 186 | 1.2713 | - | - | - | - | - | - | - | +| 0.1617 | 187 | 1.435 | - | - | - | - | - | - | - | +| 0.1626 | 188 | 1.1762 | - | - | - | - | - | - | - | +| 0.1634 | 189 | 2.3349 | - | - | - | - | - | - | - | +| 0.1643 | 190 | 1.1459 | - | - | - | - | - | - | - | +| 0.1652 | 191 | 1.313 | - | - | - | - | - | - | - | +| 0.1660 | 192 | 1.1422 | - | - | - | - | - | - | - | +| 0.1669 | 193 | 1.4937 | - | - | - | - | - | - | - | +| 0.1678 | 194 | 1.351 | - | - | - | - | - | - | - | +| 0.1686 | 195 | 1.1581 | - | - | - | - | - | - | - | +| 0.1695 | 196 | 2.1473 | - | - | - | - | - | - | - | +| 0.1704 | 197 | 0.8801 | - | - | - | - | - | - | - | +| 0.1712 | 198 | 1.3985 | - | - | - | - | - | - | - | +| 0.1721 | 199 | 0.8468 | - | - | - | - | - | - | - | +| 0.1730 | 200 | 1.0563 | - | - | - | - | - | - | - | +| 0.1738 | 201 | 0.9612 | - | - | - | - | - | - | - | +| 0.1747 | 202 | 1.665 | - | - | - | - | - | - | - | +| 0.1756 | 203 | 1.1505 | - | - | - | - | - | - | - | +| 0.1764 | 204 | 1.7958 | - | - | - | - | - | - | - | +| 0.1773 | 205 | 1.3115 | - | - | - | - | - | - | - | +| 0.1781 | 206 | 1.0855 | - | - | - | - | - | - | - | +| 0.1790 | 207 | 1.1051 | - | - | - | - | - | - | - | +| 0.1799 | 208 | 2.5371 | - | - | - | - | - | - | - | +| 0.1807 | 209 | 1.7992 | - | - | - | - | - | - | - | +| 0.1816 | 210 | 1.7798 | - | - | - | - | - | - | - | +| 0.1825 | 211 | 0.7093 | - | - | - | - | - | - | - | +| 0.1833 | 212 | 1.2681 | - | - | - | - | - | - | - | +| 0.1842 | 213 | 0.911 | - | - | - | - | - | - | - | +| 0.1851 | 214 | 1.1144 | - | - | - | - | - | - | - | +| 0.1859 | 215 | 0.8362 | - | - | - | - | - | - | - | +| 0.1868 | 216 | 0.838 | - | - | - | - | - | - | - | +| 0.1877 | 217 | 1.3143 | - | - | - | - | - | - | - | +| 0.1885 | 218 | 0.8799 | - | - | - | - | - | - | - | +| 0.1894 | 219 | 0.7928 | - | - | - | - | - | - | - | +| 0.1903 | 220 | 0.7077 | - | - | - | - | - | - | - | +| 0.1911 | 221 | 1.7815 | - | - | - | - | - | - | - | +| 0.1920 | 222 | 1.3599 | - | - | - | - | - | - | - | +| 0.1929 | 223 | 0.7413 | - | - | - | - | - | - | - | +| 0.1937 | 224 | 1.9959 | - | - | - | - | - | - | - | +| 0.1946 | 225 | 1.9112 | - | - | - | - | - | - | - | +| 0.1954 | 226 | 0.5033 | - | - | - | - | - | - | - | +| 0.1963 | 227 | 1.1669 | - | - | - | - | - | - | - | +| 0.1972 | 228 | 1.2109 | - | - | - | - | - | - | - | +| 0.1980 | 229 | 0.781 | - | - | - | - | - | - | - | +| 0.1989 | 230 | 1.5895 | 0.7282 | 0.9170 | 0.8984 | 0.7514 | 0.7132 | 0.9792 | 0.9271 | +| 0.1998 | 231 | 1.2439 | - | - | - | - | - | - | - | +| 0.2006 | 232 | 0.5947 | - | - | - | - | - | - | - | +| 0.2015 | 233 | 1.1687 | - | - | - | - | - | - | - | +| 0.2024 | 234 | 2.082 | - | - | - | - | - | - | - | +| 0.2032 | 235 | 0.521 | - | - | - | - | - | - | - | +| 0.2041 | 236 | 1.2406 | - | - | - | - | - | - | - | +| 0.2050 | 237 | 0.6586 | - | - | - | - | - | - | - | +| 0.2058 | 238 | 0.6746 | - | - | - | - | - | - | - | +| 0.2067 | 239 | 0.3925 | - | - | - | - | - | - | - | +| 0.2076 | 240 | 0.8654 | - | - | - | - | - | - | - | +| 0.2084 | 241 | 0.6723 | - | - | - | - | - | - | - | +| 0.2093 | 242 | 0.4345 | - | - | - | - | - | - | - | +| 0.2101 | 243 | 1.0822 | - | - | - | - | - | - | - | +| 0.2110 | 244 | 1.5697 | - | - | - | - | - | - | - | +| 0.2119 | 245 | 0.3771 | - | - | - | - | - | - | - | +| 0.2127 | 246 | 0.484 | - | - | - | - | - | - | - | +| 0.2136 | 247 | 1.0994 | - | - | - | - | - | - | - | +| 0.2145 | 248 | 1.0543 | - | - | - | - | - | - | - | +| 0.2153 | 249 | 1.1132 | - | - | - | - | - | - | - | +| 0.2162 | 250 | 1.7051 | - | - | - | - | - | - | - | +| 0.2171 | 251 | 0.5029 | - | - | - | - | - | - | - | +| 0.2179 | 252 | 1.5927 | - | - | - | - | - | - | - | +| 0.2188 | 253 | 0.9221 | - | - | - | - | - | - | - | +| 0.2197 | 254 | 0.989 | - | - | - | - | - | - | - | +| 0.2205 | 255 | 1.9468 | - | - | - | - | - | - | - | +| 0.2214 | 256 | 0.7299 | - | - | - | - | - | - | - | +| 0.2223 | 257 | 1.0199 | - | - | - | - | - | - | - | +| 0.2231 | 258 | 1.0238 | - | - | - | - | - | - | - | +| 0.2240 | 259 | 1.8386 | - | - | - | - | - | - | - | +| 0.2248 | 260 | 0.7887 | - | - | - | - | - | - | - | +| 0.2257 | 261 | 1.0248 | - | - | - | - | - | - | - | +| 0.2266 | 262 | 0.9891 | - | - | - | - | - | - | - | +| 0.2274 | 263 | 2.007 | - | - | - | - | - | - | - | +| 0.2283 | 264 | 1.6522 | - | - | - | - | - | - | - | +| 0.2292 | 265 | 0.8482 | - | - | - | - | - | - | - | +| 0.2300 | 266 | 0.7231 | - | - | - | - | - | - | - | +| 0.2309 | 267 | 0.9017 | - | - | - | - | - | - | - | +| 0.2318 | 268 | 1.2073 | - | - | - | - | - | - | - | +| 0.2326 | 269 | 0.3825 | - | - | - | - | - | - | - | +| 0.2335 | 270 | 0.6634 | - | - | - | - | - | - | - | +| 0.2344 | 271 | 1.3564 | - | - | - | - | - | - | - | +| 0.2352 | 272 | 1.1294 | - | - | - | - | - | - | - | +| 0.2361 | 273 | 0.5599 | - | - | - | - | - | - | - | +| 0.2370 | 274 | 0.6844 | - | - | - | - | - | - | - | +| 0.2378 | 275 | 0.7783 | - | - | - | - | - | - | - | +| 0.2387 | 276 | 1.101 | - | - | - | - | - | - | - | +| 0.2396 | 277 | 0.9447 | - | - | - | - | - | - | - | +| 0.2404 | 278 | 2.4452 | - | - | - | - | - | - | - | +| 0.2413 | 279 | 1.2032 | - | - | - | - | - | - | - | +| 0.2421 | 280 | 1.9775 | - | - | - | - | - | - | - | +| 0.2430 | 281 | 0.523 | - | - | - | - | - | - | - | +| 0.2439 | 282 | 0.6166 | - | - | - | - | - | - | - | +| 0.2447 | 283 | 0.7902 | - | - | - | - | - | - | - | +| 0.2456 | 284 | 1.1019 | - | - | - | - | - | - | - | +| 0.2465 | 285 | 1.1185 | - | - | - | - | - | - | - | +| 0.2473 | 286 | 0.7051 | - | - | - | - | - | - | - | +| 0.2482 | 287 | 1.076 | - | - | - | - | - | - | - | +| 0.2491 | 288 | 1.5065 | - | - | - | - | - | - | - | +| 0.2499 | 289 | 0.9986 | - | - | - | - | - | - | - | +| 0.2508 | 290 | 1.1196 | - | - | - | - | - | - | - | +| 0.2517 | 291 | 0.7055 | - | - | - | - | - | - | - | +| 0.2525 | 292 | 0.2865 | - | - | - | - | - | - | - | +| 0.2534 | 293 | 0.6973 | - | - | - | - | - | - | - | +| 0.2543 | 294 | 1.0347 | - | - | - | - | - | - | - | +| 0.2551 | 295 | 0.5065 | - | - | - | - | - | - | - | +| 0.2560 | 296 | 0.7479 | - | - | - | - | - | - | - | +| 0.2568 | 297 | 0.5214 | - | - | - | - | - | - | - | +| 0.2577 | 298 | 0.4925 | - | - | - | - | - | - | - | +| 0.2586 | 299 | 0.9331 | - | - | - | - | - | - | - | +| 0.2594 | 300 | 0.4239 | - | - | - | - | - | - | - | +| 0.2603 | 301 | 0.7426 | - | - | - | - | - | - | - | +| 0.2612 | 302 | 1.292 | - | - | - | - | - | - | - | +| 0.2620 | 303 | 1.1471 | - | - | - | - | - | - | - | +| 0.2629 | 304 | 0.5639 | - | - | - | - | - | - | - | +| 0.2638 | 305 | 1.592 | - | - | - | - | - | - | - | +| 0.2646 | 306 | 0.6063 | - | - | - | - | - | - | - | +| 0.2655 | 307 | 0.665 | - | - | - | - | - | - | - | +| 0.2664 | 308 | 0.7966 | - | - | - | - | - | - | - | +| 0.2672 | 309 | 0.6255 | - | - | - | - | - | - | - | +| 0.2681 | 310 | 0.7197 | - | - | - | - | - | - | - | +| 0.2690 | 311 | 2.22 | - | - | - | - | - | - | - | +| 0.2698 | 312 | 0.7472 | - | - | - | - | - | - | - | +| 0.2707 | 313 | 1.0278 | - | - | - | - | - | - | - | +| 0.2715 | 314 | 0.6434 | - | - | - | - | - | - | - | +| 0.2724 | 315 | 0.4596 | - | - | - | - | - | - | - | +| 0.2733 | 316 | 0.5547 | - | - | - | - | - | - | - | +| 0.2741 | 317 | 1.2906 | - | - | - | - | - | - | - | +| 0.2750 | 318 | 2.2893 | - | - | - | - | - | - | - | +| 0.2759 | 319 | 2.0067 | - | - | - | - | - | - | - | +| 0.2767 | 320 | 1.0523 | - | - | - | - | - | - | - | +| 0.2776 | 321 | 1.425 | - | - | - | - | - | - | - | +| 0.2785 | 322 | 1.0846 | - | - | - | - | - | - | - | +| 0.2793 | 323 | 1.614 | - | - | - | - | - | - | - | +| 0.2802 | 324 | 0.831 | - | - | - | - | - | - | - | +| 0.2811 | 325 | 0.8297 | - | - | - | - | - | - | - | +| 0.2819 | 326 | 0.5425 | - | - | - | - | - | - | - | +| 0.2828 | 327 | 0.6583 | - | - | - | - | - | - | - | +| 0.2837 | 328 | 1.0148 | - | - | - | - | - | - | - | +| 0.2845 | 329 | 1.4798 | - | - | - | - | - | - | - | +| 0.2854 | 330 | 0.3907 | - | - | - | - | - | - | - | +| 0.2862 | 331 | 1.2206 | - | - | - | - | - | - | - | +| 0.2871 | 332 | 0.6509 | - | - | - | - | - | - | - | +| 0.2880 | 333 | 0.5982 | - | - | - | - | - | - | - | +| 0.2888 | 334 | 1.066 | - | - | - | - | - | - | - | +| 0.2897 | 335 | 0.6874 | - | - | - | - | - | - | - | +| 0.2906 | 336 | 0.2232 | - | - | - | - | - | - | - | +| 0.2914 | 337 | 0.8978 | - | - | - | - | - | - | - | +| 0.2923 | 338 | 0.5689 | - | - | - | - | - | - | - | +| 0.2932 | 339 | 1.1131 | - | - | - | - | - | - | - | +| 0.2940 | 340 | 0.4493 | - | - | - | - | - | - | - | +| 0.2949 | 341 | 0.5409 | - | - | - | - | - | - | - | +| 0.2958 | 342 | 0.5921 | - | - | - | - | - | - | - | +| 0.2966 | 343 | 0.6657 | - | - | - | - | - | - | - | +| 0.2975 | 344 | 0.8345 | - | - | - | - | - | - | - | +| 0.2984 | 345 | 0.9418 | 0.6650 | 0.9193 | 0.8942 | 0.7530 | 0.7531 | 0.9792 | 0.9271 | +| 0.2992 | 346 | 0.3752 | - | - | - | - | - | - | - | +| 0.3001 | 347 | 0.362 | - | - | - | - | - | - | - | +| 0.3010 | 348 | 0.99 | - | - | - | - | - | - | - | +| 0.3018 | 349 | 0.4309 | - | - | - | - | - | - | - | +| 0.3027 | 350 | 0.4101 | - | - | - | - | - | - | - | +| 0.3035 | 351 | 0.357 | - | - | - | - | - | - | - | +| 0.3044 | 352 | 0.4257 | - | - | - | - | - | - | - | +| 0.3053 | 353 | 0.6665 | - | - | - | - | - | - | - | +| 0.3061 | 354 | 0.4523 | - | - | - | - | - | - | - | +| 0.3070 | 355 | 1.4959 | - | - | - | - | - | - | - | +| 0.3079 | 356 | 0.833 | - | - | - | - | - | - | - | +| 0.3087 | 357 | 0.7928 | - | - | - | - | - | - | - | +| 0.3096 | 358 | 0.3277 | - | - | - | - | - | - | - | +| 0.3105 | 359 | 0.5336 | - | - | - | - | - | - | - | +| 0.3113 | 360 | 0.4663 | - | - | - | - | - | - | - | +| 0.3122 | 361 | 0.5869 | - | - | - | - | - | - | - | +| 0.3131 | 362 | 0.6056 | - | - | - | - | - | - | - | +| 0.3139 | 363 | 0.5402 | - | - | - | - | - | - | - | +| 0.3148 | 364 | 1.046 | - | - | - | - | - | - | - | +| 0.3157 | 365 | 0.3102 | - | - | - | - | - | - | - | +| 0.3165 | 366 | 0.412 | - | - | - | - | - | - | - | +| 0.3174 | 367 | 0.5703 | - | - | - | - | - | - | - | +| 0.3182 | 368 | 1.6531 | - | - | - | - | - | - | - | +| 0.3191 | 369 | 0.4314 | - | - | - | - | - | - | - | +| 0.3200 | 370 | 0.7062 | - | - | - | - | - | - | - | +| 0.3208 | 371 | 0.5788 | - | - | - | - | - | - | - | +| 0.3217 | 372 | 1.8529 | - | - | - | - | - | - | - | +| 0.3226 | 373 | 0.6606 | - | - | - | - | - | - | - | +| 0.3234 | 374 | 0.8089 | - | - | - | - | - | - | - | +| 0.3243 | 375 | 0.3905 | - | - | - | - | - | - | - | +| 0.3252 | 376 | 1.2417 | - | - | - | - | - | - | - | +| 0.3260 | 377 | 0.8738 | - | - | - | - | - | - | - | +| 0.3269 | 378 | 0.8544 | - | - | - | - | - | - | - | +| 0.3278 | 379 | 0.4667 | - | - | - | - | - | - | - | +| 0.3286 | 380 | 0.8825 | - | - | - | - | - | - | - | +| 0.3295 | 381 | 0.6003 | - | - | - | - | - | - | - | +| 0.3304 | 382 | 0.4643 | - | - | - | - | - | - | - | +| 0.3312 | 383 | 0.5097 | - | - | - | - | - | - | - | +| 0.3321 | 384 | 0.4679 | - | - | - | - | - | - | - | +| 0.3329 | 385 | 0.3732 | - | - | - | - | - | - | - | +| 0.3338 | 386 | 0.9031 | - | - | - | - | - | - | - | +| 0.3347 | 387 | 0.6668 | - | - | - | - | - | - | - | +| 0.3355 | 388 | 0.7715 | - | - | - | - | - | - | - | +| 0.3364 | 389 | 0.8536 | - | - | - | - | - | - | - | +| 0.3373 | 390 | 0.9303 | - | - | - | - | - | - | - | +| 0.3381 | 391 | 0.3704 | - | - | - | - | - | - | - | +| 0.3390 | 392 | 0.3678 | - | - | - | - | - | - | - | +| 0.3399 | 393 | 1.6854 | - | - | - | - | - | - | - | +| 0.3407 | 394 | 0.7636 | - | - | - | - | - | - | - | +| 0.3416 | 395 | 0.3462 | - | - | - | - | - | - | - | +| 0.3425 | 396 | 0.644 | - | - | - | - | - | - | - | +| 0.3433 | 397 | 0.7317 | - | - | - | - | - | - | - | +| 0.3442 | 398 | 0.7023 | - | - | - | - | - | - | - | +| 0.3451 | 399 | 0.7164 | - | - | - | - | - | - | - | +| 0.3459 | 400 | 0.4271 | - | - | - | - | - | - | - | +| 0.3468 | 401 | 0.7332 | - | - | - | - | - | - | - | +| 0.3477 | 402 | 0.3978 | - | - | - | - | - | - | - | +| 0.3485 | 403 | 0.5208 | - | - | - | - | - | - | - | +| 0.3494 | 404 | 0.4889 | - | - | - | - | - | - | - | +| 0.3502 | 405 | 0.769 | - | - | - | - | - | - | - | +| 0.3511 | 406 | 0.3932 | - | - | - | - | - | - | - | +| 0.3520 | 407 | 0.5919 | - | - | - | - | - | - | - | +| 0.3528 | 408 | 0.6037 | - | - | - | - | - | - | - | +| 0.3537 | 409 | 0.4321 | - | - | - | - | - | - | - | +| 0.3546 | 410 | 1.1022 | - | - | - | - | - | - | - | +| 0.3554 | 411 | 0.5996 | - | - | - | - | - | - | - | +| 0.3563 | 412 | 0.5673 | - | - | - | - | - | - | - | +| 0.3572 | 413 | 0.3437 | - | - | - | - | - | - | - | +| 0.3580 | 414 | 0.519 | - | - | - | - | - | - | - | +| 0.3589 | 415 | 1.5373 | - | - | - | - | - | - | - | +| 0.3598 | 416 | 0.764 | - | - | - | - | - | - | - | +| 0.3606 | 417 | 0.6131 | - | - | - | - | - | - | - | +| 0.3615 | 418 | 0.6826 | - | - | - | - | - | - | - | +| 0.3624 | 419 | 0.9631 | - | - | - | - | - | - | - | +| 0.3632 | 420 | 0.4283 | - | - | - | - | - | - | - | +| 0.3641 | 421 | 1.2001 | - | - | - | - | - | - | - | +| 0.3649 | 422 | 0.4499 | - | - | - | - | - | - | - | +| 0.3658 | 423 | 1.4073 | - | - | - | - | - | - | - | +| 0.3667 | 424 | 0.8035 | - | - | - | - | - | - | - | +| 0.3675 | 425 | 0.3207 | - | - | - | - | - | - | - | +| 0.3684 | 426 | 0.4752 | - | - | - | - | - | - | - | +| 0.3693 | 427 | 1.1256 | - | - | - | - | - | - | - | +| 0.3701 | 428 | 0.4226 | - | - | - | - | - | - | - | +| 0.3710 | 429 | 1.123 | - | - | - | - | - | - | - | +| 0.3719 | 430 | 0.4425 | - | - | - | - | - | - | - | +| 0.3727 | 431 | 0.4242 | - | - | - | - | - | - | - | +| 0.3736 | 432 | 1.0072 | - | - | - | - | - | - | - | +| 0.3745 | 433 | 0.241 | - | - | - | - | - | - | - | +| 0.3753 | 434 | 0.7359 | - | - | - | - | - | - | - | +| 0.3762 | 435 | 0.4583 | - | - | - | - | - | - | - | +| 0.3771 | 436 | 0.9001 | - | - | - | - | - | - | - | +| 0.3779 | 437 | 0.4614 | - | - | - | - | - | - | - | +| 0.3788 | 438 | 1.238 | - | - | - | - | - | - | - | +| 0.3796 | 439 | 1.5289 | - | - | - | - | - | - | - | +| 0.3805 | 440 | 0.3283 | - | - | - | - | - | - | - | +| 0.3814 | 441 | 0.4147 | - | - | - | - | - | - | - | +| 0.3822 | 442 | 0.4956 | - | - | - | - | - | - | - | +| 0.3831 | 443 | 0.5166 | - | - | - | - | - | - | - | +| 0.3840 | 444 | 0.2486 | - | - | - | - | - | - | - | +| 0.3848 | 445 | 0.4493 | - | - | - | - | - | - | - | +| 0.3857 | 446 | 1.0439 | - | - | - | - | - | - | - | +| 0.3866 | 447 | 1.249 | - | - | - | - | - | - | - | +| 0.3874 | 448 | 0.8329 | - | - | - | - | - | - | - | +| 0.3883 | 449 | 0.3256 | - | - | - | - | - | - | - | +| 0.3892 | 450 | 1.0333 | - | - | - | - | - | - | - | +| 0.3900 | 451 | 0.3821 | - | - | - | - | - | - | - | +| 0.3909 | 452 | 0.4578 | - | - | - | - | - | - | - | +| 0.3918 | 453 | 0.6716 | - | - | - | - | - | - | - | +| 0.3926 | 454 | 0.2694 | - | - | - | - | - | - | - | +| 0.3935 | 455 | 1.7739 | - | - | - | - | - | - | - | +| 0.3943 | 456 | 0.7109 | - | - | - | - | - | - | - | +| 0.3952 | 457 | 0.5733 | - | - | - | - | - | - | - | +| 0.3961 | 458 | 0.283 | - | - | - | - | - | - | - | +| 0.3969 | 459 | 0.4214 | - | - | - | - | - | - | - | +| 0.3978 | 460 | 0.6332 | 0.7835 | 0.9200 | 0.9115 | 0.7569 | 0.7818 | 0.9792 | 0.9375 | +| 0.3987 | 461 | 1.2458 | - | - | - | - | - | - | - | +| 0.3995 | 462 | 0.3104 | - | - | - | - | - | - | - | +| 0.4004 | 463 | 0.4634 | - | - | - | - | - | - | - | +| 0.4013 | 464 | 0.5893 | - | - | - | - | - | - | - | +| 0.4021 | 465 | 0.377 | - | - | - | - | - | - | - | +| 0.4030 | 466 | 0.5403 | - | - | - | - | - | - | - | +| 0.4039 | 467 | 0.9454 | - | - | - | - | - | - | - | +| 0.4047 | 468 | 0.9292 | - | - | - | - | - | - | - | +| 0.4056 | 469 | 0.4132 | - | - | - | - | - | - | - | +| 0.4065 | 470 | 0.9805 | - | - | - | - | - | - | - | +| 0.4073 | 471 | 0.7784 | - | - | - | - | - | - | - | +| 0.4082 | 472 | 0.369 | - | - | - | - | - | - | - | +| 0.4091 | 473 | 1.4911 | - | - | - | - | - | - | - | +| 0.4099 | 474 | 0.4223 | - | - | - | - | - | - | - | +| 0.4108 | 475 | 1.2959 | - | - | - | - | - | - | - | +| 0.4116 | 476 | 0.4928 | - | - | - | - | - | - | - | +| 0.4125 | 477 | 1.02 | - | - | - | - | - | - | - | +| 0.4134 | 478 | 0.5034 | - | - | - | - | - | - | - | +| 0.4142 | 479 | 1.6119 | - | - | - | - | - | - | - | +| 0.4151 | 480 | 0.59 | - | - | - | - | - | - | - | +| 0.4160 | 481 | 0.5589 | - | - | - | - | - | - | - | +| 0.4168 | 482 | 0.8402 | - | - | - | - | - | - | - | +| 0.4177 | 483 | 0.5293 | - | - | - | - | - | - | - | +| 0.4186 | 484 | 0.5911 | - | - | - | - | - | - | - | +| 0.4194 | 485 | 0.1819 | - | - | - | - | - | - | - | +| 0.4203 | 486 | 0.6963 | - | - | - | - | - | - | - | +| 0.4212 | 487 | 0.6004 | - | - | - | - | - | - | - | +| 0.4220 | 488 | 2.0825 | - | - | - | - | - | - | - | +| 0.4229 | 489 | 1.6674 | - | - | - | - | - | - | - | +| 0.4238 | 490 | 0.5685 | - | - | - | - | - | - | - | +| 0.4246 | 491 | 0.7835 | - | - | - | - | - | - | - | +| 0.4255 | 492 | 0.4613 | - | - | - | - | - | - | - | +| 0.4263 | 493 | 1.0492 | - | - | - | - | - | - | - | +| 0.4272 | 494 | 0.3512 | - | - | - | - | - | - | - | +| 0.4281 | 495 | 0.8227 | - | - | - | - | - | - | - | +| 0.4289 | 496 | 0.7849 | - | - | - | - | - | - | - | +| 0.4298 | 497 | 1.2823 | - | - | - | - | - | - | - | +| 0.4307 | 498 | 1.2799 | - | - | - | - | - | - | - | +| 0.4315 | 499 | 0.596 | - | - | - | - | - | - | - | +| 0.4324 | 500 | 0.6793 | - | - | - | - | - | - | - | +| 0.4333 | 501 | 0.575 | - | - | - | - | - | - | - | +| 0.4341 | 502 | 0.2631 | - | - | - | - | - | - | - | +| 0.4350 | 503 | 0.5173 | - | - | - | - | - | - | - | +| 0.4359 | 504 | 0.9963 | - | - | - | - | - | - | - | +| 0.4367 | 505 | 0.9315 | - | - | - | - | - | - | - | +| 0.4376 | 506 | 0.7878 | - | - | - | - | - | - | - | +| 0.4385 | 507 | 0.4346 | - 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| - | - | - | - | - | - | +| 0.4869 | 563 | 1.2893 | - | - | - | - | - | - | - | +| 0.4877 | 564 | 0.5666 | - | - | - | - | - | - | - | +| 0.4886 | 565 | 0.2794 | - | - | - | - | - | - | - | +| 0.4895 | 566 | 0.6303 | - | - | - | - | - | - | - | +| 0.4903 | 567 | 0.1724 | - | - | - | - | - | - | - | +| 0.4912 | 568 | 0.662 | - | - | - | - | - | - | - | +| 0.4921 | 569 | 0.4051 | - | - | - | - | - | - | - | +| 0.4929 | 570 | 0.7596 | - | - | - | - | - | - | - | +| 0.4938 | 571 | 0.4666 | - | - | - | - | - | - | - | +| 0.4947 | 572 | 0.8923 | - | - | - | - | - | - | - | +| 0.4955 | 573 | 0.3065 | - | - | - | - | - | - | - | +| 0.4964 | 574 | 0.6386 | - | - | - | - | - | - | - | +| 0.4973 | 575 | 0.2868 | 0.5409 | 0.9205 | 0.9123 | 0.7255 | 0.7246 | 0.9688 | 0.9479 | +| 0.4981 | 576 | 0.6007 | - | - | - | - | - | - | - | +| 0.4990 | 577 | 0.2853 | - | - | - | - | - | - | - | +| 0.4999 | 578 | 0.3982 | - | - | - | - | - | - | - | +| 0.5007 | 579 | 0.7686 | - | - | - | - | - | - | - | +| 0.5016 | 580 | 0.3809 | - 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| - | - | - | +| 0.8198 | 948 | 0.3962 | - | - | - | - | - | - | - | +| 0.8207 | 949 | 0.2953 | - | - | - | - | - | - | - | +| 0.8216 | 950 | 0.1737 | - | - | - | - | - | - | - | +| 0.8224 | 951 | 0.1876 | - | - | - | - | - | - | - | +| 0.8233 | 952 | 0.2037 | - | - | - | - | - | - | - | +| 0.8242 | 953 | 0.6859 | - | - | - | - | - | - | - | +| 0.8250 | 954 | 0.5139 | - | - | - | - | - | - | - | +| 0.8259 | 955 | 0.4557 | - | - | - | - | - | - | - | +| 0.8268 | 956 | 0.418 | - | - | - | - | - | - | - | +| 0.8276 | 957 | 0.3326 | - | - | - | - | - | - | - | +| 0.8285 | 958 | 0.6773 | - | - | - | - | - | - | - | +| 0.8293 | 959 | 0.1938 | - | - | - | - | - | - | - | +| 0.8302 | 960 | 0.2502 | - | - | - | - | - | - | - | +| 0.8311 | 961 | 0.1686 | - | - | - | - | - | - | - | +| 0.8319 | 962 | 0.1174 | - | - | - | - | - | - | - | +| 0.8328 | 963 | 0.3688 | - | - | - | - | - | - | - | +| 0.8337 | 964 | 0.4529 | - | - | - | - | - | - | - | +| 0.8345 | 965 | 0.5973 | - | - | - | - | - | - | - 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| - | - | - | - | - | - | +| 0.8518 | 985 | 0.3061 | - | - | - | - | - | - | - | +| 0.8527 | 986 | 0.8352 | - | - | - | - | - | - | - | +| 0.8536 | 987 | 0.125 | - | - | - | - | - | - | - | +| 0.8544 | 988 | 0.2284 | - | - | - | - | - | - | - | +| 0.8553 | 989 | 0.838 | - | - | - | - | - | - | - | +| 0.8562 | 990 | 0.5318 | - | - | - | - | - | - | - | +| 0.8570 | 991 | 0.1618 | - | - | - | - | - | - | - | +| 0.8579 | 992 | 0.0871 | - | - | - | - | - | - | - | +| 0.8587 | 993 | 0.6915 | - | - | - | - | - | - | - | +| 0.8596 | 994 | 0.3728 | - | - | - | - | - | - | - | +| 0.8605 | 995 | 0.2403 | - | - | - | - | - | - | - | +| 0.8613 | 996 | 0.0651 | - | - | - | - | - | - | - | +| 0.8622 | 997 | 0.5783 | - | - | - | - | - | - | - | +| 0.8631 | 998 | 0.2061 | - | - | - | - | - | - | - | +| 0.8639 | 999 | 0.154 | - | - | - | - | - | - | - | +| 0.8648 | 1000 | 0.4689 | - | - | - | - | - | - | - | +| 0.8657 | 1001 | 0.2088 | - | - | - | - | - | - | - | +| 0.8665 | 1002 | 0.5299 | - | - | - | - | - | - | - | +| 0.8674 | 1003 | 0.2689 | - | - | - | - | - | - | - | +| 0.8683 | 1004 | 0.3098 | - | - | - | - | - | - | - | +| 0.8691 | 1005 | 0.3478 | - | - | - | - | - | - | - | +| 0.8700 | 1006 | 0.1841 | - | - | - | - | - | - | - | +| 0.8709 | 1007 | 0.0862 | - | - | - | - | - | - | - | +| 0.8717 | 1008 | 0.5991 | - | - | - | - | - | - | - | +| 0.8726 | 1009 | 0.4026 | - | - | - | - | - | - | - | +| 0.8735 | 1010 | 0.4991 | - | - | - | - | - | - | - | +| 0.8743 | 1011 | 0.2786 | - | - | - | - | - | - | - | +| 0.8752 | 1012 | 0.4708 | - | - | - | - | - | - | - | +| 0.8760 | 1013 | 0.4201 | - | - | - | - | - | - | - | +| 0.8769 | 1014 | 0.2089 | - | - | - | - | - | - | - | +| 0.8778 | 1015 | 0.3158 | - | - | - | - | - | - | - | +| 0.8786 | 1016 | 0.7363 | - | - | - | - | - | - | - | +| 0.8795 | 1017 | 0.2885 | - | - | - | - | - | - | - | +| 0.8804 | 1018 | 0.4937 | - | - | - | - | - | - | - | +| 0.8812 | 1019 | 0.1684 | - | - | - | - | - | - | - | +| 0.8821 | 1020 | 0.2749 | - | - | - | - | - | - | - | +| 0.8830 | 1021 | 0.2789 | - | - | - | - | - | - | - | +| 0.8838 | 1022 | 0.5868 | - | - | - | - | - | - | - | +| 0.8847 | 1023 | 0.5894 | - | - | - | - | - | - | - | +| 0.8856 | 1024 | 0.1635 | - | - | - | - | - | - | - | +| 0.8864 | 1025 | 0.6829 | - | - | - | - | - | - | - | +| 0.8873 | 1026 | 0.1124 | - | - | - | - | - | - | - | +| 0.8882 | 1027 | 1.2642 | - | - | - | - | - | - | - | +| 0.8890 | 1028 | 0.6585 | - | - | - | - | - | - | - | +| 0.8899 | 1029 | 0.2622 | - | - | - | - | - | - | - | +| 0.8907 | 1030 | 0.2797 | - | - | - | - | - | - | - | +| 0.8916 | 1031 | 0.2747 | - | - | - | - | - | - | - | +| 0.8925 | 1032 | 0.3748 | - | - | - | - | - | - | - | +| 0.8933 | 1033 | 0.1792 | - | - | - | - | - | - | - | +| 0.8942 | 1034 | 0.342 | - | - | - | - | - | - | - | +| 0.8951 | 1035 | 0.3287 | 0.5188 | 0.9223 | 0.9135 | 0.7340 | 0.7000 | 0.9792 | 0.9375 | +| 0.8959 | 1036 | 0.5348 | - | - | - | - | - | - | - | +| 0.8968 | 1037 | 0.6588 | - | - | - | - | - | - | - | +| 0.8977 | 1038 | 0.5132 | - | - | - | - | - | - | - | +| 0.8985 | 1039 | 1.1707 | - | - | - | - | - | - | - | +| 0.8994 | 1040 | 0.1442 | - | - | - | - | - | - | - | +| 0.9003 | 1041 | 0.2243 | - | - | - | - | - | - | - | +| 0.9011 | 1042 | 0.3099 | - | - | - | - | - | - | - | +| 0.9020 | 1043 | 0.1839 | - | - | - | - | - | - | - | +| 0.9029 | 1044 | 0.6248 | - | - | - | - | - | - | - | +| 0.9037 | 1045 | 0.4605 | - | - | - | - | - | - | - | +| 0.9046 | 1046 | 0.4857 | - | - | - | - | - | - | - | +| 0.9054 | 1047 | 0.3966 | - | - | - | - | - | - | - | +| 0.9063 | 1048 | 0.4439 | - | - | - | - | - | - | - | +| 0.9072 | 1049 | 0.6051 | - | - | - | - | - | - | - | +| 0.9080 | 1050 | 0.3982 | - | - | - | - | - | - | - | +| 0.9089 | 1051 | 0.1718 | - | - | - | - | - | - | - | +| 0.9098 | 1052 | 0.2641 | - | - | - | - | - | - | - | +| 0.9106 | 1053 | 0.3788 | - | - | - | - | - | - | - | +| 0.9115 | 1054 | 0.4456 | - | - | - | - | - | - | - | +| 0.9124 | 1055 | 1.0234 | - | - | - | - | - | - | - | +| 0.9132 | 1056 | 0.6228 | - | - | - | - | - | - | - | +| 0.9141 | 1057 | 0.4588 | - | - | - | - | - | - | - | +| 0.9150 | 1058 | 0.8651 | - | - | - | - | - | - | - | +| 0.9158 | 1059 | 0.8571 | - | - | - | - | - | - | - | +| 0.9167 | 1060 | 0.6446 | - | - | - | - | - | - | - | +| 0.9176 | 1061 | 0.2193 | - | - | - | - | - | - | - | +| 0.9184 | 1062 | 0.258 | - | - | - | - | - | - | - | +| 0.9193 | 1063 | 0.4164 | - | - | - | - | - | - | - | +| 0.9201 | 1064 | 0.4783 | - | - | - | - | - | - | - | +| 0.9210 | 1065 | 0.246 | - | - | - | - | - | - | - | +| 0.9219 | 1066 | 0.3821 | - | - | - | - | - | - | - | +| 0.9227 | 1067 | 0.182 | - | - | - | - | - | - | - | +| 0.9236 | 1068 | 0.6648 | - | - | - | - | - | - | - | +| 0.9245 | 1069 | 0.5405 | - | - | - | - | - | - | - | +| 0.9253 | 1070 | 0.3681 | - | - | - | - | - | - | - | +| 0.9262 | 1071 | 0.2323 | - | - | - | - | - | - | - | +| 0.9271 | 1072 | 0.4676 | - | - | - | - | - | - | - | +| 0.9279 | 1073 | 0.5141 | - | - | - | - | - | - | - | +| 0.9288 | 1074 | 0.3067 | - | - | - | - | - | - | - | +| 0.9297 | 1075 | 0.4638 | - | - | - | - | - | - | - | +| 0.9305 | 1076 | 0.3082 | - | - | - | - | - | - | - | +| 0.9314 | 1077 | 0.4003 | - | - | - | - | - | - | - | +| 0.9323 | 1078 | 0.3053 | - | - | - | - | - | - | - | +| 0.9331 | 1079 | 0.1876 | - | - | - | - | - | - | - | +| 0.9340 | 1080 | 0.569 | - | - | - | - | - | - | - | +| 0.9349 | 1081 | 0.8728 | - | - | - | - | - | - | - | +| 0.9357 | 1082 | 0.4347 | - | - | - | - | - | - | - | +| 0.9366 | 1083 | 0.8919 | - | - | - | - | - | - | - | +| 0.9374 | 1084 | 0.4828 | - | - | - | - | - | - | - | +| 0.9383 | 1085 | 0.4338 | - | - | - | - | - | - | - | +| 0.9392 | 1086 | 0.359 | - | - | - | - | - | - | - | +| 0.9400 | 1087 | 0.3828 | - | - | - | - | - | - | - | +| 0.9409 | 1088 | 0.5294 | - | - | - | - | - | - | - | +| 0.9418 | 1089 | 0.4212 | - | - | - | - | - | - | - | +| 0.9426 | 1090 | 0.5723 | - | - | - | - | - | - | - | +| 0.9435 | 1091 | 0.2047 | - | - | - | - | - | - | - | +| 0.9444 | 1092 | 0.4359 | - | - | - | - | - | - | - | +| 0.9452 | 1093 | 0.2121 | - | - | - | - | - | - | - | +| 0.9461 | 1094 | 0.461 | - | - | - | - | - | - | - | +| 0.9470 | 1095 | 0.1127 | - | - | - | - | - | - | - | +| 0.9478 | 1096 | 0.5365 | - | - | - | - | - | - | - | +| 0.9487 | 1097 | 1.0112 | - | - | - | - | - | - | - | +| 0.9496 | 1098 | 0.6563 | - | - | - | - | - | - | - | +| 0.9504 | 1099 | 0.0732 | - | - | - | - | - | - | - | +| 0.9513 | 1100 | 0.1631 | - | - | - | - | - | - | - | +| 0.9521 | 1101 | 0.4512 | - | - | - | - | - | - | - | +| 0.9530 | 1102 | 0.2731 | - | - | - | - | - | - | - | +| 0.9539 | 1103 | 0.4866 | - | - | - | - | - | - | - | +| 0.9547 | 1104 | 0.2007 | - | - | - | - | - | - | - | +| 0.9556 | 1105 | 0.501 | - | - | - | - | - | - | - | +| 0.9565 | 1106 | 0.8343 | - | - | - | - | - | - | - | +| 0.9573 | 1107 | 0.1781 | - | - | - | - | - | - | - | +| 0.9582 | 1108 | 0.3011 | - | - | - | - | - | - | - | +| 0.9591 | 1109 | 0.423 | - | - | - | - | - | - | - | +| 0.9599 | 1110 | 0.6317 | - | - | - | - | - | - | - | +| 0.9608 | 1111 | 0.3058 | - | - | - | - | - | - | - | +| 0.9617 | 1112 | 0.1734 | - | - | - | - | - | - | - | +| 0.9625 | 1113 | 0.1935 | - | - | - | - | - | - | - | +| 0.9634 | 1114 | 0.1533 | - | - | - | - | - | - | - | +| 0.9643 | 1115 | 0.9337 | - | - | - | - | - | - | - | +| 0.9651 | 1116 | 0.3695 | - | - | - | - | - | - | - | +| 0.9660 | 1117 | 0.7095 | - | - | - | - | - | - | - | +| 0.9668 | 1118 | 0.4206 | - | - | - | - | - | - | - | +| 0.9677 | 1119 | 0.235 | - | - | - | - | - | - | - | +| 0.9686 | 1120 | 0.1233 | - | - | - | - | - | - | - | +| 0.9694 | 1121 | 0.1605 | - | - | - | - | - | - | - | +| 0.9703 | 1122 | 0.1095 | - | - | - | - | - | - | - | +| 0.9712 | 1123 | 0.2737 | - | - | - | - | - | - | - | +| 0.9720 | 1124 | 0.3482 | - | - | - | - | - | - | - | +| 0.9729 | 1125 | 0.4144 | - | - | - | - | - | - | - | +| 0.9738 | 1126 | 0.4055 | - | - | - | - | - | - | - | +| 0.9746 | 1127 | 0.5001 | - | - | - | - | - | - | - | +| 0.9755 | 1128 | 0.6129 | - | - | - | - | - | - | - | +| 0.9764 | 1129 | 0.3793 | - | - | - | - | - | - | - | +| 0.9772 | 1130 | 1.0901 | - | - | - | - | - | - | - | +| 0.9781 | 1131 | 0.1219 | - | - | - | - | - | - | - | +| 0.9790 | 1132 | 0.5126 | - | - | - | - | - | - | - | +| 0.9798 | 1133 | 0.2261 | - | - | - | - | - | - | - | +| 0.9807 | 1134 | 0.1636 | - | - | - | - | - | - | - | +| 0.9816 | 1135 | 0.2711 | - | - | - | - | - | - | - | +| 0.9824 | 1136 | 0.3117 | - | - | - | - | - | - | - | +| 0.9833 | 1137 | 0.8633 | - | - | - | - | - | - | - | +| 0.9841 | 1138 | 0.3619 | - | - | - | - | - | - | - | +| 0.9850 | 1139 | 0.1079 | - | - | - | - | - | - | - | +| 0.9859 | 1140 | 0.4303 | - | - | - | - | - | - | - | +| 0.9867 | 1141 | 0.1799 | - | - | - | - | - | - | - | +| 0.9876 | 1142 | 0.3902 | - | - | - | - | - | - | - | +| 0.9885 | 1143 | 0.1557 | - | - | - | - | - | - | - | +| 0.9893 | 1144 | 0.1057 | - | - | - | - | - | - | - | +| 0.9902 | 1145 | 0.1142 | - | - | - | - | - | - | - | + +
+ +### Framework Versions +- Python: 3.11.11 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.1 +- PyTorch: 2.5.1+cu124 +- Accelerate: 1.3.0 +- Datasets: 3.5.0 +- Tokenizers: 0.21.0 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@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", +} +``` + +#### Matryoshka2dLoss +```bibtex +@misc{li20242d, + title={2D Matryoshka Sentence Embeddings}, + author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, + year={2024}, + eprint={2402.14776}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +#### MatryoshkaLoss +```bibtex +@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} +} +``` + +#### GISTEmbedLoss +```bibtex +@misc{solatorio2024gistembed, + title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, + author={Aivin V. Solatorio}, + year={2024}, + eprint={2402.16829}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + + + + + \ No newline at end of file