diff --git "a/checkpoint-1830/README.md" "b/checkpoint-1830/README.md" new file mode 100644--- /dev/null +++ "b/checkpoint-1830/README.md" @@ -0,0 +1,2657 @@ +--- +base_model: microsoft/deberta-v3-small +library_name: sentence-transformers +metrics: +- pearson_cosine +- spearman_cosine +- pearson_manhattan +- spearman_manhattan +- pearson_euclidean +- spearman_euclidean +- pearson_dot +- spearman_dot +- pearson_max +- spearman_max +- cosine_accuracy +- cosine_accuracy_threshold +- cosine_f1 +- cosine_f1_threshold +- cosine_precision +- cosine_recall +- cosine_ap +- dot_accuracy +- dot_accuracy_threshold +- dot_f1 +- dot_f1_threshold +- dot_precision +- dot_recall +- dot_ap +- manhattan_accuracy +- manhattan_accuracy_threshold +- manhattan_f1 +- manhattan_f1_threshold +- manhattan_precision +- manhattan_recall +- manhattan_ap +- euclidean_accuracy +- euclidean_accuracy_threshold +- euclidean_f1 +- euclidean_f1_threshold +- euclidean_precision +- euclidean_recall +- euclidean_ap +- max_accuracy +- max_accuracy_threshold +- max_f1 +- max_f1_threshold +- max_precision +- max_recall +- max_ap +pipeline_tag: sentence-similarity +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:32500 +- loss:GISTEmbedLoss +widget: +- source_sentence: phase changes do not change + sentences: + - The major Atlantic slave trading nations, ordered by trade volume, were the Portuguese, + the British, the Spanish, the French, the Dutch, and the Danish. Several had established + outposts on the African coast where they purchased slaves from local African leaders. + - "phase changes do not change mass. Particles have mass, but mass is energy. \n\ + \ phase changes do not change energy" + - According to the U.S. Census Bureau , the county is a total area of , which has + land and ( 0.2 % ) is water . +- source_sentence: what jobs can you get with a bachelor degree in anthropology? + sentences: + - To determine the atomic weight of an element, you should add up protons and neutrons. + - '[''Paleontologist*'', ''Archaeologist*'', ''University Professor*'', ''Market + Research Analyst*'', ''Primatologist.'', ''Forensic Scientist*'', ''Medical Anthropologist.'', + ''Museum Technician.'']' + - The wingspan flies , the moth comes depending on the location from July to August + . +- source_sentence: Identify different forms of energy (e.g., light, sound, heat). + sentences: + - '`` Irreplaceable '''' '''' remained on the chart for thirty weeks , and was certified + double-platinum by the Recording Industry Association of America ( RIAA ) , denoting + sales of two million downloads , and had sold over 3,139,000 paid digital downloads + in the US as of October 2012 , according to Nielsen SoundScan . ''''' + - On Rotten Tomatoes , the film has a rating of 63 % , based on 87 reviews , with + an average rating of 5.9/10 . + - Heat, light, and sound are all different forms of energy. +- source_sentence: what is so small it can only be seen with an electron microscope? + sentences: + - "Viruses are so small that they can be seen only with an electron microscope..\ + \ Where most viruses are DNA, HIV is an RNA virus. \n HIV is so small it can only\ + \ be seen with an electron microscope" + - The development of modern lasers has opened many doors to both research and applications. + A laser beam was used to measure the distance from the Earth to the moon. Lasers + are important components of CD players. As the image above illustrates, lasers + can provide precise focusing of beams to selectively destroy cancer cells in patients. + The ability of a laser to focus precisely is due to high-quality crystals that + help give rise to the laser beam. A variety of techniques are used to manufacture + pure crystals for use in lasers. + - Discussion for (a) This value is the net work done on the package. The person + actually does more work than this, because friction opposes the motion. Friction + does negative work and removes some of the energy the person expends and converts + it to thermal energy. The net work equals the sum of the work done by each individual + force. Strategy and Concept for (b) The forces acting on the package are gravity, + the normal force, the force of friction, and the applied force. The normal force + and force of gravity are each perpendicular to the displacement, and therefore + do no work. Solution for (b) The applied force does work. +- source_sentence: what aspects of your environment may relate to the epidemic of + obesity + sentences: + - Jan Kromkamp ( born August 17 , 1980 in Makkinga , Netherlands ) is a Dutch footballer + . + - When chemicals in solution react, the proper way of writing the chemical formulas + of the dissolved ionic compounds is in terms of the dissociated ions, not the + complete ionic formula. A complete ionic equation is a chemical equation in which + the dissolved ionic compounds are written as separated ions. Solubility rules + are very useful in determining which ionic compounds are dissolved and which are + not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement + reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation + includes NaCl, AgNO3, and NaNO3 written as separated ions:. + - Genetic changes in human populations occur too slowly to be responsible for the + obesity epidemic. Nevertheless, the variation in how people respond to the environment + that promotes physical inactivity and intake of high-calorie foods suggests that + genes do play a role in the development of obesity. +model-index: +- name: SentenceTransformer based on microsoft/deberta-v3-small + results: + - task: + type: semantic-similarity + name: Semantic Similarity + dataset: + name: sts test + type: sts-test + metrics: + - type: pearson_cosine + value: 0.6764054032395426 + name: Pearson Cosine + - type: spearman_cosine + value: 0.6929200286447658 + name: Spearman Cosine + - type: pearson_manhattan + value: 0.700820653979197 + name: Pearson Manhattan + - type: spearman_manhattan + value: 0.6972355270345225 + name: Spearman Manhattan + - type: pearson_euclidean + value: 0.6974498133985872 + name: Pearson Euclidean + - type: spearman_euclidean + value: 0.6929374816367083 + name: Spearman Euclidean + - type: pearson_dot + value: 0.676046362499036 + name: Pearson Dot + - type: spearman_dot + value: 0.692721109287881 + name: Spearman Dot + - type: pearson_max + value: 0.700820653979197 + name: Pearson Max + - type: spearman_max + value: 0.6972355270345225 + name: Spearman Max + - task: + type: binary-classification + name: Binary Classification + dataset: + name: allNLI dev + type: allNLI-dev + metrics: + - type: cosine_accuracy + value: 0.697265625 + name: Cosine Accuracy + - type: cosine_accuracy_threshold + value: 0.9135359525680542 + name: Cosine Accuracy Threshold + - type: cosine_f1 + value: 0.5375854214123007 + name: Cosine F1 + - type: cosine_f1_threshold + value: 0.8361715078353882 + name: Cosine F1 Threshold + - type: cosine_precision + value: 0.44360902255639095 + name: Cosine Precision + - type: cosine_recall + value: 0.6820809248554913 + name: Cosine Recall + - type: cosine_ap + value: 0.5215030688158616 + name: Cosine Ap + - type: dot_accuracy + value: 0.697265625 + name: Dot Accuracy + - type: dot_accuracy_threshold + value: 702.328125 + name: Dot Accuracy Threshold + - type: dot_f1 + value: 0.5375854214123007 + name: Dot F1 + - type: dot_f1_threshold + value: 642.5977783203125 + name: Dot F1 Threshold + - type: dot_precision + value: 0.44360902255639095 + name: Dot Precision + - type: dot_recall + value: 0.6820809248554913 + name: Dot Recall + - type: dot_ap + value: 0.5215320356910601 + name: Dot Ap + - type: manhattan_accuracy + value: 0.705078125 + name: Manhattan Accuracy + - type: manhattan_accuracy_threshold + value: 246.65103149414062 + name: Manhattan Accuracy Threshold + - type: manhattan_f1 + value: 0.5364705882352941 + name: Manhattan F1 + - type: manhattan_f1_threshold + value: 332.9952392578125 + name: Manhattan F1 Threshold + - type: manhattan_precision + value: 0.4523809523809524 + name: Manhattan Precision + - type: manhattan_recall + value: 0.6589595375722543 + name: Manhattan Recall + - type: manhattan_ap + value: 0.5262241457144412 + name: Manhattan Ap + - type: euclidean_accuracy + value: 0.697265625 + name: Euclidean Accuracy + - type: euclidean_accuracy_threshold + value: 11.530282020568848 + name: Euclidean Accuracy Threshold + - type: euclidean_f1 + value: 0.5375854214123007 + name: Euclidean F1 + - type: euclidean_f1_threshold + value: 15.868341445922852 + name: Euclidean F1 Threshold + - type: euclidean_precision + value: 0.44360902255639095 + name: Euclidean Precision + - type: euclidean_recall + value: 0.6820809248554913 + name: Euclidean Recall + - type: euclidean_ap + value: 0.5215763814962693 + name: Euclidean Ap + - type: max_accuracy + value: 0.705078125 + name: Max Accuracy + - type: max_accuracy_threshold + value: 702.328125 + name: Max Accuracy Threshold + - type: max_f1 + value: 0.5375854214123007 + name: Max F1 + - type: max_f1_threshold + value: 642.5977783203125 + name: Max F1 Threshold + - type: max_precision + value: 0.4523809523809524 + name: Max Precision + - type: max_recall + value: 0.6820809248554913 + name: Max Recall + - type: max_ap + value: 0.5262241457144412 + name: Max Ap + - task: + type: binary-classification + name: Binary Classification + dataset: + name: Qnli dev + type: Qnli-dev + metrics: + - type: cosine_accuracy + value: 0.6796875 + name: Cosine Accuracy + - type: cosine_accuracy_threshold + value: 0.8366684317588806 + name: Cosine Accuracy Threshold + - type: cosine_f1 + value: 0.6749611197511665 + name: Cosine F1 + - type: cosine_f1_threshold + value: 0.7199565172195435 + name: Cosine F1 Threshold + - type: cosine_precision + value: 0.5331695331695332 + name: Cosine Precision + - type: cosine_recall + value: 0.9194915254237288 + name: Cosine Recall + - type: cosine_ap + value: 0.7025711036957332 + name: Cosine Ap + - type: dot_accuracy + value: 0.6796875 + name: Dot Accuracy + - type: dot_accuracy_threshold + value: 643.223388671875 + name: Dot Accuracy Threshold + - type: dot_f1 + value: 0.673913043478261 + name: Dot F1 + - type: dot_f1_threshold + value: 552.9798583984375 + name: Dot F1 Threshold + - type: dot_precision + value: 0.5318627450980392 + name: Dot Precision + - type: dot_recall + value: 0.9194915254237288 + name: Dot Recall + - type: dot_ap + value: 0.7028877754827524 + name: Dot Ap + - type: manhattan_accuracy + value: 0.677734375 + name: Manhattan Accuracy + - type: manhattan_accuracy_threshold + value: 335.95025634765625 + name: Manhattan Accuracy Threshold + - type: manhattan_f1 + value: 0.6774716369529984 + name: Manhattan F1 + - type: manhattan_f1_threshold + value: 431.2140808105469 + name: Manhattan F1 Threshold + - type: manhattan_precision + value: 0.5485564304461942 + name: Manhattan Precision + - type: manhattan_recall + value: 0.885593220338983 + name: Manhattan Recall + - type: manhattan_ap + value: 0.7026678687600421 + name: Manhattan Ap + - type: euclidean_accuracy + value: 0.6796875 + name: Euclidean Accuracy + - type: euclidean_accuracy_threshold + value: 15.844499588012695 + name: Euclidean Accuracy Threshold + - type: euclidean_f1 + value: 0.6749611197511665 + name: Euclidean F1 + - type: euclidean_f1_threshold + value: 20.740978240966797 + name: Euclidean F1 Threshold + - type: euclidean_precision + value: 0.5331695331695332 + name: Euclidean Precision + - type: euclidean_recall + value: 0.9194915254237288 + name: Euclidean Recall + - type: euclidean_ap + value: 0.7026118478627446 + name: Euclidean Ap + - type: max_accuracy + value: 0.6796875 + name: Max Accuracy + - type: max_accuracy_threshold + value: 643.223388671875 + name: Max Accuracy Threshold + - type: max_f1 + value: 0.6774716369529984 + name: Max F1 + - type: max_f1_threshold + value: 552.9798583984375 + name: Max F1 Threshold + - type: max_precision + value: 0.5485564304461942 + name: Max Precision + - type: max_recall + value: 0.9194915254237288 + name: Max Recall + - type: max_ap + value: 0.7028877754827524 + name: Max Ap +--- + +# SentenceTransformer based on microsoft/deberta-v3-small + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). 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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 tokens +- **Similarity Function:** Cosine Similarity + + + + +### 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: DebertaV2Model + (1): AdvancedWeightedPooling( + (alpha_dropout_layer): Dropout(p=0.05, inplace=False) + (gate_dropout_layer): Dropout(p=0.0, inplace=False) + (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True) + (linear_attnOut): Linear(in_features=768, out_features=768, bias=True) + (mha): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) + ) + (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (layernorm_attnOut): LayerNorm((768,), 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/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp") +# Run inference +sentences = [ + 'what aspects of your environment may relate to the epidemic of obesity', + 'Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.', + 'When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:.', +] +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 + +#### 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 | 0.6764 | +| **spearman_cosine** | **0.6929** | +| pearson_manhattan | 0.7008 | +| spearman_manhattan | 0.6972 | +| pearson_euclidean | 0.6974 | +| spearman_euclidean | 0.6929 | +| pearson_dot | 0.676 | +| spearman_dot | 0.6927 | +| pearson_max | 0.7008 | +| spearman_max | 0.6972 | + +#### Binary Classification +* Dataset: `allNLI-dev` +* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) + +| Metric | Value | +|:-----------------------------|:-----------| +| cosine_accuracy | 0.6973 | +| cosine_accuracy_threshold | 0.9135 | +| cosine_f1 | 0.5376 | +| cosine_f1_threshold | 0.8362 | +| cosine_precision | 0.4436 | +| cosine_recall | 0.6821 | +| cosine_ap | 0.5215 | +| dot_accuracy | 0.6973 | +| dot_accuracy_threshold | 702.3281 | +| dot_f1 | 0.5376 | +| dot_f1_threshold | 642.5978 | +| dot_precision | 0.4436 | +| dot_recall | 0.6821 | +| dot_ap | 0.5215 | +| manhattan_accuracy | 0.7051 | +| manhattan_accuracy_threshold | 246.651 | +| manhattan_f1 | 0.5365 | +| manhattan_f1_threshold | 332.9952 | +| manhattan_precision | 0.4524 | +| manhattan_recall | 0.659 | +| manhattan_ap | 0.5262 | +| euclidean_accuracy | 0.6973 | +| euclidean_accuracy_threshold | 11.5303 | +| euclidean_f1 | 0.5376 | +| euclidean_f1_threshold | 15.8683 | +| euclidean_precision | 0.4436 | +| euclidean_recall | 0.6821 | +| euclidean_ap | 0.5216 | +| max_accuracy | 0.7051 | +| max_accuracy_threshold | 702.3281 | +| max_f1 | 0.5376 | +| max_f1_threshold | 642.5978 | +| max_precision | 0.4524 | +| max_recall | 0.6821 | +| **max_ap** | **0.5262** | + +#### 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 | 0.6797 | +| cosine_accuracy_threshold | 0.8367 | +| cosine_f1 | 0.675 | +| cosine_f1_threshold | 0.72 | +| cosine_precision | 0.5332 | +| cosine_recall | 0.9195 | +| cosine_ap | 0.7026 | +| dot_accuracy | 0.6797 | +| dot_accuracy_threshold | 643.2234 | +| dot_f1 | 0.6739 | +| dot_f1_threshold | 552.9799 | +| dot_precision | 0.5319 | +| dot_recall | 0.9195 | +| dot_ap | 0.7029 | +| manhattan_accuracy | 0.6777 | +| manhattan_accuracy_threshold | 335.9503 | +| manhattan_f1 | 0.6775 | +| manhattan_f1_threshold | 431.2141 | +| manhattan_precision | 0.5486 | +| manhattan_recall | 0.8856 | +| manhattan_ap | 0.7027 | +| euclidean_accuracy | 0.6797 | +| euclidean_accuracy_threshold | 15.8445 | +| euclidean_f1 | 0.675 | +| euclidean_f1_threshold | 20.741 | +| euclidean_precision | 0.5332 | +| euclidean_recall | 0.9195 | +| euclidean_ap | 0.7026 | +| max_accuracy | 0.6797 | +| max_accuracy_threshold | 643.2234 | +| max_f1 | 0.6775 | +| max_f1_threshold | 552.9799 | +| max_precision | 0.5486 | +| max_recall | 0.9195 | +| **max_ap** | **0.7029** | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + + +* Size: 32,500 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | In which London road is Harrod’s department store? | Harrods, Brompton Road, London | Shopping/Department Stores in London | LondonTown.com Opening Times Britain's most famous store and possibly the most famous store in the world, Harrods features on many tourist 'must-see' lists - and with good reason. Its humble beginnings date back to 1849, when Charles Henry Harrod opened a small East End grocer and tea merchant business that emphasised impeccable service over value. Today, it occupies a vast seven floor site in London's fashionable Knightsbridge and boasts a phenomenal range of products from pianos and cooking pans to fashion and perfumery. The luxurious Urban Retreat can be found on the sixth floor while newer departments include Superbrands, with 17 boutiques from top international designers, and Salon du Parfums, housing some of the most exceptional and exclusive perfumes in the world. The Food Hall is ostentatious to the core and mouth-wateringly exotic, and the store as a whole is well served with 27 restaurants. At Christmas time the Brompton Road windows are transformed into a magical winter wonderland and Father Christmas takes up residence at the enchanting Christmas Grotto. The summer and winter sales are calendar events in the shopping year, and although both sales are extremely crowded there are some great bargains on offer. � | + | e. in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently. | Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas | + | Joe Cole was unable to join West Bromwich Albion . | On 16th October Joe Cole took a long hard look at himself realising that he would never get the opportunity to join West Bromwich Albion and joined Coventry City instead. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (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}) + (2): Normalize() + ), 'temperature': 0.025} + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 32 +- `per_device_eval_batch_size`: 256 +- `lr_scheduler_type`: cosine_with_min_lr +- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} +- `warmup_ratio`: 0.33 +- `save_safetensors`: False +- `fp16`: True +- `push_to_hub`: True +- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp +- `hub_strategy`: all_checkpoints +- `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`: 256 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 5e-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`: 3 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine_with_min_lr +- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} +- `warmup_ratio`: 0.33 +- `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`: True +- `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} +- `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/DeBERTa3-s-CustomPoolin-toytest4-step1-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 +- `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 +- `eval_use_gather_object`: False +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap | +|:------:|:----:|:-------------:|:------------------------:|:-----------------:|:---------------:| +| 0.0010 | 1 | 6.0688 | - | - | - | +| 0.0020 | 2 | 7.5576 | - | - | - | +| 0.0030 | 3 | 4.6849 | - | - | - | +| 0.0039 | 4 | 5.4503 | - | - | - | +| 0.0049 | 5 | 5.6057 | - | - | - | +| 0.0059 | 6 | 6.3049 | - | - | - | +| 0.0069 | 7 | 6.8336 | - | - | - | +| 0.0079 | 8 | 5.0777 | - | - | - | +| 0.0089 | 9 | 4.8358 | - | - | - | +| 0.0098 | 10 | 4.641 | - | - | - | +| 0.0108 | 11 | 4.828 | - | - | - | +| 0.0118 | 12 | 5.2269 | - | - | - | +| 0.0128 | 13 | 5.6772 | - | - | - | +| 0.0138 | 14 | 5.1422 | - | - | - | +| 0.0148 | 15 | 6.2469 | - | - | - | +| 0.0157 | 16 | 4.6802 | - | - | - | +| 0.0167 | 17 | 4.5492 | - | - | - | +| 0.0177 | 18 | 4.8062 | - | - | - | +| 0.0187 | 19 | 7.5141 | - | - | - | +| 0.0197 | 20 | 5.5202 | - | - | - | +| 0.0207 | 21 | 6.5025 | - | - | - | +| 0.0217 | 22 | 7.318 | - | - | - | +| 0.0226 | 23 | 4.6458 | - | - | - | +| 0.0236 | 24 | 4.6191 | - | - | - | +| 0.0246 | 25 | 4.3159 | - | - | - | +| 0.0256 | 26 | 6.3677 | - | - | - | +| 0.0266 | 27 | 5.6052 | - | - | - | +| 0.0276 | 28 | 4.196 | - | - | - | +| 0.0285 | 29 | 4.4802 | - | - | - | +| 0.0295 | 30 | 4.9193 | - | - | - | +| 0.0305 | 31 | 4.0996 | - | - | - | +| 0.0315 | 32 | 5.6307 | - | - | - | +| 0.0325 | 33 | 4.5745 | - | - | - | +| 0.0335 | 34 | 4.4514 | - | - | - | +| 0.0344 | 35 | 4.0617 | - | - | - | +| 0.0354 | 36 | 5.0298 | - | - | - | +| 0.0364 | 37 | 3.9815 | - | - | - | +| 0.0374 | 38 | 4.0871 | - | - | - | +| 0.0384 | 39 | 4.2378 | - | - | - | +| 0.0394 | 40 | 3.8226 | - | - | - | +| 0.0404 | 41 | 4.3519 | - | - | - | +| 0.0413 | 42 | 3.6345 | - | - | - | +| 0.0423 | 43 | 5.0829 | - | - | - | +| 0.0433 | 44 | 4.6701 | - | - | - | +| 0.0443 | 45 | 4.1371 | - | - | - | +| 0.0453 | 46 | 4.2418 | - | - | - | +| 0.0463 | 47 | 4.4766 | - | - | - | +| 0.0472 | 48 | 4.4797 | - | - | - | +| 0.0482 | 49 | 3.8471 | - | - | - | +| 0.0492 | 50 | 4.3194 | - | - | - | +| 0.0502 | 51 | 3.9426 | - | - | - | +| 0.0512 | 52 | 3.5333 | - | - | - | +| 0.0522 | 53 | 4.2426 | - | - | - | +| 0.0531 | 54 | 3.9816 | - | - | - | +| 0.0541 | 55 | 3.663 | - | - | - | +| 0.0551 | 56 | 3.9057 | - | - | - | +| 0.0561 | 57 | 4.0345 | - | - | - | +| 0.0571 | 58 | 3.5233 | - | - | - | +| 0.0581 | 59 | 3.7999 | - | - | - | +| 0.0591 | 60 | 3.1885 | - | - | - | +| 0.0600 | 61 | 3.6013 | - | - | - | +| 0.0610 | 62 | 3.392 | - | - | - | +| 0.0620 | 63 | 3.3814 | - | - | - | +| 0.0630 | 64 | 4.0428 | - | - | - | +| 0.0640 | 65 | 3.7825 | - | - | - | +| 0.0650 | 66 | 3.4181 | - | - | - | +| 0.0659 | 67 | 3.7793 | - | - | - | +| 0.0669 | 68 | 3.8344 | - | - | - | +| 0.0679 | 69 | 3.2165 | - | - | - | +| 0.0689 | 70 | 3.3811 | - | - | - | +| 0.0699 | 71 | 3.5984 | - | - | - | +| 0.0709 | 72 | 3.8583 | - | - | - | +| 0.0719 | 73 | 3.296 | - | - | - | +| 0.0728 | 74 | 2.7661 | - | - | - | +| 0.0738 | 75 | 2.9805 | - | - | - | +| 0.0748 | 76 | 2.566 | - | - | - | +| 0.0758 | 77 | 3.258 | - | - | - | +| 0.0768 | 78 | 3.3804 | - | - | - | +| 0.0778 | 79 | 2.8828 | - | - | - | +| 0.0787 | 80 | 3.1077 | - | - | - | +| 0.0797 | 81 | 2.9441 | - | - | - | +| 0.0807 | 82 | 2.9465 | - | - | - | +| 0.0817 | 83 | 2.7088 | - | - | - | +| 0.0827 | 84 | 2.9215 | - | - | - | +| 0.0837 | 85 | 3.4698 | - | - | - | +| 0.0846 | 86 | 2.2414 | - | - | - | +| 0.0856 | 87 | 3.1601 | - | - | - | +| 0.0866 | 88 | 2.7714 | - | - | - | +| 0.0876 | 89 | 3.0311 | - | - | - | +| 0.0886 | 90 | 3.0336 | - | - | - | +| 0.0896 | 91 | 1.9358 | - | - | - | +| 0.0906 | 92 | 2.6031 | - | - | - | +| 0.0915 | 93 | 2.7515 | - | - | - | +| 0.0925 | 94 | 2.8496 | - | - | - | +| 0.0935 | 95 | 1.8015 | - | - | - | +| 0.0945 | 96 | 2.8138 | - | - | - | +| 0.0955 | 97 | 2.0597 | - | - | - | +| 0.0965 | 98 | 2.1053 | - | - | - | +| 0.0974 | 99 | 2.6785 | - | - | - | +| 0.0984 | 100 | 2.588 | - | - | - | +| 0.0994 | 101 | 2.0099 | - | - | - | +| 0.1004 | 102 | 2.7947 | - | - | - | +| 0.1014 | 103 | 2.3274 | - | - | - | +| 0.1024 | 104 | 2.2545 | - | - | - | +| 0.1033 | 105 | 2.4575 | - | - | - | +| 0.1043 | 106 | 2.4413 | - | - | - | +| 0.1053 | 107 | 2.3185 | - | - | - | +| 0.1063 | 108 | 2.1577 | - | - | - | +| 0.1073 | 109 | 2.1278 | - | - | - | +| 0.1083 | 110 | 2.0967 | - | - | - | +| 0.1093 | 111 | 2.6142 | - | - | - | +| 0.1102 | 112 | 1.8553 | - | - | - | +| 0.1112 | 113 | 2.1523 | - | - | - | +| 0.1122 | 114 | 2.1726 | - | - | - | +| 0.1132 | 115 | 1.8564 | - | - | - | +| 0.1142 | 116 | 1.8413 | - | - | - | +| 0.1152 | 117 | 2.0441 | - | - | - | +| 0.1161 | 118 | 2.2159 | - | - | - | +| 0.1171 | 119 | 2.6779 | - | - | - | +| 0.1181 | 120 | 2.2976 | - | - | - | +| 0.1191 | 121 | 1.9407 | - | - | - | +| 0.1201 | 122 | 1.9019 | - | - | - | +| 0.1211 | 123 | 2.2149 | - | - | - | +| 0.1220 | 124 | 1.6823 | - | - | - | +| 0.1230 | 125 | 1.8402 | - | - | - | +| 0.1240 | 126 | 1.6914 | - | - | - | +| 0.125 | 127 | 2.1626 | - | - | - | +| 0.1260 | 128 | 1.6414 | - | - | - | +| 0.1270 | 129 | 2.2043 | - | - | - | +| 0.1280 | 130 | 1.9987 | - | - | - | +| 0.1289 | 131 | 1.8868 | - | - | - | +| 0.1299 | 132 | 1.8262 | - | - | - | +| 0.1309 | 133 | 2.0404 | - | - | - | +| 0.1319 | 134 | 1.9134 | - | - | - | +| 0.1329 | 135 | 2.3725 | - | - | - | +| 0.1339 | 136 | 1.4127 | - | - | - | +| 0.1348 | 137 | 1.6876 | - | - | - | +| 0.1358 | 138 | 1.8376 | - | - | - | +| 0.1368 | 139 | 1.6992 | - | - | - | +| 0.1378 | 140 | 1.5032 | - | - | - | +| 0.1388 | 141 | 2.0334 | - | - | - | +| 0.1398 | 142 | 2.3581 | - | - | - | +| 0.1407 | 143 | 1.4236 | - | - | - | +| 0.1417 | 144 | 2.202 | - | - | - | +| 0.1427 | 145 | 1.7654 | - | - | - | +| 0.1437 | 146 | 1.5748 | - | - | - | +| 0.1447 | 147 | 1.7996 | - | - | - | +| 0.1457 | 148 | 1.7517 | - | - | - | +| 0.1467 | 149 | 1.8933 | - | - | - | +| 0.1476 | 150 | 1.2836 | - | - | - | +| 0.1486 | 151 | 1.7145 | - | - | - | +| 0.1496 | 152 | 1.6499 | - | - | - | +| 0.1506 | 153 | 1.8273 | 0.4057 | 0.4389 | 0.6725 | +| 0.1516 | 154 | 2.2859 | - | - | - | +| 0.1526 | 155 | 1.0833 | - | - | - | +| 0.1535 | 156 | 1.6829 | - | - | - | +| 0.1545 | 157 | 2.1464 | - | - | - | +| 0.1555 | 158 | 1.745 | - | - | - | +| 0.1565 | 159 | 1.7319 | - | - | - | +| 0.1575 | 160 | 1.6968 | - | - | - | +| 0.1585 | 161 | 1.7401 | - | - | - | +| 0.1594 | 162 | 1.729 | - | - | - | +| 0.1604 | 163 | 2.0782 | - | - | - | +| 0.1614 | 164 | 2.6545 | - | - | - | +| 0.1624 | 165 | 1.4045 | - | - | - | +| 0.1634 | 166 | 1.2937 | - | - | - | +| 0.1644 | 167 | 1.1171 | - | - | - | +| 0.1654 | 168 | 1.3537 | - | - | - | +| 0.1663 | 169 | 1.7028 | - | - | - | +| 0.1673 | 170 | 1.4143 | - | - | - | +| 0.1683 | 171 | 1.8648 | - | - | - | +| 0.1693 | 172 | 1.6768 | - | - | - | +| 0.1703 | 173 | 1.9528 | - | - | - | +| 0.1713 | 174 | 1.1718 | - | - | - | +| 0.1722 | 175 | 1.8176 | - | - | - | +| 0.1732 | 176 | 0.8439 | - | - | - | +| 0.1742 | 177 | 1.5092 | - | - | - | +| 0.1752 | 178 | 1.1947 | - | - | - | +| 0.1762 | 179 | 1.6395 | - | - | - | +| 0.1772 | 180 | 1.4394 | - | - | - | +| 0.1781 | 181 | 1.7548 | - | - | - | +| 0.1791 | 182 | 1.1181 | - | - | - | +| 0.1801 | 183 | 1.0271 | - | - | - | +| 0.1811 | 184 | 2.3108 | - | - | - | +| 0.1821 | 185 | 2.1242 | - | - | - | +| 0.1831 | 186 | 1.9822 | - | - | - | +| 0.1841 | 187 | 2.3605 | - | - | - | +| 0.1850 | 188 | 1.5251 | - | - | - | +| 0.1860 | 189 | 1.2351 | - | - | - | +| 0.1870 | 190 | 1.5859 | - | - | - | +| 0.1880 | 191 | 1.8056 | - | - | - | +| 0.1890 | 192 | 1.349 | - | - | - | +| 0.1900 | 193 | 0.893 | - | - | - | +| 0.1909 | 194 | 1.5122 | - | - | - | +| 0.1919 | 195 | 1.3875 | - | - | - | +| 0.1929 | 196 | 1.29 | - | - | - | +| 0.1939 | 197 | 2.2931 | - | - | - | +| 0.1949 | 198 | 1.2663 | - | - | - | +| 0.1959 | 199 | 1.9712 | - | - | - | +| 0.1969 | 200 | 2.3307 | - | - | - | +| 0.1978 | 201 | 1.6544 | - | - | - | +| 0.1988 | 202 | 1.638 | - | - | - | +| 0.1998 | 203 | 1.3412 | - | - | - | +| 0.2008 | 204 | 1.4454 | - | - | - | +| 0.2018 | 205 | 1.5437 | - | - | - | +| 0.2028 | 206 | 1.4921 | - | - | - | +| 0.2037 | 207 | 1.4298 | - | - | - | +| 0.2047 | 208 | 1.6174 | - | - | - | +| 0.2057 | 209 | 1.4137 | - | - | - | +| 0.2067 | 210 | 1.5652 | - | - | - | +| 0.2077 | 211 | 1.1631 | - | - | - | +| 0.2087 | 212 | 1.2351 | - | - | - | +| 0.2096 | 213 | 1.7537 | - | - | - | +| 0.2106 | 214 | 1.3186 | - | - | - | +| 0.2116 | 215 | 1.2258 | - | - | - | +| 0.2126 | 216 | 0.7695 | - | - | - | +| 0.2136 | 217 | 1.2775 | - | - | - | +| 0.2146 | 218 | 1.6795 | - | - | - | +| 0.2156 | 219 | 1.2862 | - | - | - | +| 0.2165 | 220 | 1.1723 | - | - | - | +| 0.2175 | 221 | 1.3322 | - | - | - | +| 0.2185 | 222 | 1.7564 | - | - | - | +| 0.2195 | 223 | 1.1071 | - | - | - | +| 0.2205 | 224 | 1.2011 | - | - | - | +| 0.2215 | 225 | 1.2303 | - | - | - | +| 0.2224 | 226 | 1.212 | - | - | - | +| 0.2234 | 227 | 1.0117 | - | - | - | +| 0.2244 | 228 | 1.1907 | - | - | - | +| 0.2254 | 229 | 2.1293 | - | - | - | +| 0.2264 | 230 | 1.3063 | - | - | - | +| 0.2274 | 231 | 1.2841 | - | - | - | +| 0.2283 | 232 | 1.3778 | - | - | - | +| 0.2293 | 233 | 1.2242 | - | - | - | +| 0.2303 | 234 | 0.9227 | - | - | - | +| 0.2313 | 235 | 1.2221 | - | - | - | +| 0.2323 | 236 | 2.1041 | - | - | - | +| 0.2333 | 237 | 1.3341 | - | - | - | +| 0.2343 | 238 | 1.0876 | - | - | - | +| 0.2352 | 239 | 1.3328 | - | - | - | +| 0.2362 | 240 | 1.2958 | - | - | - | +| 0.2372 | 241 | 1.1522 | - | - | - | +| 0.2382 | 242 | 1.7942 | - | - | - | +| 0.2392 | 243 | 1.1325 | - | - | - | +| 0.2402 | 244 | 1.6466 | - | - | - | +| 0.2411 | 245 | 1.4608 | - | - | - | +| 0.2421 | 246 | 0.6375 | - | - | - | +| 0.2431 | 247 | 2.0177 | - | - | - | +| 0.2441 | 248 | 1.2069 | - | - | - | +| 0.2451 | 249 | 0.7639 | - | - | - | +| 0.2461 | 250 | 1.3465 | - | - | - | +| 0.2470 | 251 | 1.064 | - | - | - | +| 0.2480 | 252 | 1.3757 | - | - | - | +| 0.2490 | 253 | 1.612 | - | - | - | +| 0.25 | 254 | 0.7917 | - | - | - | +| 0.2510 | 255 | 1.5515 | - | - | - | +| 0.2520 | 256 | 0.799 | - | - | - | +| 0.2530 | 257 | 0.9882 | - | - | - | +| 0.2539 | 258 | 1.1814 | - | - | - | +| 0.2549 | 259 | 0.6394 | - | - | - | +| 0.2559 | 260 | 1.4756 | - | - | - | +| 0.2569 | 261 | 0.5338 | - | - | - | +| 0.2579 | 262 | 0.9779 | - | - | - | +| 0.2589 | 263 | 1.5307 | - | - | - | +| 0.2598 | 264 | 1.1213 | - | - | - | +| 0.2608 | 265 | 0.9482 | - | - | - | +| 0.2618 | 266 | 0.9599 | - | - | - | +| 0.2628 | 267 | 1.4455 | - | - | - | +| 0.2638 | 268 | 1.6496 | - | - | - | +| 0.2648 | 269 | 0.7402 | - | - | - | +| 0.2657 | 270 | 0.7835 | - | - | - | +| 0.2667 | 271 | 0.7821 | - | - | - | +| 0.2677 | 272 | 1.5422 | - | - | - | +| 0.2687 | 273 | 1.0995 | - | - | - | +| 0.2697 | 274 | 1.378 | - | - | - | +| 0.2707 | 275 | 1.3562 | - | - | - | +| 0.2717 | 276 | 0.7376 | - | - | - | +| 0.2726 | 277 | 1.1678 | - | - | - | +| 0.2736 | 278 | 1.2989 | - | - | - | +| 0.2746 | 279 | 1.9559 | - | - | - | +| 0.2756 | 280 | 1.1237 | - | - | - | +| 0.2766 | 281 | 0.952 | - | - | - | +| 0.2776 | 282 | 1.6629 | - | - | - | +| 0.2785 | 283 | 1.871 | - | - | - | +| 0.2795 | 284 | 1.5946 | - | - | - | +| 0.2805 | 285 | 1.4456 | - | - | - | +| 0.2815 | 286 | 1.4085 | - | - | - | +| 0.2825 | 287 | 1.1394 | - | - | - | +| 0.2835 | 288 | 1.0315 | - | - | - | +| 0.2844 | 289 | 1.488 | - | - | - | +| 0.2854 | 290 | 1.4006 | - | - | - | +| 0.2864 | 291 | 0.9237 | - | - | - | +| 0.2874 | 292 | 1.163 | - | - | - | +| 0.2884 | 293 | 1.7037 | - | - | - | +| 0.2894 | 294 | 0.8715 | - | - | - | +| 0.2904 | 295 | 1.2101 | - | - | - | +| 0.2913 | 296 | 1.1179 | - | - | - | +| 0.2923 | 297 | 1.3986 | - | - | - | +| 0.2933 | 298 | 1.7068 | - | - | - | +| 0.2943 | 299 | 0.8695 | - | - | - | +| 0.2953 | 300 | 1.3778 | - | - | - | +| 0.2963 | 301 | 1.2834 | - | - | - | +| 0.2972 | 302 | 0.8123 | - | - | - | +| 0.2982 | 303 | 1.6521 | - | - | - | +| 0.2992 | 304 | 1.1064 | - | - | - | +| 0.3002 | 305 | 0.9578 | - | - | - | +| 0.3012 | 306 | 0.9254 | 0.4888 | 0.4789 | 0.7040 | +| 0.3022 | 307 | 0.7541 | - | - | - | +| 0.3031 | 308 | 0.7324 | - | - | - | +| 0.3041 | 309 | 0.5974 | - | - | - | +| 0.3051 | 310 | 1.1481 | - | - | - | +| 0.3061 | 311 | 1.6179 | - | - | - | +| 0.3071 | 312 | 1.4641 | - | - | - | +| 0.3081 | 313 | 1.7185 | - | - | - | +| 0.3091 | 314 | 0.9328 | - | - | - | +| 0.3100 | 315 | 0.742 | - | - | - | +| 0.3110 | 316 | 1.4173 | - | - | - | +| 0.3120 | 317 | 0.7267 | - | - | - | +| 0.3130 | 318 | 0.9494 | - | - | - | +| 0.3140 | 319 | 1.5111 | - | - | - | +| 0.3150 | 320 | 1.6949 | - | - | - | +| 0.3159 | 321 | 1.7562 | - | - | - | +| 0.3169 | 322 | 1.2532 | - | - | - | +| 0.3179 | 323 | 1.1086 | - | - | - | +| 0.3189 | 324 | 0.7377 | - | - | - | +| 0.3199 | 325 | 1.085 | - | - | - | +| 0.3209 | 326 | 0.7767 | - | - | - | +| 0.3219 | 327 | 1.4441 | - | - | - | +| 0.3228 | 328 | 0.8146 | - | - | - | +| 0.3238 | 329 | 0.7403 | - | - | - | +| 0.3248 | 330 | 0.8476 | - | - | - | +| 0.3258 | 331 | 0.7323 | - | - | - | +| 0.3268 | 332 | 1.2241 | - | - | - | +| 0.3278 | 333 | 1.5065 | - | - | - | +| 0.3287 | 334 | 0.5259 | - | - | - | +| 0.3297 | 335 | 1.3103 | - | - | - | +| 0.3307 | 336 | 0.8655 | - | - | - | +| 0.3317 | 337 | 0.7575 | - | - | - | +| 0.3327 | 338 | 1.968 | - | - | - | +| 0.3337 | 339 | 1.317 | - | - | - | +| 0.3346 | 340 | 1.1972 | - | - | - | +| 0.3356 | 341 | 1.6323 | - | - | - | +| 0.3366 | 342 | 1.0469 | - | - | - | +| 0.3376 | 343 | 1.3349 | - | - | - | +| 0.3386 | 344 | 0.9544 | - | - | - | +| 0.3396 | 345 | 1.1894 | - | - | - | +| 0.3406 | 346 | 0.7717 | - | - | - | +| 0.3415 | 347 | 1.2563 | - | - | - | +| 0.3425 | 348 | 1.2437 | - | - | - | +| 0.3435 | 349 | 0.7806 | - | - | - | +| 0.3445 | 350 | 0.8303 | - | - | - | +| 0.3455 | 351 | 1.0926 | - | - | - | +| 0.3465 | 352 | 0.6654 | - | - | - | +| 0.3474 | 353 | 1.1087 | - | - | - | +| 0.3484 | 354 | 1.1525 | - | - | - | +| 0.3494 | 355 | 1.1127 | - | - | - | +| 0.3504 | 356 | 1.4267 | - | - | - | +| 0.3514 | 357 | 0.6148 | - | - | - | +| 0.3524 | 358 | 1.0123 | - | - | - | +| 0.3533 | 359 | 1.9682 | - | - | - | +| 0.3543 | 360 | 0.8487 | - | - | - | +| 0.3553 | 361 | 1.0412 | - | - | - | +| 0.3563 | 362 | 1.0902 | - | - | - | +| 0.3573 | 363 | 0.9606 | - | - | - | +| 0.3583 | 364 | 0.9206 | - | - | - | +| 0.3593 | 365 | 1.4727 | - | - | - | +| 0.3602 | 366 | 0.9379 | - | - | - | +| 0.3612 | 367 | 0.8387 | - | - | - | +| 0.3622 | 368 | 0.9692 | - | - | - | +| 0.3632 | 369 | 1.6298 | - | - | - | +| 0.3642 | 370 | 1.0882 | - | - | - | +| 0.3652 | 371 | 1.1558 | - | - | - | +| 0.3661 | 372 | 0.9546 | - | - | - | +| 0.3671 | 373 | 1.0124 | - | - | - | +| 0.3681 | 374 | 1.3916 | - | - | - | +| 0.3691 | 375 | 0.527 | - | - | - | +| 0.3701 | 376 | 0.6387 | - | - | - | +| 0.3711 | 377 | 1.1445 | - | - | - | +| 0.3720 | 378 | 1.3309 | - | - | - | +| 0.3730 | 379 | 1.5888 | - | - | - | +| 0.3740 | 380 | 1.4422 | - | - | - | +| 0.375 | 381 | 1.7044 | - | - | - | +| 0.3760 | 382 | 0.7913 | - | - | - | +| 0.3770 | 383 | 1.3241 | - | - | - | +| 0.3780 | 384 | 0.6473 | - | - | - | +| 0.3789 | 385 | 1.221 | - | - | - | +| 0.3799 | 386 | 0.7773 | - | - | - | +| 0.3809 | 387 | 1.054 | - | - | - | +| 0.3819 | 388 | 0.9862 | - | - | - | +| 0.3829 | 389 | 0.9684 | - | - | - | +| 0.3839 | 390 | 1.3244 | - | - | - | +| 0.3848 | 391 | 1.1787 | - | - | - | +| 0.3858 | 392 | 1.4698 | - | - | - | +| 0.3868 | 393 | 1.0961 | - | - | - | +| 0.3878 | 394 | 1.1364 | - | - | - | +| 0.3888 | 395 | 0.9368 | - | - | - | +| 0.3898 | 396 | 1.1731 | - | - | - | +| 0.3907 | 397 | 0.8686 | - | - | - | +| 0.3917 | 398 | 0.7481 | - | - | - | +| 0.3927 | 399 | 0.7261 | - | - | - | +| 0.3937 | 400 | 1.2062 | - | - | - | +| 0.3947 | 401 | 0.7462 | - | - | - | +| 0.3957 | 402 | 1.0318 | - | - | - | +| 0.3967 | 403 | 1.105 | - | - | - | +| 0.3976 | 404 | 1.009 | - | - | - | +| 0.3986 | 405 | 0.5941 | - | - | - | +| 0.3996 | 406 | 1.7972 | - | - | - | +| 0.4006 | 407 | 1.0544 | - | - | - | +| 0.4016 | 408 | 1.3912 | - | - | - | +| 0.4026 | 409 | 0.8305 | - | - | - | +| 0.4035 | 410 | 0.8688 | - | - | - | +| 0.4045 | 411 | 1.0069 | - | - | - | +| 0.4055 | 412 | 1.3141 | - | - | - | +| 0.4065 | 413 | 1.1042 | - | - | - | +| 0.4075 | 414 | 1.1011 | - | - | - | +| 0.4085 | 415 | 1.1192 | - | - | - | +| 0.4094 | 416 | 1.5957 | - | - | - | +| 0.4104 | 417 | 1.164 | - | - | - | +| 0.4114 | 418 | 0.6425 | - | - | - | +| 0.4124 | 419 | 0.6068 | - | - | - | +| 0.4134 | 420 | 0.9275 | - | - | - | +| 0.4144 | 421 | 0.8836 | - | - | - | +| 0.4154 | 422 | 1.2115 | - | - | - | +| 0.4163 | 423 | 0.8367 | - | - | - | +| 0.4173 | 424 | 1.0595 | - | - | - | +| 0.4183 | 425 | 0.826 | - | - | - | +| 0.4193 | 426 | 0.707 | - | - | - | +| 0.4203 | 427 | 0.6235 | - | - | - | +| 0.4213 | 428 | 0.7719 | - | - | - | +| 0.4222 | 429 | 1.0862 | - | - | - | +| 0.4232 | 430 | 0.9311 | - | - | - | +| 0.4242 | 431 | 1.2339 | - | - | - | +| 0.4252 | 432 | 0.9891 | - | - | - | +| 0.4262 | 433 | 1.8443 | - | - | - | +| 0.4272 | 434 | 1.1799 | - | - | - | +| 0.4281 | 435 | 0.759 | - | - | - | +| 0.4291 | 436 | 1.1002 | - | - | - | +| 0.4301 | 437 | 0.9141 | - | - | - | +| 0.4311 | 438 | 0.5467 | - | - | - | +| 0.4321 | 439 | 0.7476 | - | - | - | +| 0.4331 | 440 | 1.14 | - | - | - | +| 0.4341 | 441 | 1.1504 | - | - | - | +| 0.4350 | 442 | 1.26 | - | - | - | +| 0.4360 | 443 | 1.0311 | - | - | - | +| 0.4370 | 444 | 1.0646 | - | - | - | +| 0.4380 | 445 | 0.8687 | - | - | - | +| 0.4390 | 446 | 0.6839 | - | - | - | +| 0.4400 | 447 | 1.1376 | - | - | - | +| 0.4409 | 448 | 0.9759 | - | - | - | +| 0.4419 | 449 | 0.7971 | - | - | - | +| 0.4429 | 450 | 0.9708 | - | - | - | +| 0.4439 | 451 | 0.8217 | - | - | - | +| 0.4449 | 452 | 1.3728 | - | - | - | +| 0.4459 | 453 | 0.9119 | - | - | - | +| 0.4469 | 454 | 1.012 | - | - | - | +| 0.4478 | 455 | 1.3738 | - | - | - | +| 0.4488 | 456 | 0.8219 | - | - | - | +| 0.4498 | 457 | 1.2558 | - | - | - | +| 0.4508 | 458 | 0.6247 | - | - | - | +| 0.4518 | 459 | 0.7295 | 0.5410 | 0.4920 | 0.6879 | +| 0.4528 | 460 | 0.8154 | - | - | - | +| 0.4537 | 461 | 1.1392 | - | - | - | +| 0.4547 | 462 | 0.8618 | - | - | - | +| 0.4557 | 463 | 0.9669 | - | - | - | +| 0.4567 | 464 | 0.8804 | - | - | - | +| 0.4577 | 465 | 0.8479 | - | - | - | +| 0.4587 | 466 | 0.6296 | - | - | - | +| 0.4596 | 467 | 0.8449 | - | - | - | +| 0.4606 | 468 | 0.9772 | - | - | - | +| 0.4616 | 469 | 0.6424 | - | - | - | +| 0.4626 | 470 | 0.9169 | - | - | - | +| 0.4636 | 471 | 0.7599 | - | - | - | +| 0.4646 | 472 | 0.8943 | - | - | - | +| 0.4656 | 473 | 0.9475 | - | - | - | +| 0.4665 | 474 | 1.4518 | - | - | - | +| 0.4675 | 475 | 1.274 | - | - | - | +| 0.4685 | 476 | 0.7306 | - | - | - | +| 0.4695 | 477 | 0.9238 | - | - | - | +| 0.4705 | 478 | 0.6593 | - | - | - | +| 0.4715 | 479 | 1.0183 | - | - | - | +| 0.4724 | 480 | 1.2577 | - | - | - | +| 0.4734 | 481 | 0.8738 | - | - | - | +| 0.4744 | 482 | 1.1416 | - | - | - | +| 0.4754 | 483 | 0.7135 | - | - | - | +| 0.4764 | 484 | 1.2587 | - | - | - | +| 0.4774 | 485 | 0.8823 | - | - | - | +| 0.4783 | 486 | 0.8423 | - | - | - | +| 0.4793 | 487 | 0.7704 | - | - | - | +| 0.4803 | 488 | 0.7049 | - | - | - | +| 0.4813 | 489 | 1.1893 | - | - | - | +| 0.4823 | 490 | 1.3985 | - | - | - | +| 0.4833 | 491 | 1.3567 | - | - | - | +| 0.4843 | 492 | 1.2573 | - | - | - | +| 0.4852 | 493 | 0.7671 | - | - | - | +| 0.4862 | 494 | 0.5425 | - | - | - | +| 0.4872 | 495 | 0.9372 | - | - | - | +| 0.4882 | 496 | 0.799 | - | - | - | +| 0.4892 | 497 | 0.9548 | - | - | - | +| 0.4902 | 498 | 1.0855 | - | - | - | +| 0.4911 | 499 | 1.0465 | - | - | - | +| 0.4921 | 500 | 1.1004 | - | - | - | +| 0.4931 | 501 | 0.6392 | - | - | - | +| 0.4941 | 502 | 0.7102 | - | - | - | +| 0.4951 | 503 | 1.3242 | - | - | - | +| 0.4961 | 504 | 0.6861 | - | - | - | +| 0.4970 | 505 | 0.9291 | - | - | - | +| 0.4980 | 506 | 0.8592 | - | - | - | +| 0.4990 | 507 | 0.9462 | - | - | - | +| 0.5 | 508 | 1.0167 | - | - | - | +| 0.5010 | 509 | 1.0118 | - | - | - | +| 0.5020 | 510 | 0.6741 | - | - | - | +| 0.5030 | 511 | 1.4578 | - | - | - | +| 0.5039 | 512 | 1.2959 | - | - | - | +| 0.5049 | 513 | 0.8533 | - | - | - | +| 0.5059 | 514 | 0.6685 | - | - | - | +| 0.5069 | 515 | 1.1556 | - | - | - | +| 0.5079 | 516 | 0.8177 | - | - | - | +| 0.5089 | 517 | 0.6296 | - | - | - | +| 0.5098 | 518 | 0.8407 | - | - | - | +| 0.5108 | 519 | 0.6987 | - | - | - | +| 0.5118 | 520 | 0.9888 | - | - | - | +| 0.5128 | 521 | 0.8938 | - | - | - | +| 0.5138 | 522 | 0.582 | - | - | - | +| 0.5148 | 523 | 0.6596 | - | - | - | +| 0.5157 | 524 | 0.6029 | - | - | - | +| 0.5167 | 525 | 0.9806 | - | - | - | +| 0.5177 | 526 | 0.9463 | - | - | - | +| 0.5187 | 527 | 0.7088 | - | - | - | +| 0.5197 | 528 | 0.7525 | - | - | - | +| 0.5207 | 529 | 0.7625 | - | - | - | +| 0.5217 | 530 | 0.8271 | - | - | - | +| 0.5226 | 531 | 0.6129 | - | - | - | +| 0.5236 | 532 | 1.1563 | - | - | - | +| 0.5246 | 533 | 0.8131 | - | - | - | +| 0.5256 | 534 | 0.5363 | - | - | - | +| 0.5266 | 535 | 0.8819 | - | - | - | +| 0.5276 | 536 | 0.9772 | - | - | - | +| 0.5285 | 537 | 1.2102 | - | - | - | +| 0.5295 | 538 | 1.1234 | - | - | - | +| 0.5305 | 539 | 1.1857 | - | - | - | +| 0.5315 | 540 | 0.7873 | - | - | - | +| 0.5325 | 541 | 0.5034 | - | - | - | +| 0.5335 | 542 | 1.3305 | - | - | - | +| 0.5344 | 543 | 1.1727 | - | - | - | +| 0.5354 | 544 | 1.2825 | - | - | - | +| 0.5364 | 545 | 1.0446 | - | - | - | +| 0.5374 | 546 | 0.9838 | - | - | - | +| 0.5384 | 547 | 1.2194 | - | - | - | +| 0.5394 | 548 | 0.7709 | - | - | - | +| 0.5404 | 549 | 0.748 | - | - | - | +| 0.5413 | 550 | 1.0948 | - | - | - | +| 0.5423 | 551 | 0.915 | - | - | - | +| 0.5433 | 552 | 1.537 | - | - | - | +| 0.5443 | 553 | 0.3239 | - | - | - | +| 0.5453 | 554 | 0.9592 | - | - | - | +| 0.5463 | 555 | 0.7737 | - | - | - | +| 0.5472 | 556 | 0.613 | - | - | - | +| 0.5482 | 557 | 1.3646 | - | - | - | +| 0.5492 | 558 | 0.6659 | - | - | - | +| 0.5502 | 559 | 0.5207 | - | - | - | +| 0.5512 | 560 | 0.9467 | - | - | - | +| 0.5522 | 561 | 0.5692 | - | - | - | +| 0.5531 | 562 | 1.5855 | - | - | - | +| 0.5541 | 563 | 0.8855 | - | - | - | +| 0.5551 | 564 | 1.1829 | - | - | - | +| 0.5561 | 565 | 0.978 | - | - | - | +| 0.5571 | 566 | 1.1818 | - | - | - | +| 0.5581 | 567 | 0.701 | - | - | - | +| 0.5591 | 568 | 1.0226 | - | - | - | +| 0.5600 | 569 | 0.5937 | - | - | - | +| 0.5610 | 570 | 0.8095 | - | - | - | +| 0.5620 | 571 | 1.174 | - | - | - | +| 0.5630 | 572 | 0.96 | - | - | - | +| 0.5640 | 573 | 0.8339 | - | - | - | +| 0.5650 | 574 | 0.717 | - | - | - | +| 0.5659 | 575 | 0.5938 | - | - | - | +| 0.5669 | 576 | 0.6501 | - | - | - | +| 0.5679 | 577 | 0.7003 | - | - | - | +| 0.5689 | 578 | 0.5525 | - | - | - | +| 0.5699 | 579 | 0.7003 | - | - | - | +| 0.5709 | 580 | 1.059 | - | - | - | +| 0.5719 | 581 | 0.8625 | - | - | - | +| 0.5728 | 582 | 0.5862 | - | - | - | +| 0.5738 | 583 | 0.9162 | - | - | - | +| 0.5748 | 584 | 0.926 | - | - | - | +| 0.5758 | 585 | 1.2729 | - | - | - | +| 0.5768 | 586 | 0.8935 | - | - | - | +| 0.5778 | 587 | 0.541 | - | - | - | +| 0.5787 | 588 | 1.1455 | - | - | - | +| 0.5797 | 589 | 0.7306 | - | - | - | +| 0.5807 | 590 | 0.9088 | - | - | - | +| 0.5817 | 591 | 0.9166 | - | - | - | +| 0.5827 | 592 | 0.8679 | - | - | - | +| 0.5837 | 593 | 0.9329 | - | - | - | +| 0.5846 | 594 | 1.1201 | - | - | - | +| 0.5856 | 595 | 0.6418 | - | - | - | +| 0.5866 | 596 | 1.145 | - | - | - | +| 0.5876 | 597 | 1.4041 | - | - | - | +| 0.5886 | 598 | 0.6954 | - | - | - | +| 0.5896 | 599 | 0.4567 | - | - | - | +| 0.5906 | 600 | 1.1305 | - | - | - | +| 0.5915 | 601 | 0.8077 | - | - | - | +| 0.5925 | 602 | 0.6143 | - | - | - | +| 0.5935 | 603 | 1.3139 | - | - | - | +| 0.5945 | 604 | 0.7694 | - | - | - | +| 0.5955 | 605 | 0.9622 | - | - | - | +| 0.5965 | 606 | 0.91 | - | - | - | +| 0.5974 | 607 | 1.3125 | - | - | - | +| 0.5984 | 608 | 1.0153 | - | - | - | +| 0.5994 | 609 | 0.8468 | - | - | - | +| 0.6004 | 610 | 1.1026 | - | - | - | +| 0.6014 | 611 | 0.8291 | - | - | - | +| 0.6024 | 612 | 0.7235 | 0.5680 | 0.5175 | 0.6900 | +| 0.6033 | 613 | 0.9613 | - | - | - | +| 0.6043 | 614 | 0.7124 | - | - | - | +| 0.6053 | 615 | 1.0719 | - | - | - | +| 0.6063 | 616 | 0.7233 | - | - | - | +| 0.6073 | 617 | 1.6863 | - | - | - | +| 0.6083 | 618 | 0.8665 | - | - | - | +| 0.6093 | 619 | 1.6432 | - | - | - | +| 0.6102 | 620 | 0.771 | - | - | - | +| 0.6112 | 621 | 0.6755 | - | - | - | +| 0.6122 | 622 | 0.6809 | - 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| - | +| 1.5433 | 1568 | 0.8785 | - | - | - | +| 1.5443 | 1569 | 0.2573 | - | - | - | +| 1.5453 | 1570 | 0.6737 | - | - | - | +| 1.5463 | 1571 | 0.3374 | - | - | - | +| 1.5472 | 1572 | 0.2978 | - | - | - | +| 1.5482 | 1573 | 0.6462 | - | - | - | +| 1.5492 | 1574 | 0.1323 | - | - | - | +| 1.5502 | 1575 | 0.2862 | - | - | - | +| 1.5512 | 1576 | 0.4854 | - | - | - | +| 1.5522 | 1577 | 0.4033 | - | - | - | +| 1.5531 | 1578 | 0.7233 | - | - | - | +| 1.5541 | 1579 | 0.5184 | - | - | - | +| 1.5551 | 1580 | 0.9382 | - | - | - | +| 1.5561 | 1581 | 0.6416 | - | - | - | +| 1.5571 | 1582 | 0.6853 | - | - | - | +| 1.5581 | 1583 | 0.604 | - | - | - | +| 1.5591 | 1584 | 0.498 | - | - | - | +| 1.5600 | 1585 | 0.3023 | - | - | - | +| 1.5610 | 1586 | 0.2496 | - | - | - | +| 1.5620 | 1587 | 0.8291 | - | - | - | +| 1.5630 | 1588 | 0.4926 | - | - | - | +| 1.5640 | 1589 | 0.6343 | - | - | - | +| 1.5650 | 1590 | 0.569 | - | - | - | +| 1.5659 | 1591 | 0.21 | - | - | - | +| 1.5669 | 1592 | 0.3462 | - | - | - | +| 1.5679 | 1593 | 0.6391 | - | - | - | +| 1.5689 | 1594 | 0.2757 | - | - | - | +| 1.5699 | 1595 | 0.4453 | - | - | - | +| 1.5709 | 1596 | 0.3759 | - | - | - | +| 1.5719 | 1597 | 0.5071 | - | - | - | +| 1.5728 | 1598 | 0.1958 | - | - | - | +| 1.5738 | 1599 | 0.3417 | - | - | - | +| 1.5748 | 1600 | 0.4496 | - | - | - | +| 1.5758 | 1601 | 0.5615 | - | - | - | +| 1.5768 | 1602 | 0.4346 | - | - | - | +| 1.5778 | 1603 | 0.3385 | - | - | - | +| 1.5787 | 1604 | 0.6684 | - | - | - | +| 1.5797 | 1605 | 0.3634 | - | - | - | +| 1.5807 | 1606 | 0.6762 | - | - | - | +| 1.5817 | 1607 | 0.3477 | - | - | - | +| 1.5827 | 1608 | 0.5679 | - | - | - | +| 1.5837 | 1609 | 0.7458 | - | - | - | +| 1.5846 | 1610 | 0.7871 | - | - | - | +| 1.5856 | 1611 | 0.3641 | - | - | - | +| 1.5866 | 1612 | 0.6641 | - | - | - | +| 1.5876 | 1613 | 1.1934 | - | - | - | +| 1.5886 | 1614 | 0.3024 | - | - | - | +| 1.5896 | 1615 | 0.3846 | - | - | - | +| 1.5906 | 1616 | 0.6288 | - | - | - | +| 1.5915 | 1617 | 0.4733 | - | - | - | +| 1.5925 | 1618 | 0.2775 | - | - | - | +| 1.5935 | 1619 | 0.6975 | - | - | - | +| 1.5945 | 1620 | 0.317 | - | - | - | +| 1.5955 | 1621 | 0.5374 | - | - | - | +| 1.5965 | 1622 | 0.8305 | - | - | - | +| 1.5974 | 1623 | 0.56 | - | - | - | +| 1.5984 | 1624 | 0.5452 | - | - | - | +| 1.5994 | 1625 | 0.602 | - | - | - | +| 1.6004 | 1626 | 0.7525 | - | - | - | +| 1.6014 | 1627 | 0.3503 | - | - | - | +| 1.6024 | 1628 | 0.3675 | - | - | - | +| 1.6033 | 1629 | 0.7333 | - | - | - | +| 1.6043 | 1630 | 0.4482 | - | - | - | +| 1.6053 | 1631 | 0.315 | - | - | - | +| 1.6063 | 1632 | 0.2173 | - | - | - | +| 1.6073 | 1633 | 1.0407 | - | - | - | +| 1.6083 | 1634 | 0.5099 | - | - | - | +| 1.6093 | 1635 | 0.8723 | - | - | - | +| 1.6102 | 1636 | 0.2828 | - | - | - | +| 1.6112 | 1637 | 0.3407 | - | - | - | +| 1.6122 | 1638 | 0.2632 | - | - | - | +| 1.6132 | 1639 | 0.2201 | - | - | - | +| 1.6142 | 1640 | 0.3298 | - | - | - | +| 1.6152 | 1641 | 0.2171 | - | - | - | +| 1.6161 | 1642 | 0.418 | - | - | - | +| 1.6171 | 1643 | 0.8353 | - | - | - | +| 1.6181 | 1644 | 0.4935 | - | - | - | +| 1.6191 | 1645 | 0.5569 | - | - | - | +| 1.6201 | 1646 | 0.2752 | - | - | - | +| 1.6211 | 1647 | 0.3603 | - | - | - | +| 1.6220 | 1648 | 0.4289 | - | - | - | +| 1.6230 | 1649 | 0.6225 | - | - | - | +| 1.6240 | 1650 | 0.3835 | - | - | - | +| 1.625 | 1651 | 0.1686 | - | - | - | +| 1.6260 | 1652 | 0.5061 | - | - | - | +| 1.6270 | 1653 | 0.2085 | - | - | - | +| 1.6280 | 1654 | 0.3367 | - | - | - | +| 1.6289 | 1655 | 0.5719 | - | - | - | +| 1.6299 | 1656 | 1.073 | - | - | - | +| 1.6309 | 1657 | 0.1355 | - | - | - | +| 1.6319 | 1658 | 0.4165 | - | - | - | +| 1.6329 | 1659 | 0.831 | - | - | - | +| 1.6339 | 1660 | 0.4592 | - | - | - | +| 1.6348 | 1661 | 0.4908 | - | - | - | +| 1.6358 | 1662 | 0.7708 | - | - | - | +| 1.6368 | 1663 | 0.5747 | - | - | - | +| 1.6378 | 1664 | 0.2344 | - | - | - | +| 1.6388 | 1665 | 0.4998 | - | - | - | +| 1.6398 | 1666 | 0.1832 | - | - | - | +| 1.6407 | 1667 | 0.4653 | - | - | - | +| 1.6417 | 1668 | 0.2594 | - | - | - | +| 1.6427 | 1669 | 0.4378 | - | - | - | +| 1.6437 | 1670 | 0.3691 | - | - | - | +| 1.6447 | 1671 | 0.2519 | - | - | - | +| 1.6457 | 1672 | 0.5589 | - | - | - | +| 1.6467 | 1673 | 0.2041 | - | - | - | +| 1.6476 | 1674 | 0.3355 | - | - | - | +| 1.6486 | 1675 | 0.6531 | - | - | - | +| 1.6496 | 1676 | 0.2044 | - | - | - | +| 1.6506 | 1677 | 0.4636 | - | - | - | +| 1.6516 | 1678 | 0.4883 | - | - | - | +| 1.6526 | 1679 | 1.1288 | - | - | - | +| 1.6535 | 1680 | 0.5687 | - | - | - | +| 1.6545 | 1681 | 0.6162 | - | - | - | +| 1.6555 | 1682 | 0.2443 | - | - | - | +| 1.6565 | 1683 | 0.9008 | 0.6929 | 0.5262 | 0.7029 | +| 1.6575 | 1684 | 0.344 | - | - | - | +| 1.6585 | 1685 | 0.2586 | - | - | - | +| 1.6594 | 1686 | 0.6468 | - | - | - | +| 1.6604 | 1687 | 0.8279 | - | - | - | +| 1.6614 | 1688 | 0.3112 | - | - | - | +| 1.6624 | 1689 | 0.6875 | - | - | - | +| 1.6634 | 1690 | 0.231 | - | - | - | +| 1.6644 | 1691 | 0.235 | - | - | - | +| 1.6654 | 1692 | 0.347 | - | - | - | +| 1.6663 | 1693 | 0.5593 | - | - | - | +| 1.6673 | 1694 | 0.4908 | - | - | - | +| 1.6683 | 1695 | 0.3652 | - | - | - | +| 1.6693 | 1696 | 0.3771 | - | - | - | +| 1.6703 | 1697 | 0.3547 | - | - | - | +| 1.6713 | 1698 | 0.3422 | - | - | - | +| 1.6722 | 1699 | 0.5911 | - | - | - | +| 1.6732 | 1700 | 0.5544 | - | - | - | +| 1.6742 | 1701 | 0.295 | - | - | - | +| 1.6752 | 1702 | 0.4211 | - | - | - | +| 1.6762 | 1703 | 0.3966 | - | - | - | +| 1.6772 | 1704 | 0.7783 | - | - | - | +| 1.6781 | 1705 | 0.7513 | - | - | - | +| 1.6791 | 1706 | 0.7145 | - | - | - | +| 1.6801 | 1707 | 0.8779 | - | - | - | +| 1.6811 | 1708 | 0.2505 | - | - | - | +| 1.6821 | 1709 | 0.7892 | - | - | - | +| 1.6831 | 1710 | 0.4353 | - | - | - | +| 1.6841 | 1711 | 0.5166 | - | - | - | +| 1.6850 | 1712 | 0.946 | - | - | - | +| 1.6860 | 1713 | 0.3011 | - | - | - | +| 1.6870 | 1714 | 0.7152 | - | - | - | +| 1.6880 | 1715 | 0.1964 | - | - | - | +| 1.6890 | 1716 | 0.4533 | - | - | - | +| 1.6900 | 1717 | 0.2566 | - | - | - | +| 1.6909 | 1718 | 0.5991 | - | - | - | +| 1.6919 | 1719 | 0.8419 | - | - | - | +| 1.6929 | 1720 | 0.3322 | - | - | - | +| 1.6939 | 1721 | 0.9387 | - | - | - | +| 1.6949 | 1722 | 0.6228 | - | - | - | +| 1.6959 | 1723 | 0.4324 | - | - | - | +| 1.6969 | 1724 | 0.1489 | - | - | - | +| 1.6978 | 1725 | 0.6311 | - | - | - | +| 1.6988 | 1726 | 0.8488 | - | - | - | +| 1.6998 | 1727 | 0.2451 | - | - | - | +| 1.7008 | 1728 | 0.6744 | - | - | - | +| 1.7018 | 1729 | 0.3383 | - | - | - | +| 1.7028 | 1730 | 0.3949 | - | - | - | +| 1.7037 | 1731 | 0.4743 | - | - | - | +| 1.7047 | 1732 | 0.3058 | - | - | - | +| 1.7057 | 1733 | 0.5883 | - | - | - | +| 1.7067 | 1734 | 0.8702 | - | - | - | +| 1.7077 | 1735 | 0.3621 | - | - | - | +| 1.7087 | 1736 | 0.7945 | - | - | - | +| 1.7096 | 1737 | 0.1308 | - | - | - | +| 1.7106 | 1738 | 0.2788 | - | - | - | +| 1.7116 | 1739 | 0.7254 | - | - | - | +| 1.7126 | 1740 | 0.2491 | - | - | - | +| 1.7136 | 1741 | 0.4208 | - | - | - | +| 1.7146 | 1742 | 0.4208 | - | - | - | +| 1.7156 | 1743 | 0.4741 | - | - | - | +| 1.7165 | 1744 | 0.4124 | - | - | - | +| 1.7175 | 1745 | 0.5402 | - | - | - | +| 1.7185 | 1746 | 0.4638 | - | - | - | +| 1.7195 | 1747 | 0.2356 | - | - | - | +| 1.7205 | 1748 | 0.4948 | - | - | - | +| 1.7215 | 1749 | 0.3384 | - | - | - | +| 1.7224 | 1750 | 0.2449 | - | - | - | +| 1.7234 | 1751 | 0.6048 | - | - | - | +| 1.7244 | 1752 | 0.3199 | - | - | - | +| 1.7254 | 1753 | 0.4052 | - | - | - | +| 1.7264 | 1754 | 0.2473 | - | - | - | +| 1.7274 | 1755 | 0.6658 | - | - | - | +| 1.7283 | 1756 | 0.4531 | - | - | - | +| 1.7293 | 1757 | 0.6038 | - | - | - | +| 1.7303 | 1758 | 0.6887 | - | - | - | +| 1.7313 | 1759 | 0.4222 | - | - | - | +| 1.7323 | 1760 | 0.068 | - | - | - | +| 1.7333 | 1761 | 0.4014 | - | - | - | +| 1.7343 | 1762 | 0.2613 | - | - | - | +| 1.7352 | 1763 | 0.6287 | - | - | - | +| 1.7362 | 1764 | 0.462 | - | - | - | +| 1.7372 | 1765 | 0.3476 | - | - | - | +| 1.7382 | 1766 | 0.2576 | - | - | - | +| 1.7392 | 1767 | 0.8844 | - | - | - | +| 1.7402 | 1768 | 0.6081 | - | - | - | +| 1.7411 | 1769 | 0.1292 | - | - | - | +| 1.7421 | 1770 | 0.6448 | - | - | - | +| 1.7431 | 1771 | 0.2812 | - | - | - | +| 1.7441 | 1772 | 0.2312 | - | - | - | +| 1.7451 | 1773 | 1.1008 | - | - | - | +| 1.7461 | 1774 | 0.7085 | - | - | - | +| 1.7470 | 1775 | 0.4462 | - | - | - | +| 1.7480 | 1776 | 0.528 | - | - | - | +| 1.7490 | 1777 | 0.7114 | - | - | - | +| 1.75 | 1778 | 0.2463 | - | - | - | +| 1.7510 | 1779 | 0.4123 | - | - | - | +| 1.7520 | 1780 | 0.383 | - | - | - | +| 1.7530 | 1781 | 1.4599 | - | - | - | +| 1.7539 | 1782 | 0.2778 | - | - | - | +| 1.7549 | 1783 | 0.2371 | - | - | - | +| 1.7559 | 1784 | 0.2167 | - | - | - | +| 1.7569 | 1785 | 0.3909 | - | - | - | +| 1.7579 | 1786 | 0.5294 | - | - | - | +| 1.7589 | 1787 | 0.5687 | - | - | - | +| 1.7598 | 1788 | 0.1999 | - | - | - | +| 1.7608 | 1789 | 0.5501 | - | - | - | +| 1.7618 | 1790 | 0.71 | - | - | - | +| 1.7628 | 1791 | 0.3877 | - | - | - | +| 1.7638 | 1792 | 0.4649 | - | - | - | +| 1.7648 | 1793 | 0.719 | - | - | - | +| 1.7657 | 1794 | 0.2317 | - | - | - | +| 1.7667 | 1795 | 0.7701 | - | - | - | +| 1.7677 | 1796 | 0.1972 | - | - | - | +| 1.7687 | 1797 | 0.0844 | - | - | - | +| 1.7697 | 1798 | 0.2223 | - | - | - | +| 1.7707 | 1799 | 0.6739 | - | - | - | +| 1.7717 | 1800 | 0.4762 | - | - | - | +| 1.7726 | 1801 | 0.4405 | - | - | - | +| 1.7736 | 1802 | 0.7204 | - | - | - | +| 1.7746 | 1803 | 0.5104 | - | - | - | +| 1.7756 | 1804 | 0.2602 | - | - | - | +| 1.7766 | 1805 | 0.3529 | - | - | - | +| 1.7776 | 1806 | 0.3906 | - | - | - | +| 1.7785 | 1807 | 0.4859 | - | - | - | +| 1.7795 | 1808 | 0.5454 | - | - | - | +| 1.7805 | 1809 | 0.4257 | - | - | - | +| 1.7815 | 1810 | 0.4074 | - | - | - | +| 1.7825 | 1811 | 0.6397 | - | - | - | +| 1.7835 | 1812 | 0.5058 | - | - | - | +| 1.7844 | 1813 | 0.6833 | - | - | - | +| 1.7854 | 1814 | 0.6613 | - | - | - | +| 1.7864 | 1815 | 0.6993 | - | - | - | +| 1.7874 | 1816 | 0.4406 | - | - | - | +| 1.7884 | 1817 | 0.2914 | - | - | - | +| 1.7894 | 1818 | 0.4076 | - | - | - | +| 1.7904 | 1819 | 0.8532 | - | - | - | +| 1.7913 | 1820 | 0.5586 | - | - | - | +| 1.7923 | 1821 | 0.629 | - | - | - | +| 1.7933 | 1822 | 0.5267 | - | - | - | +| 1.7943 | 1823 | 0.4557 | - | - | - | +| 1.7953 | 1824 | 0.2006 | - | - | - | +| 1.7963 | 1825 | 0.3406 | - | - | - | +| 1.7972 | 1826 | 0.1683 | - | - | - | +| 1.7982 | 1827 | 0.2141 | - | - | - | +| 1.7992 | 1828 | 0.0945 | - | - | - | +| 1.8002 | 1829 | 0.4701 | - | - | - | +| 1.8012 | 1830 | 0.4771 | - | - | - | + +
+ +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 3.2.1 +- Transformers: 4.44.2 +- PyTorch: 2.5.0+cu121 +- Accelerate: 0.34.2 +- Datasets: 3.0.2 +- Tokenizers: 0.19.1 + +## 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", +} +``` + +#### 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