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--- |
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base_model: google-bert/bert-base-uncased |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:100000 |
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- loss:DenoisingAutoEncoderLoss |
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widget: |
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- source_sentence: 1109/icnsurv |
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sentences: |
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- 1109/icnsurv |
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- A cost function is needed to assign a performance metric value to a particular |
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test run |
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- Aircraft OperationsFuture aircraft will sense, control, communicate, and navigate |
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with increasing levels of autonomy, enabling new concepts in air traffic management |
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- source_sentence: Table 1 of and to well as the median taxi from STBO KDFW |
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sentences: |
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- Table 1 Metrics of accuracy, median and MAD of residuals as compared to STBO predictions, |
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as well as the median taxi time from STBO for KDFW and KCLT airports |
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- ', IEEE, 2005, pp' |
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- 'RESULTS: EFFICIENCY ANALYSIS' |
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- source_sentence: gate time to known |
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sentences: |
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- 3FIVE INPUT VARIABLESParameterDescriptionHead windHead WindGust windGust WindCeiling_ftForecast |
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CeilingVis_ftForecast VisibilityAct_Land_Wgt Actual Landing Weightfive parameters |
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listed in |
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- Instead, gate departure time was assumed to be known |
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- The proof is very similar to that presented for the NP-completeness of ASP, and |
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is based on reduction from PLANAR-P3( 6), hence we simply provide the main idea |
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of the proof |
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- source_sentence: ', Hough" Pattern Recognition, Vol' |
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sentences: |
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- 9 Station Keeping scores |
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- "\t\tAGARD CD-410" |
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- ', "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, |
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Vol' |
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- source_sentence: Airlines often ferry from locations fuel prices |
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sentences: |
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- Scheduler Inputs and Order of ConsiderationThe surface model provides EOBT, UOBT, |
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UTOT and other detailed flight-specific modeled input |
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- "\t\t\tKeithWichman" |
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- Airlines often ferry fuel from locations where fuel prices are cheapest |
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--- |
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# SentenceTransformer based on google-bert/bert-base-uncased |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). 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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("kathleenge/tsdae-bert-base-uncased") |
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# Run inference |
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sentences = [ |
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'Airlines often ferry from locations fuel prices', |
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'Airlines often ferry fuel from locations where fuel prices are cheapest', |
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'\t\t\tKeithWichman', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 100,000 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 10.95 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.39 tokens</li><li>max: 239 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| |
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| <code>selected and reviewed for value current on metroplex</code> | <code>The literature was selected and reviewed for its value to the current research on metroplex operations</code> | |
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| <code>and</code> | <code>, and Dulchinos, V</code> | |
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| <code>,</code> | <code>, Atkins, S</code> | |
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* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `num_train_epochs`: 1 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 8 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:-----:|:-----:|:-------------:| |
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| 0.04 | 500 | 7.3777 | |
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| 0.08 | 1000 | 6.9771 | |
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| 0.12 | 1500 | 6.8481 | |
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| 0.16 | 2000 | 6.7737 | |
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| 0.2 | 2500 | 6.6935 | |
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| 0.24 | 3000 | 6.6264 | |
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| 0.28 | 3500 | 6.5918 | |
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| 0.32 | 4000 | 6.5504 | |
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| 0.36 | 4500 | 6.4805 | |
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| 0.4 | 5000 | 6.4539 | |
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| 0.44 | 5500 | 6.4242 | |
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| 0.48 | 6000 | 6.4017 | |
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| 0.52 | 6500 | 6.3808 | |
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| 0.56 | 7000 | 6.3595 | |
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| 0.6 | 7500 | 6.3174 | |
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| 0.64 | 8000 | 6.2911 | |
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| 0.68 | 8500 | 6.2917 | |
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| 0.72 | 9000 | 6.2555 | |
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| 0.76 | 9500 | 6.2314 | |
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| 0.8 | 10000 | 6.2223 | |
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| 0.84 | 10500 | 6.1852 | |
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| 0.88 | 11000 | 6.2067 | |
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| 0.92 | 11500 | 6.1562 | |
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| 0.96 | 12000 | 6.1563 | |
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| 1.0 | 12500 | 6.092 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.3.1+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### DenoisingAutoEncoderLoss |
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```bibtex |
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@inproceedings{wang-2021-TSDAE, |
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title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", |
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author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", |
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month = nov, |
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year = "2021", |
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address = "Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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pages = "671--688", |
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url = "https://arxiv.org/abs/2104.06979", |
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} |
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``` |
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