eval_triple_encoder / README.md

abhiraj1

Add new SentenceTransformer model.

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	---
	base_model: UKPLab/triple-encoders-dailydialog
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:985575
	- loss:CosineSimilarityTripleEncoderLoss
	- loss:ContrastiveLoss
	widget:
	- source_sentence: A small white and tan dog licking up peanut butter.
	sentences:
	- Someone is making dinner in the kitchen.
	- Someone put peanut butter on the dog's nose because that's always good for a laugh.
	- Two dogs are eating food from a bowl in a kitchen
	- source_sentence: A person in a heavy coat shoveling snow.
	sentences:
	- Someone is holding a rocket launcher.
	- An old person is shoveling snow.
	- The private bar's pro bono work was supported by the judges.
	- source_sentence: '[B1] [O] [BEFORE] '
	sentences:
	- '[B2] [E] [BEFORE] '
	- '[B2] [O] [BEFORE] e'
	- '[AFTER] u'
	- source_sentence: '[B1] [E] [BEFORE] e'
	sentences:
	- '[B2] [O] [BEFORE] :'
	- '[B2] [O] [BEFORE] t'
	- '[AFTER] C'
	- source_sentence: '[B1] [O] [BEFORE] s'
	sentences:
	- '[B2] [O] [BEFORE] o'
	- '[B2] [E] [BEFORE] '
	- '[AFTER] u'
	---

	# SentenceTransformer based on UKPLab/triple-encoders-dailydialog

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog). It maps sentences & paragraphs to a 1024-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: [UKPLab/triple-encoders-dailydialog](https://huggingface.co/UKPLab/triple-encoders-dailydialog) <!-- at revision 390bfe14e21b0eb89068887d1032afcb4f2a1b27 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### 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: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```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("abhiraj1/eval_triple_encoder")
	# Run inference
	sentences = [
	'[B1] [O] [BEFORE] s',
	'[B2] [E] [BEFORE] ',
	'[AFTER] u',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

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

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	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
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	### Out-of-Scope Use

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	## Training Details

	### Training Datasets

	#### Unnamed Dataset


	* Size: 43,506 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \| label \|
	\|:--------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 5.86 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 5.84 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.81 tokens</li><li>max: 4 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.2</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \| label \|
	\|:---------------------------------\|:---------------------------------\|:-----------------------\|:--------------------------------\|
	\| <code>[B1] [O] [BEFORE] </code> \| <code>[B2] [E] [BEFORE] </code> \| <code>[AFTER] u</code> \| <code>0.0</code> \|
	\| <code>[B1] [E] [BEFORE] e</code> \| <code>[B2] [O] [BEFORE] :</code> \| <code>[AFTER] C</code> \| <code>0.0</code> \|
	\| <code>[B1] [O] [BEFORE] s</code> \| <code>[B2] [E] [BEFORE] </code> \| <code>[AFTER] u</code> \| <code>0.6000000000000001</code> \|
	* Loss: <code>triple_encoders.losses.CosineSimilarityTripleEncoderLoss.CosineSimilarityTripleEncoderLoss</code>

	#### Unnamed Dataset


	* Size: 942,069 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 20.26 tokens</li><li>max: 182 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 11.94 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>0: ~32.40%</li><li>1: ~33.70%</li><li>2: ~33.90%</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:---------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------\|
	\| <code>And the reason Lincoln and his goons had shown up? Well, not everybody was full of respect.</code> \| <code>Lincoln didn't show up.</code> \| <code>0</code> \|
	\| <code>a rally car driving down a roadway with people on the side taking pictures</code> \| <code>People on the side of road taking picture of a rally car driving down</code> \| <code>1</code> \|
	\| <code>The dog is wearing a purple cape.</code> \| <code>THE ANIMAL IS IN A PAGEANT</code> \| <code>2</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_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
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: 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`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.0919 \| 500 \| 0.0838 \|
	\| 0.1838 \| 1000 \| 0.0474 \|
	\| 0.2757 \| 1500 \| 0.0414 \|
	\| 0.3676 \| 2000 \| 0.0417 \|
	\| 0.4596 \| 2500 \| 0.042 \|
	\| 0.5515 \| 3000 \| 0.0423 \|
	\| 0.6434 \| 3500 \| 0.0408 \|
	\| 0.7353 \| 4000 \| 0.0427 \|
	\| 0.8272 \| 4500 \| 0.0414 \|
	\| 0.9191 \| 5000 \| 0.0415 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.32.1
	- Datasets: 2.20.0
	- 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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