Add new SentenceTransformer model.

3b76d79 verified 8 months ago

25.3 kB

	---
	base_model: google/electra-large-discriminator
	datasets:
	- PiC/phrase_similarity
	language:
	- en
	library_name: sentence-transformers
	metrics:
	- 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:7004
	- loss:SoftmaxLoss
	widget:
	- source_sentence: Google SEO expert Matt Cutts had a similar experience, of the eight
	magazines and newspapers Cutts tried to order, he received zero.
	sentences:
	- He dissolved the services of her guards and her court attendants and seized an
	expansive reach of properties belonging to her.
	- Google SEO expert Matt Cutts had a comparable occurrence, of the eight magazines
	and newspapers Cutts tried to order, he received zero.
	- bill's newest solo play, "all over the map", premiered off broadway in april 2016,
	produced by all for an individual cinema.
	- source_sentence: Shula said that Namath "beat our blitz" with his fast release,
	which let him quickly dump the football off to a receiver.
	sentences:
	- Shula said that Namath "beat our blitz" with his quick throw, which let him quickly
	dump the football off to a receiver.
	- it elects a single component of parliament (mp) by the first past the post system
	of election.
	- Matt Groening said that West was one of the most widely known group to ever come
	to the studio.
	- source_sentence: When Angel calls out her name, Cordelia suddenly appears from the
	opposite side of the room saying, "Yep, that chick's in rough shape.
	sentences:
	- The ruined row of text, part of the Florida East Coast Railway, was repaired by
	2014 renewing freight train access to the port.
	- When Angel calls out her name, Cordelia suddenly appears from the opposite side
	of the room saying, "Yep, that chick's in approximate form.
	- Chaplin's films introduced a moderated kind of comedy than the typical Keystone
	farce, and he developed a large fan base.
	- source_sentence: The following table shows the distances traversed by National Route
	11 in each different department, showing cities and towns that it passes by (or
	near).
	sentences:
	- The following table shows the distances traversed by National Route 11 in each
	separate city authority, showing cities and towns that it passes by (or near).
	- Similarly, indigenous communities and leaders practice as the main rule of law
	on local native lands and reserves.
	- later, sylvan mixed gary numan's albums "replicas" (with numan's previous band
	tubeway army) and "the quest for instant gratification".
	- source_sentence: She wants to write about Keima but suffers a major case of writer's
	block.
	sentences:
	- In some countries, new extremist parties on the extreme opposite of left of the
	political spectrum arose, motivated through issues of immigration, multiculturalism
	and integration.
	- specific medical status of movement and the general condition of movement both
	are conditions under which contradictions can move.
	- She wants to write about Keima but suffers a huge occurrence of writer's block.
	model-index:
	- name: SentenceTransformer based on google/electra-large-discriminator
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: quora duplicates dev
	type: quora-duplicates-dev
	metrics:
	- type: cosine_accuracy
	value: 0.748
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.9737387895584106
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.7604846225535881
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.9574624300003052
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.7120418848167539
	name: Cosine Precision
	- type: cosine_recall
	value: 0.816
	name: Cosine Recall
	- type: cosine_ap
	value: 0.786909093121924
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.667
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 275.4551696777344
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.733229329173167
	name: Dot F1
	- type: dot_f1_threshold
	value: 266.14727783203125
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.6010230179028133
	name: Dot Precision
	- type: dot_recall
	value: 0.94
	name: Dot Recall
	- type: dot_ap
	value: 0.5935392159238977
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.746
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 87.73857116699219
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.7614678899082568
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 131.43374633789062
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.7033898305084746
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.83
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.7904964653279406
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.747
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 4.5833892822265625
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.7610121836925962
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 5.5540361404418945
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.7160493827160493
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.812
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.789806008641207
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.748
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 275.4551696777344
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.7614678899082568
	name: Max F1
	- type: max_f1_threshold
	value: 266.14727783203125
	name: Max F1 Threshold
	- type: max_precision
	value: 0.7160493827160493
	name: Max Precision
	- type: max_recall
	value: 0.94
	name: Max Recall
	- type: max_ap
	value: 0.7904964653279406
	name: Max Ap
	---

	# SentenceTransformer based on google/electra-large-discriminator

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) on the [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) dataset. 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: [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) <!-- at revision c13c3df7efadc2162f42588bd28eb4e187d602a5 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity)
	- Language: en
	<!-- - 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: ElectraModel
	(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("Deehan1866/Electra")
	# Run inference
	sentences = [
	"She wants to write about Keima but suffers a major case of writer's block.",
	"She wants to write about Keima but suffers a huge occurrence of writer's block.",
	'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
	]
	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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	## Evaluation

	### Metrics

	#### Binary Classification
	* Dataset: `quora-duplicates-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.748 \|
	\| cosine_accuracy_threshold \| 0.9737 \|
	\| cosine_f1 \| 0.7605 \|
	\| cosine_f1_threshold \| 0.9575 \|
	\| cosine_precision \| 0.712 \|
	\| cosine_recall \| 0.816 \|
	\| cosine_ap \| 0.7869 \|
	\| dot_accuracy \| 0.667 \|
	\| dot_accuracy_threshold \| 275.4552 \|
	\| dot_f1 \| 0.7332 \|
	\| dot_f1_threshold \| 266.1473 \|
	\| dot_precision \| 0.601 \|
	\| dot_recall \| 0.94 \|
	\| dot_ap \| 0.5935 \|
	\| manhattan_accuracy \| 0.746 \|
	\| manhattan_accuracy_threshold \| 87.7386 \|
	\| manhattan_f1 \| 0.7615 \|
	\| manhattan_f1_threshold \| 131.4337 \|
	\| manhattan_precision \| 0.7034 \|
	\| manhattan_recall \| 0.83 \|
	\| manhattan_ap \| 0.7905 \|
	\| euclidean_accuracy \| 0.747 \|
	\| euclidean_accuracy_threshold \| 4.5834 \|
	\| euclidean_f1 \| 0.761 \|
	\| euclidean_f1_threshold \| 5.554 \|
	\| euclidean_precision \| 0.716 \|
	\| euclidean_recall \| 0.812 \|
	\| euclidean_ap \| 0.7898 \|
	\| max_accuracy \| 0.748 \|
	\| max_accuracy_threshold \| 275.4552 \|
	\| max_f1 \| 0.7615 \|
	\| max_f1_threshold \| 266.1473 \|
	\| max_precision \| 0.716 \|
	\| max_recall \| 0.94 \|
	\| max_ap \| 0.7905 \|

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	## Training Details

	### Training Dataset

	#### PiC/phrase_similarity

	* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
	* Size: 7,004 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 12 tokens</li><li>mean: 26.35 tokens</li><li>max: 57 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 26.89 tokens</li><li>max: 58 tokens</li></ul> \| <ul><li>0: ~48.80%</li><li>1: ~51.20%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> \| <code>recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.</code> \| <code>0</code> \|
	\| <code>According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> \| <code>According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.</code> \| <code>1</code> \|
	\| <code>Note that Fact 1 does not assume any particular structure on the set formula_65.</code> \| <code>Note that Fact 1 does not assume any specific edifice on the set formula_65.</code> \| <code>0</code> \|
	* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

	### Evaluation Dataset

	#### PiC/phrase_similarity

	* Dataset: [PiC/phrase_similarity](https://huggingface.co/datasets/PiC/phrase_similarity) at [fc67ce7](https://huggingface.co/datasets/PiC/phrase_similarity/tree/fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d)
	* Size: 1,000 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 26.21 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 26.8 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:----------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.</code> \| <code>after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.</code> \| <code>0</code> \|
	\| <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.</code> \| <code>The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.</code> \| <code>0</code> \|
	\| <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.</code> \| <code>Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.</code> \| <code>0</code> \|
	* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `load_best_model_at_end`: True

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `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`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: 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`: True
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `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
	- `eval_on_start`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| quora-duplicates-dev_max_ap \|
	\|:----------:\|:-------:\|:-------------:\|:----------:\|:---------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.6721 \|
	\| 0.2283 \| 100 \| - \| 0.6805 \| 0.6847 \|
	\| 0.4566 \| 200 \| - \| 0.5313 \| 0.7905 \|
	\| 0.6849 \| 300 \| - \| 0.5383 \| 0.7838 \|
	\| 0.9132 \| 400 \| - \| 0.6442 \| 0.7585 \|
	\| 1.1416 \| 500 \| 0.5761 \| 0.5742 \| 0.7843 \|
	\| 1.3699 \| 600 \| - \| 0.5606 \| 0.7558 \|
	\| 1.5982 \| 700 \| - \| 0.5716 \| 0.7772 \|
	\| 1.8265 \| 800 \| - \| 0.5573 \| 0.7619 \|
	\| 2.0548 \| 900 \| - \| 0.6951 \| 0.7760 \|
	\| 2.2831 \| 1000 \| 0.3712 \| 0.7678 \| 0.7753 \|
	\| 2.5114 \| 1100 \| - \| 0.7712 \| 0.7915 \|
	\| 2.7397 \| 1200 \| - \| 0.8120 \| 0.7914 \|
	\| 2.9680 \| 1300 \| - \| 0.8045 \| 0.7789 \|
	\| 3.1963 \| 1400 \| - \| 0.9936 \| 0.7821 \|
	\| 3.4247 \| 1500 \| 0.1942 \| 1.0883 \| 0.7679 \|
	\| 3.6530 \| 1600 \| - \| 0.9814 \| 0.7566 \|
	\| 3.8813 \| 1700 \| - \| 1.0897 \| 0.7830 \|
	\| 4.1096 \| 1800 \| - \| 1.0764 \| 0.7729 \|
	\| 4.3379 \| 1900 \| - \| 1.1209 \| 0.7802 \|
	\| 4.5662 \| 2000 \| 0.1175 \| 1.1522 \| 0.7804 \|
	\| 4.7945 \| 2100 \| - \| 1.1545 \| 0.7807 \|
	\| 5.0 \| 2190 \| - \| - \| 0.7905 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.10
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.3
	- PyTorch: 2.2.1+cu121
	- Accelerate: 0.32.1
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers and SoftmaxLoss
	```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",
	}
	```

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