Add new SentenceTransformer model.

545552b verified 4 months ago

23 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:43371
	- loss:MultipleNegativesRankingLoss
	base_model: sentence-transformers/all-MiniLM-L6-v2
	datasets: []
	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
	widget:
	- source_sentence: ' New Kids on the Block: Step by Step (1990/I) Step closer to
	the New Kids on the Block as they share their newest songs, their hottest performances,
	and their most personal thoughts. Join the guys as they look at where they came
	from, where they are right now, and where they''re headed - step by step.'
	sentences:
	- Rare
	- Rare
	- thriller
	- source_sentence: ' "Vampirism Bites" (2010) Vampire fan girl Belle always dreamed
	of becoming a vampire, and finally got her wish on a blind date. She quickly discovers
	the life of a vampire is not what books, movies and TV have told her, and learns
	that Vampirism is not a 24/7 sexual and romantic fantasy. In fact, Vampirism Bites.'
	sentences:
	- thriller
	- comedy
	- Rare
	- source_sentence: ' O Candidato Vieira (2005) A feature documentary about satirical
	rock star Manuel Joăo Vieira who ran as a candidate for the Presidency of Portugal
	in 2001. Altough he didn''t collect the number of signatures needed to officially
	put him on the ballots, Vieira''s surreal campaign appearances on television talk
	shows, radio and concerts took the country by storm and left everybody laughing.
	A political, comedic and musical documentary!'
	sentences:
	- documentary
	- short
	- short
	- source_sentence: ' Ani DiFranco: Live at Babeville (2008) On September 11 and 12,
	2007, Ani DiFranco and her band (Allison Miller on drums, Todd Sickafoose on bass
	and Mike Dillon on vibes and percussion) played two sold-out shows before a hometown
	audience in Buffalo, New York. What made those nights so special wasn''t just
	the music-that''s always special at an Ani show-but the fact that she was playing
	the inaugural shows in her very own venue, "Babeville". Now the highlights of
	the two shows are available on a single DVD featuring eighteen songs (two of which
	have not yet appeared on studio albums), plus bonus sound check and interview
	footage, all shot in high definition video and 5.1 surround sound. The result
	is a must-have memento of Ani at her finest-onstage, playing her guitar and singing
	with the passion, intensity, and joy that have made her a legend.'
	sentences:
	- drama
	- Rare
	- documentary
	- source_sentence: ' "Oliver Twist" (1985) In a storm, in a workhouse, to a nameless
	woman, young Oliver Twist is born into parish care where he''s overworked and
	underfed. As he grows older his adventures take him from the countryside to London,
	through harsh treatment, kindness, an undertaker, and a thieves'' dens, where
	he makes friends and enemies. But all the time he is pursued by the mysterious
	Monks, who hires Fagin to turn Oliver into a thief. Oliver is rescued by chance
	and kind friends. But it''s a puzzle of legitimacy, inheritance, and identity
	that Oliver''s friends must attempt to unravel before Monks can destroy Oliver.'
	sentences:
	- documentary
	- drama
	- drama
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy
	value: 0.900683492678328
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.601991593837738
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.4642871879513101
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.520057201385498
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.4201015531660693
	name: Cosine Precision
	- type: cosine_recall
	value: 0.5188600940699069
	name: Cosine Recall
	- type: cosine_ap
	value: 0.46368250557502916
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.900683492678328
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.6019916534423828
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.4642871879513101
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.5200573205947876
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.4201015531660693
	name: Dot Precision
	- type: dot_recall
	value: 0.5188600940699069
	name: Dot Recall
	- type: dot_ap
	value: 0.4636826492476884
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.900304343816287
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 13.547416687011719
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.45818772856562373
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 15.149662017822266
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.40953003559235857
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.5199667988564051
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.45787992811626
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.900683492678328
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.8921977281570435
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.4642871879513101
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.979737401008606
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.4201015531660693
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.5188600940699069
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.46368245984449313
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.900683492678328
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 13.547416687011719
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.4642871879513101
	name: Max F1
	- type: max_f1_threshold
	value: 15.149662017822266
	name: Max F1 Threshold
	- type: max_precision
	value: 0.4201015531660693
	name: Max Precision
	- type: max_recall
	value: 0.5199667988564051
	name: Max Recall
	- type: max_ap
	value: 0.4636826492476884
	name: Max Ap
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy
	value: 0.6381767038642442
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.3618232961357558
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.6227289495527069
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.6381767038642442
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.6381767038642442
	name: Max Accuracy
	---

	# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) on the imdb-triplet dataset. It maps sentences & paragraphs to a 384-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: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
	- Maximum Sequence Length: 256 tokens
	- Output Dimensionality: 384 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- imdb-triplet
	<!-- - 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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})
	(2): Normalize()
	)
	```

	## 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("celik-muhammed/all-MiniLM-L6-v2-finetuned-imdb")
	# Run inference
	sentences = [
	' "Oliver Twist" (1985) In a storm, in a workhouse, to a nameless woman, young Oliver Twist is born into parish care where he\'s overworked and underfed. As he grows older his adventures take him from the countryside to London, through harsh treatment, kindness, an undertaker, and a thieves\' dens, where he makes friends and enemies. But all the time he is pursued by the mysterious Monks, who hires Fagin to turn Oliver into a thief. Oliver is rescued by chance and kind friends. But it\'s a puzzle of legitimacy, inheritance, and identity that Oliver\'s friends must attempt to unravel before Monks can destroy Oliver.',
	'drama',
	'documentary',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

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

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

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	## Evaluation

	### Metrics

	#### Binary Classification

	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.9007 \|
	\| cosine_accuracy_threshold \| 0.602 \|
	\| cosine_f1 \| 0.4643 \|
	\| cosine_f1_threshold \| 0.5201 \|
	\| cosine_precision \| 0.4201 \|
	\| cosine_recall \| 0.5189 \|
	\| cosine_ap \| 0.4637 \|
	\| dot_accuracy \| 0.9007 \|
	\| dot_accuracy_threshold \| 0.602 \|
	\| dot_f1 \| 0.4643 \|
	\| dot_f1_threshold \| 0.5201 \|
	\| dot_precision \| 0.4201 \|
	\| dot_recall \| 0.5189 \|
	\| dot_ap \| 0.4637 \|
	\| manhattan_accuracy \| 0.9003 \|
	\| manhattan_accuracy_threshold \| 13.5474 \|
	\| manhattan_f1 \| 0.4582 \|
	\| manhattan_f1_threshold \| 15.1497 \|
	\| manhattan_precision \| 0.4095 \|
	\| manhattan_recall \| 0.52 \|
	\| manhattan_ap \| 0.4579 \|
	\| euclidean_accuracy \| 0.9007 \|
	\| euclidean_accuracy_threshold \| 0.8922 \|
	\| euclidean_f1 \| 0.4643 \|
	\| euclidean_f1_threshold \| 0.9797 \|
	\| euclidean_precision \| 0.4201 \|
	\| euclidean_recall \| 0.5189 \|
	\| euclidean_ap \| 0.4637 \|
	\| max_accuracy \| 0.9007 \|
	\| max_accuracy_threshold \| 13.5474 \|
	\| max_f1 \| 0.4643 \|
	\| max_f1_threshold \| 15.1497 \|
	\| max_precision \| 0.4201 \|
	\| max_recall \| 0.52 \|
	\| max_ap \| 0.4637 \|

	#### Triplet

	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| cosine_accuracy \| 0.6382 \|
	\| dot_accuracy \| 0.3618 \|
	\| manhattan_accuracy \| 0.6227 \|
	\| euclidean_accuracy \| 0.6382 \|
	\| max_accuracy \| 0.6382 \|

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	## Training Details

	### Training Dataset

	#### imdb-triplet

	* Dataset: imdb-triplet
	* Size: 43,371 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:-------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 31 tokens</li><li>mean: 129.65 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------\|
	\| <code> A Metafísica dos Chocolates (1967) Beautiful girls (pre-teens, adolescents, and young women) in street scenes and one of them visiting a chocolate factory, where all the workers are young women, too. A poetic text and an extract from a major Portuguese poet, convey to us the sensual feeling of choosing, unwrapping, and munching chocolate.</code> \| <code>short</code> \|
	\| <code> Thai Jashe! (2016) Thai Jashe! is an upcoming Gujarati film written and directed by Nirav Barot. It is about the struggles of a middle class man to achieve his goals in the metro-city Ahmedabad. The film stars Manoj Joshi, Malhar Thakar and Monal Gajjar.</code> \| <code>drama</code> \|
	\| <code> Vuelco (2005) A teenage boy rides out of town to meet a a girl in the countryside. She is deaf, and he explains the different means he uses to get her attention when she has not seen him. Then they say goodbye, with one poignant hug and a desperate yell punctuating their final farewell.</code> \| <code>short</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 256
	- `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
	- `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`: 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`: 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`: 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`: no_duplicates
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| max_accuracy \| max_ap \|
	\|:------:\|:----:\|:-------------:\|:------------:\|:------:\|
	\| 0 \| 0 \| - \| 0.6382 \| 0.2004 \|
	\| 0.5882 \| 100 \| 1.7867 \| - \| 0.3542 \|
	\| 1.1765 \| 200 \| 1.3073 \| - \| 0.4564 \|
	\| 1.7647 \| 300 \| 1.266 \| - \| 0.3862 \|
	\| 2.3529 \| 400 \| 1.1889 \| - \| 0.4011 \|
	\| 2.9412 \| 500 \| 1.1554 \| - \| 0.4398 \|
	\| 3.5294 \| 600 \| 1.1558 \| - \| 0.4386 \|
	\| 4.1176 \| 700 \| 1.1555 \| - \| 0.4566 \|
	\| 4.7059 \| 800 \| 1.0835 \| - \| 0.4637 \|


	### Framework Versions
	- Python: 3.10.13
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2
	- Accelerate: 0.30.1
	- Datasets: 2.19.2
	- 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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