--- 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) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - imdb-triplet ### 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] ``` ## Evaluation ### Metrics #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](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 [TripletEvaluator](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** | ## Training Details ### Training Dataset #### imdb-triplet * Dataset: imdb-triplet * Size: 43,371 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------| | 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. | short | | 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. | drama | | 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. | short | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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
### 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} } ```