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---
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 see the direct usage in Transformers</summary>
</details>
-->
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### 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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## 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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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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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