README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6066
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Mitochondria, often called 'powerhouses of the cell,' generate
    most of the cell's ATP through cellular respiration and have their own DNA.
  sentences:
  - Plate tectonics theory explains that Earth's lithosphere is divided into plates
    that move, causing earthquakes, volcanoes, and mountain formation.
  - The Titanic was intentionally sunk as part of an insurance scam by J.P. Morgan.
  - Why can't you trust a statistician? They're always plotting something, and they
    have a mean personality.
- source_sentence: Sharks have existed for about 400 million years, predating trees
    (which appeared around 350 million years ago).
  sentences:
  - What is a physicist's favorite food? Fission chips.
  - Venus has a surface temperature of ~465°C (870°F) due to a runaway greenhouse
    effect from its dense CO2 atmosphere, making it hotter than Mercury.
  - My therapist told me time heals all wounds. So I stabbed him. Now we wait. For
    science!
- source_sentence: CRISPR-Cas9 is a gene-editing tool that uses a guide RNA to direct
    the Cas9 enzyme to a specific DNA sequence for cutting.
  sentences:
  - Plate tectonics theory explains that Earth's lithosphere is divided into plates
    that move, causing earthquakes, volcanoes, and mountain formation.
  - Elvis Presley faked his death and is still alive, living in secret.
  - Why don't skeletons fight each other? They don't have the guts.
- source_sentence: Venus has a surface temperature of ~465°C (870°F) due to a runaway
    greenhouse effect from its dense CO2 atmosphere, making it hotter than Mercury.
  sentences:
  - JFK was assassinated by the CIA/Mafia/LBJ, not a lone gunman.
  - Why do programmers prefer dark mode? Because light attracts bugs.
  - Plate tectonics theory explains that Earth's lithosphere is divided into plates
    that move, causing earthquakes, volcanoes, and mountain formation.
- source_sentence: Finland doesn't exist; it's a fabrication by Japan and Russia.
  sentences:
  - Why did the functions stop calling each other? Because they had constant arguments
    and no common ground.
  - What's a pirate's favorite programming language? Rrrrr! (or C, for the sea)
  - The lost city of Atlantis is real and its advanced technology is hidden from us.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: meme dev binary
      type: meme-dev-binary
    metrics:
    - type: cosine_accuracy
      value: 1.0
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7174700498580933
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 1.0
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7174700498580933
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 1.0
      name: Cosine Precision
    - type: cosine_recall
      value: 1.0
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9999999999999999
      name: Cosine Ap
    - type: cosine_mcc
      value: 1.0
      name: Cosine Mcc
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 
The main goal of thius fine-tuned model is to assignb memes into 3 different clusters:

- Conspiracy
- Cluster Educational Science Humor
- Wordplay & Nerd Humor

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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

model = 'PietroSaveri/meme-cluster-classifier'
fine_tuned_model = SentenceTransformer(model)

# 3) Compute centroids just once
seed_centroids = {}
for cat, texts in seed_texts.items():
    embs = embedding_model.encode(texts, convert_to_numpy=True)
    seed_centroids[cat] = embs.mean(axis=0)

# 4) Define a tiny helper for cosine
def cosine_sim(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# 5) Wrap it all up in a function
def predict(text: str):
    vec = fine_tuned_model.encode(text, convert_to_numpy=True)
    sims = { cat: cosine_sim(vec, centroid) for cat, centroid in seed_centroids.items()}
    # sort by descending similarity
    assigned = max(sims, key=sims.get)
    return sims, assigned


# --- USAGE ---
text = "Why did the biologist go broke? Because his cells were division!"
scores, ranking = predict(text)

print("Raw scores:")
for cat, score in scores.items():
    print(f"  {cat:25s}: {score:.3f}")Raw scores:

# Conspiracy               : 0.700
# Wordplay & Nerd Humor    : 0.907
# Educational Science Humor: 0.903
```

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

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## Evaluation

### Metrics

#### Binary Classification

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

| Metric                    | Value   |
|:--------------------------|:--------|
| cosine_accuracy           | 1.0     |
| cosine_accuracy_threshold | 0.7175  |
| cosine_f1                 | 1.0     |
| cosine_f1_threshold       | 0.7175  |
| cosine_precision          | 1.0     |
| cosine_recall             | 1.0     |
| **cosine_ap**             | **1.0** |
| cosine_mcc                | 1.0     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 6,066 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                                                                             | float                                                          |
  | details | <ul><li>min: 11 tokens</li><li>mean: 24.61 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 24.17 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                                           | sentence_1                                                                                                                                          | label            |
  |:-----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>The cure for AIDS was discovered decades ago but suppressed to reduce world population.</code> | <code>Einstein’s theory of general relativity describes gravity not as a force, but as the curvature of spacetime caused by mass and energy.</code> | <code>0.0</code> |
  | <code>5G towers are designed to activate nanoparticles from vaccines for population control.</code>  | <code>The Mandela Effect proves we've shifted into an alternate reality.</code>                                                                     | <code>1.0</code> |
  | <code>The Georgia Guidestones were a NWO manifesto, destroyed to hide the plans.</code>              | <code>Elvis Presley faked his death and is still alive, living in secret.</code>                                                                    | <code>1.0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `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`: 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
- `torch_empty_cache_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`: 4
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | meme-dev-binary_cosine_ap |
|:------:|:----:|:-------------:|:-------------------------:|
| 0.5    | 190  | -             | 0.9999                    |
| 1.0    | 380  | -             | 1.0000                    |
| 1.3158 | 500  | 0.3125        | -                         |
| 1.5    | 570  | -             | 1.0000                    |
| 2.0    | 760  | -             | 0.9999                    |
| 2.5    | 950  | -             | 1.0000                    |


### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1

## 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",
}
```

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