# SimCSE
Gao et al. present in [SimCSE](https://arxiv.org/abs/2104.08821) a simple method to train sentence embeddings without having training data. 

The idea is to encode the same sentence twice. Due to the used dropout in transformer models, both sentence embeddings will be at slightly different positions. The distance between these two embeddings will be minimized, while the distance to other embeddings of the other sentences in the same batch will be maximized (they serve as negative examples).

![SimCSE working](https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/SimCSE.png)

## Usage with SentenceTransformers
SentenceTransformers implements the [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss), which makes training with SimCSE trivial:

```python
from sentence_transformers import SentenceTransformer, InputExample
from sentence_transformers import models, losses
from torch.utils.data import DataLoader

# Define your sentence transformer model using CLS pooling
model_name = "distilroberta-base"
word_embedding_model = models.Transformer(model_name, max_seq_length=32)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

# Define a list with sentences (1k - 100k sentences)
train_sentences = [
    "Your set of sentences",
    "Model will automatically add the noise",
    "And re-construct it",
    "You should provide at least 1k sentences",
]

# Convert train sentences to sentence pairs
train_data = [InputExample(texts=[s, s]) for s in train_sentences]

# DataLoader to batch your data
train_dataloader = DataLoader(train_data, batch_size=128, shuffle=True)

# Use the denoising auto-encoder loss
train_loss = losses.MultipleNegativesRankingLoss(model)

# Call the fit method
model.fit(
    train_objectives=[(train_dataloader, train_loss)], epochs=1, show_progress_bar=True
)

model.save("output/simcse-model")
``` 

## SimCSE from Sentences File

**[train_simcse_from_file.py](train_simcse_from_file.py)** loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.

SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.


## Training Examples
- **[train_askubuntu_simcse.py](train_askubuntu_simcse.py)** - Shows the example how to train with SimCSE on the [AskUbuntu Questions dataset](https://github.com/taolei87/askubuntu).
- **[train_stsb_simcse.py](train_stsb_simcse.py)** - This script uses 1 million sentences and evaluates SimCSE on the [STSbenchmark dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark).

## Ablation Study
We use the evaluation setup proposed in our [TSDAE paper](https://arxiv.org/abs/2104.06979).


Using mean pooling, with max_seq_length=32 and batch_size=128

| Base Model | AskUbuntu Test-Performance (MAP) |
| ---- | :----: |
| distilbert-base-uncased | 53.59 |
| bert-base-uncased | 54.89 |
| **distilroberta-base** | **56.16** |
| roberta-base | 55.89 |

Using mean pooling, with max_seq_length=32 and distilroberta-base model.

| Batch Size | AskUbuntu Test-Performance (MAP) |
| ---- | :----: |
| 128 | 56.16 |
| 256 | 56.63  |
| **512** | **56.69** |

Using max_seq_length=32, distilroberta-base model, and 512 batch size.

| Pooling Mode | AskUbuntu Test-Performance (MAP) |
| ---- | :----: |
| **Mean pooling** |  **56.69** |
| CLS pooling | 56.56  |
| Max pooling | 52.91 |


**Note:**
This is a re-implementation of SimCSE within sentence-transformers. For the official CT code, see: [princeton-nlp/SimCSE](https://github.com/princeton-nlp/SimCSE)