semvec/pypi-packages huggingface dataset:
ct-loss 0.0.3
The contrastive token loss for reducing generative repetition of augoregressive neural language models.
Requires Python: >=3.7

# Contrastive Token loss function for PyTorch

This repo is the clean (PyTorch) implementation of the contrastive token loss proposed in our paper: _A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration._
For reproducing our results, please check [this repo](https://github.com/ShaojieJiang/lit-seq).

## Install

`pip install ct-loss`

## Usage
You can use our CT objective when **pretraining** or **finetuning** your augoregressive language models.
With CT, the resulting language models will have significantly less **repetitive** generations, even with deterministic decoding such as greedy and beam search.
It only takes several lines of code to use CT loss, around where you calculate PyTorch's `CrossEntropyLoss`.
Here is an example:
```python
import torch

# Suppose we already have the model output logits and labels (sequences of token indices).
# For example when the batch size is 10, sequence length is 50 and vocabulary size is 1000:
logits = torch.rand(10, 50, 1000)
labels = torch.randint(0, 999, (10, 50))

# This is how you normally use cross-entropy for a language model:
from torch.nn import CrossEntropyLoss
ce_criterion = CrossEntropyLoss()
ce_loss = ce_criterion(logits.view(-1, 1000), labels.view(-1))

# This is how you can use our contrastive token loss:
from ct.ct_loss import ContrastiveTokenLoss
ct_criterion = ContrastiveTokenLoss(pad_id=999) # we need pad tokens for masking out tokens in a sequence that should not be used as negative tokens
ct_loss = ct_criterion(logits, labels)

# In our paper [1], we use CE and CT together
loss = ce_loss + ct_loss

print(ce_loss, ct_loss)

>>> tensor(6.9536) tensor(1.5848)
```

## Cite our paper