---
library_name: transformers
base_model:
- google/byt5-small
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is pre-trained to take a representation of a Finite State Transducer (FST) and a string and predict the output of the FST for that string. The FSTs for pre-training were synthetically generated.
The goal is to inject an inductive bias for FST-like tasks. Analysis of the model suggests that it has learned to internally simulate transitions between FST states in its hidden representations -- without being explicitly trained to do so.

See [SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation](https://aclanthology.org/2024.acl-long.355/) for all the details.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Matthias Lindemann
- **Funded by:** UKRI, Huawei, Dutch National Science Foundation
- **Model type:**  Sequence-to-Sequence model
- **Language(s) (NLP):** no natural language data was used for continual pretraining
- **License:** [More Information Needed]
- **Finetuned from model:** ByT5

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/namednil/sip
- **Paper:** https://aclanthology.org/2024.acl-long.355/

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Without fine-tuning, the model can approximately simulate FST behavior (see also `namednil/sip-d4-pt` and the documentation in the git repo). The main use is in fine-tuning.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

FST-like tasks such as grapheme-to-phoneme conversion, or simple text editing in few-shot setups.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import transformers, torch
tokenizer = transformers.AutoTokenizer.from_pretrained("google/byt5-small")
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("namednil/sip-d4", trust_remote_code=True)
# (always make sure to check the remote code on Huggingface!)

# Construct an optimizer that uses the SIP-finetuning procedure:
optimizer = model.get_optimizer(torch.optim.Adam, prefix_lr=1.0, lr=3e-4)
# ... fine-tune the model as usual

# The above code uses a random initialization of the tunable prefix of SIP. 
# If you don't want that and have more control over the length of the tunable prefix, run:

config = transformers.AutoConfig.from_pretrained("namednil/sip-d4", trust_remote_code=True)
config.random_selection = False
config.prefix_length = 50 
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("namednil/sip-d4", config=config, trust_remote_code=True)
```

## Model Examination

<!-- Relevant interpretability work for the model goes here -->

See [SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation](https://aclanthology.org/2024.acl-long.355/)

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

- **Hardware Type:** Nvidia RTX 2080 Ti
- **Hours used:** 30
- **Compute Region:** Scotland
- **Carbon Emitted:** 0.2 kg CO2eq

## Citation

```bibtex
@inproceedings{lindemann-etal-2024-sip,
    title = "{SIP}: Injecting a Structural Inductive Bias into a {S}eq2{S}eq Model by Simulation",
    author = "Lindemann, Matthias  and
      Koller, Alexander  and
      Titov, Ivan",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.355/",
    doi = "10.18653/v1/2024.acl-long.355",
}
```