mission-impossible-lms
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no-shuffle-gpt2

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gpt2

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juliekallini commited on Nov 4, 2024

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 {}
 ---
-# Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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 ## Uses
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-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.
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 ## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 {}
 ---
+# Model Card for NoShuffle GPT-2
 <!-- Provide a quick summary of what the model is/does. -->
+This is one model in a collection of models trained on the impossible
+languages of [Kallini et al. 2024](https://arxiv.org/abs/2401.06416).
+This model is a GPT-2 Small model trained from scratch on the *NoShuffle*
+language.
+![languages.png](https://cdn-uploads.huggingface.co/production/uploads/6268bc06adb1c6525b3d5157/pBt38YYQL1gj8DqjyorWS.png)
+## Model Details
+- **Developed by:** Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** English
+- **GitHub Repository:** https://github.com/jkallini/mission-impossible-language-models
+- **Paper:** https://arxiv.org/pdf/2401.06416
 ## Uses
+This artefact is solely intended for the study of language learning
+and acquisition in computational models. It should not be
+used in any production setting.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+```
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+# Load model and tokenizer
+model_id = "mission-impossible-lms/no-shuffle-gpt2"
+model = GPT2LMHeadModel.from_pretrained(model_id)
+tokenizer = GPT2Tokenizer.from_pretrained(model_id)
+# Set up the prompt and encode it
+prompt = "He clean"
+inputs = tokenizer(prompt, return_tensors="pt")
+# Generate text
+output = model.generate(inputs.input_ids, max_length=20)
+# Decode and print the generated text
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
 ## Training Details
 ### Training Data
+This model was trained on the 100M-word BabyLM dataset.
+Before training, we first transform the dataset into
+the corresponding impossible language, as described in
+our paper.
 ### Training Procedure
+This model was trained for 3,000 gradient steps with
+a batch size of 2^19 tokens. We train with a learning
+rate that linearly warms up from 0 to 6e-4 over 300 steps.
 ## Environmental Impact
+- **Hardware Type:** NVIDIA RTX 3090 (24GB) + NVIDIA RTX A6000 (48GB) GPUs.
+- **Hours used:** ~24 hours.
+## Citation
 **BibTeX:**
+```bibtex
+@inproceedings{kallini-etal-2024-mission,
+    title = "Mission: Impossible Language Models",
+    author = "Kallini, Julie  and
+      Papadimitriou, Isabel  and
+      Futrell, Richard  and
+      Mahowald, Kyle  and
+      Potts, Christopher",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    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.787",
+    doi = "10.18653/v1/2024.acl-long.787",
+    pages = "14691--14714",
+}
+```
 **APA:**
+## Model Card Authors
+Julie Kallini
 ## Model Card Contact
+[email protected]