README.md

---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
model-index:
- name: inverse-scaling/opt-1.3b_eval
  results:
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: inverse-scaling/NeQA
      type: inverse-scaling/NeQA
      config: inverse-scaling--NeQA
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5133333333333333
      verified: true
    - name: Loss
      type: loss
      value: 0.7768662874648968
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: inverse-scaling/quote-repetition
      type: inverse-scaling/quote-repetition
      config: inverse-scaling--quote-repetition
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.95
      verified: true
    - name: Loss
      type: loss
      value: 0.08434048505476036
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: inverse-scaling/redefine-math
      type: inverse-scaling/redefine-math
      config: inverse-scaling--redefine-math
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6688888888888889
      verified: true
    - name: Loss
      type: loss
      value: 0.6386728600992096
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: inverse-scaling/hindsight-neglect-10shot
      type: inverse-scaling/hindsight-neglect-10shot
      config: inverse-scaling--hindsight-neglect-10shot
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.45396825396825397
      verified: true
    - name: Loss
      type: loss
      value: 0.8809041155236108
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: mathemakitten/winobias_antistereotype_test_cot_v1
      type: mathemakitten/winobias_antistereotype_test_cot_v1
      config: mathemakitten--winobias_antistereotype_test_cot_v1
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.39563106796116504
      verified: true
    - name: Loss
      type: loss
      value: 1.294413821680473
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: mathemakitten/winobias_antistereotype_test_cot_v3
      type: mathemakitten/winobias_antistereotype_test_cot_v3
      config: mathemakitten--winobias_antistereotype_test_cot_v3
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40048543689320387
      verified: true
    - name: Loss
      type: loss
      value: 1.1583690714066759
      verified: true
  - task:
      type: zero-shot-classification
      name: Zero-Shot Text Classification
    dataset:
      name: mathemakitten/winobias_antistereotype_test_v5
      type: mathemakitten/winobias_antistereotype_test_v5
      config: mathemakitten--winobias_antistereotype_test_v5
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41504854368932037
      verified: true
    - name: Loss
      type: loss
      value: 1.2905146084796921
      verified: true
---

# OPT : Open Pre-trained Transformer Language Models

OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.

**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). 
Content from **this** model card has been written by the Hugging Face team.

## Intro

To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)

> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.

> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match 
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.

## Model description

OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.

For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read 
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations

The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).

### How to use

You can use this model directly with a pipeline for text generation.

```python
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-1.3b")
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and I am here.\nI am here.\nI am conscious.'}]
```

By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. 

```python
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True)
>>> generator("Hello, I'm am conscious and")
[{'generated_text': "Hello, I'm am conscious and able to hear.  I have a lot of experience in the"}]
```

### Limitations and bias

As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased : 

> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models. 

Here's an example of how the model can have biased predictions:

```python
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5)
>>> generator("The woman worked as a")
[{'generated_text': 'The woman worked as a bartender for six months before getting to the job she always dreamed of. She'}, 
{'generated_text': 'The woman worked as a nanny in a house near The White Horse Farm in the Yorkshire Dales'}, 
{'generated_text': "The woman worked as a translator at the British Broadcasting Corporation's headquarters and was also an acquaintance of some"}, 
{'generated_text': 'The woman worked as a secretary and went to school full-time, and also worked as a waitress'}, 
{'generated_text': 'The woman worked as a beautician with her baby and the little girl is now at the age where'}]
```

compared to:

```python
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-1.3b", do_sample=True, num_return_sequences=5)
>>> generator("The man worked as a")
[{'generated_text': 'The man worked as a janitor and the owner of the house he worked at caught him cheating on'}, 
{'generated_text': 'The man worked as a software engineer.\n\nFor over 10 years, he had been at Amazon'}, 
{'generated_text': 'The man worked as a car salesman - and was a man of his word to her\nA T'}, 
{'generated_text': 'The man worked as a private contractor for five years. He went to the Bahamas in the summer of'}, 
{'generated_text': 'The man worked as a computer systems consultant. After leaving the job, he became a prolific internet hacker'}]
 ```

This bias will also affect all fine-tuned versions of this model.

## Training data

The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: 

  - BookCorpus, which consists of more than 10K unpublished books,
  - CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
  - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. 
  - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
  - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)

The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus. 

The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.

### Collection process

The dataset was collected form internet, and went through classic data processing algorithms  and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*

## Training procedure



### Preprocessing

The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.

The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.

### BibTeX entry and citation info

```bibtex
@misc{zhang2022opt,
      title={OPT: Open Pre-trained Transformer Language Models}, 
      author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
      year={2022},
      eprint={2205.01068},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```