cappr 0.3.0

Zero-shot text classification using autoregressive language models.

CAPPr: zero-shot text classification using autoregressive language models

Perform zero-shot text classification by estimating the probability that an inputted completion comes after an inputted prompt. Hence the name:

Completion
After
Prompt
Probability

The method is fleshed out in my question on Cross Validated.

Usage

Use a model from the OpenAI API

Specifically, this model must be compatible with the /v1/completions endpoint.

from cappr.openai.classify import predict

prompt = """
Tweet about a movie: "Oppenheimer was pretty good. But 3 hrs...cmon Nolan."

This tweet contains the following criticism:
""".strip("\n")

class_names = ("bad message", "too long", "unfunny")

preds = predict(
    prompts=[prompt],
    completions=class_names,
    model="text-ada-001",
)
print(preds)
# ['too long']

Notice that the completions can contain many tokens.

Extract the final answer from a step-by-step completion

Step-by-step and chain-of-thought prompts are highly effective ways to get an LLM to "reason" about more complex tasks. But if you need a structured output, a step-by-step completion is unwieldy. Use CAPPr to extract the final answer from these types of completions, given a list of possible answers.

See this idea in action here in the docs. CAPPr is 100% guaranteed to return an output from the list of answers.

Use a model from the HuggingFace model hub

Specifically, this model must be able to be loaded using transformers.AutoModelForCausalLM.from_pretrained.

from transformers import AutoModelForCausalLM, AutoTokenizer
from cappr.huggingface.classify import predict

prompt = "Which planet is closer to the Sun: Mercury or Earth?"
class_names = ("Mercury", "Earth")

# load model and tokenizer
model_name = "gpt2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

preds = predict(
    prompts=[prompt],
    completions=class_names,
    model_and_tokenizer=(model, tokenizer),
)
print(preds)
# ['Mercury']

For an example with Llama 2, see the notebook demos/llama2.ipynb. So far, CAPPr has been tested for correctness on the following models:

• GPT-2
• GPT-J
• Llama
• Llama 2 (chat and raw).

Run in batches

Let's use huggingface for this example cuz it's free. And let's predict probabilities instead of the class.

from transformers import AutoModelForCausalLM, AutoTokenizer
from cappr.huggingface.classify import predict_proba

prompts = [
    "Stephen Curry is a",
    "Martina Navratilova was a",
    "Dexter, from the TV Series Dexter's Laboratory, is a",
    "LeBron James is a",
]

# each of the prompts could be completed with one of these:
class_names = ("basketball player", "tennis player", "scientist")
prior =       (      1/6,                1/6,            2/3    )
# say I expect most of my data to have scientists

# load model and tokenizer
model_name = "gpt2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

pred_probs = predict_proba(
    prompts=prompts,
    completions=class_names,
    model_and_tokenizer=(model, tokenizer),
    batch_size=32,  # whatever fits on your CPU/GPU
    prior=prior,
)

# pred_probs[i,j] = probability that prompts[i] is classified as class_names[j]
print(pred_probs.round(1))
# [[0.5 0.3 0.2]
#  [0.3 0.6 0.2]
#  [0.1 0.1 0.8]
#  [0.8 0.2 0. ]]

# for each prompt, which completion is most likely?
pred_class_idxs = pred_probs.argmax(axis=1)
print([class_names[pred_class_idx] for pred_class_idx in pred_class_idxs])
# ['basketball player',
#  'tennis player',
#  'scientist',
#  'basketball player']

Run in batches, where each prompt has a different set of possible completions

Again, let's use huggingface to predict probabilities.

import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer

from cappr import Example
from cappr.huggingface.classify import predict_proba_examples

examples = [
    Example(
        prompt="Jodie Foster played",
        completions=("Clarice Starling", "Trinity in The Matrix"),
    ),
    Example(
        prompt="Batman, from Batman: The Animated Series, was played by",
        completions=("Pete Holmes", "Kevin Conroy", "Spongebob!"),
        prior=      (     1/3      ,      2/3     ,      0      ),
    ),
]

model_name = "gpt2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
pred_probs = predict_proba_examples(examples, model_and_tokenizer=(model, tokenizer))

# pred_probs[i][j] = probability that examples[i].prompt is classified as
# examples[i].completions[j]
print([example_pred_probs.round(2) for example_pred_probs in pred_probs])
# [array([0.7, 0.3]),
#  array([0.03, 0.97, 0.  ])]

# for each example, which completion is most likely?
pred_class_idxs = [np.argmax(example_pred_probs) for example_pred_probs in pred_probs]
print(
    [
        example.completions[pred_class_idx]
        for example, pred_class_idx in zip(examples, pred_class_idxs)
    ]
)
# ['Clarice Starling',
#  'Kevin Conroy']

More examples are linked here in the documentation.

See demos/superglue/copa.ipynb for a demonstration of a slightly harder classification task.

Documentation

https://cappr.readthedocs.io

Setup

If you intend on using OpenAI models, sign up for the OpenAI API here, and then set the environment variable OPENAI_API_KEY. For zero-shot classification, OpenAI models are currently far ahead of others. But using them will cost ya 💰!

Install with pip:

pip install cappr

(Optional) Install requirements for HuggingFace models

pip install cappr[hf]

(Optional) Install requirements for running demos

pip install cappr[demos]

Motivation

Create a more usable zero-shot text classification interface than classification via sampling (CVS).

Short

With CVS, your job is to write up your classification task in a prompt string, and then write custom code to post-process arbitrary completion/output strings.

With CAPPr, your job starts and stops at writing up your classification task as a {prompt}{end_of_prompt}{completion} string.

Long

Please see this page of the documentation.

Unstudied

I'm curious to see how much easier estimation/discrimination is than generation. In demos/superglue/copa.ipynb, CVS using OpenAI's text-curie-001 is less than 50% accurate, while CAPPr is 80% accurate.

Honest

Keep myself busy

Results

Statistical performance

Not too shabby. TODO: summary table comparing CVS vs. CAPPr vs. few-shot methods like SetFit and PET.

2 SuperGLUE datasets

RAFT zero-shot training sets

Computational performance

One concern was that CAPPr requires as many model() calls as there are classes. But in the CAPPr scheme, we can simply cache each attention block's keys and values for the prompts. This feature is already supported by AutoModelForCausalLMs. See this code for the implementation. Note that this caching is not implemented for OpenAI models, as I can't control their backend. This means that when running cappr.openai functions, you'll be on the cappr (no cache) line :-(

Figure 1: COPA dataset, repeating the choices to simulate multi-class classification tasks. GPT-2 (small) was run on a Tesla K80 GPU (whatever was free in Google Colab in March 2023). 96 classification inputs were processed in batches of size 32. Each point in the graph is a median of 5 runs. For classification via sampling (CVS), exactly 4 tokens were generated for each prompt, which is the number of tokens in '\n\nAnswer A'. 1-token times are also shown. But for COPA (and other multiple-choice style prompts), that may result in lower zero-shot accuracy, as most of the sampled choices come after the first token.

See the demos/computational_analysis.ipynb notebook.

Related work

There are many papers where averaging token log-probabilities is a useful subroutine. Here are some papers which focus on this idea.

While benchmarking this method on the Winograd Schema Challenge, I found that this paper is very similar:

Trinh, Trieu H., and Quoc V. Le. "A simple method for commonsense reasoning." arXiv preprint arXiv:1806.02847 (2018).

PET with multiple masks also aggregates token probabilities to do prompt-completion classification, but these probabilities are assumed to come from masked language models like BERT.

Schick, Timo, and Hinrich Schütze. "It's not just size that matters: Small language models are also few-shot learners." arXiv preprint arXiv:2009.07118 (2020).

Contributing

TODO

Local development

Setup

1. Create a new Python 3.8+ environment using venv. Activate it

2. Clone the repo (or fork it and clone that)

   git clone https://github.com/kddubey/cappr.git
   

3. cd to the repo and install this package in editable mode, along with development requirements (ensure your venv is activated)

   python -m pip install -e .[dev]
   

VS code extensions for development

• autoDocstring. Use the numpy format.
• Set Python formatting to black.
• Rewrap. Enable Auto Wrap.

Testing

pytest

Note that a few small, dummy model will be downloaded to your computer if you don't have them already.

Docs

To locally build docs (I'm on Windows lol), run

cd docs

make.bat html

To preview these docs, open docs/build/html/index.html.

Docs are automatically built when code is merged to main.

Release

Bump the version, and then create a new release on GitHub. A new version of the package will then be automatically published on PyPI.

Todo

(**) = I'm currently working on this or will work on it really soon. Expect it in the next release or two.

Code

• Factor out the discount feature in cappr.openai.classify.predict_proba into cappr.utils.classify._predict_proba
• Small CPU speed-ups
  • For constant-completions input, vectorize cappr.utils.classify.agg_log_probs
  • For examples input, if # completions per prompt is constant, vectorize cappr.utils.classify.posterior_prob
• HuggingFace transformers.AutoModelForCausalLM
  • Support as many of them as possible
    • GPT-2
    • GPT-J
    • Llama
    • Llama 2
    • Llama 2 chat
    • Vicuna
    • PaLM
    • T5
  • If all completions are single-tokens, just run inference once (**)
  • Optimize backend to enable greater scaling wrt # completions/classes
  • Get it working on GPU, check that it's faster than sampling
    • Get to the bottom of why it's slower w/o batching (**)
  • Allow non-' ' end_of_prompt. I'm not sure how helpful that is.
  • Factor out repeated code b/t classify and classify_no_cache
  • Support Inference Endpoints?
  • Support TensorFlow models?
  • Support priming, as in: cache it. See backprompt
• User conveniences (**)
  • Make progress bars optional, since inference often isn't batched
  • Accept string input and return string instead of list
• Factor out input checks (on prompts and completions)
• (for me) Auto-enforced code formatting b/c it's getting time-consuming
• Allow for multi-label classification
  • Pass normalize as an argument to predict_proba functions
  • For huggingface, add note that you'll get faster results by passing all labels at once (assuming prompt is identical for each label)
• Fill in missing or non-numpy docstrings
• Testing
  • Test cappr.huggingface.classify_no_cache by comparing to results w/o batching!
  • For heavily quantized models, only test that pred probs are w/in 1e-2 atol
  • Increase test cases
  • Test input checks
  • Test cappr.openai.api

Research

Evaluate on more datasets, and understand its relative advantages and disadvantages vs other classification methods.

• (Llama2 + CAPPr) (Llama2 + CVS) vs (Llama2 chat + CAPPr) vs (Llama2 chat + CVS) (**)
• RAFT benchmark
  • Zero-shot training scores
  • Submit zero-shot test predictions
  • Few-shot (priming) training scores
  • Submit few-shot test predictions
• Create a user guide, build a table of results comparing competing approaches on statistical performance, cost, and computation
• Evaluate a CoT/SbS prompt -> CAPPr to pull out the answer
• Make a computational comparison to sampling
  • Assume I have full freedom to decide how inference works. Demo w/ GPT-2. Process inputs in batches.
  • Process inputs 1-by-1
• More SuperGLUE tasks?
• Calibration
  • Is the prior actually effective? Downsample and see
  • curves
• Finetune smaller, cheaper model and compare against zero-shot w/ davinci
  • e.g., GPT-2 from huggingface, text-ada-001
  • Again, compare against sampling
• Evaluate a bigger model like GPT-J