Datasets:
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parquet
Languages:
English
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Tags:
reinforcement-learning-from-human-feedback
reinforcement-learning
dialogue
conversational-ai
preference-alignment
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File size: 4,505 Bytes
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---
language:
- en
license: apache-2.0
task_categories:
- question-answering
- text-generation
tags:
- reinforcement-learning-from-human-feedback
- reinforcement-learning
- dialogue
- conversational-ai
- preference-alignment
dataset_info:
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- name: labels
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- name: game_turn_id
dtype: string
- name: text
dtype: string
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- name: text
dtype: string
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- config_name: human_eval
features:
- name: correctness
dtype: string
- name: feedback
dtype: string
- name: delta_clicks
sequence: string
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dtype: string
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- name: clicks
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data_files:
- split: train
path: feedback_decoder_binary/train-*
- config_name: feedback_decoder_ternary
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path: feedback_decoder_ternary/train-*
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path: human_eval/train-*
- config_name: interaction
data_files:
- split: train
path: interaction/train-*
- config_name: turn
data_files:
- split: train
path: turn/train-*
---
# Retrospective Learning from Interactions (Respect) Dataset
This repository contains the `lil-lab/respect` data, based on the ACL paper [Retrospective Learning from Interactions](https://huggingface.co/papers/2410.13852). For more resources, please see <https://lil-lab.github.io/respect> and <https://github.com/lil-lab/respect>.
## Sample Usage
You can load the data and associated checkpoints as follows:
```python
from datasets import load_dataset
from transformers import Idefics2ForConditionalGeneration
from peft import PeftModel
import torch # Ensure torch is imported
# Download data
ds = load_dataset("lil-lab/respect", name="turn", split="train")
# Download checkpoints
checkpoint = "HuggingFaceM4/idefics2-8b"
model_id = 'lil-lab/respect'
model = Idefics2ForConditionalGeneration.from_pretrained(
checkpoint, torch_dtype=torch.bfloat16)
peft_model = PeftModel.from_pretrained(
model, model_id, adapter_name="r6_bp", revision="r6_bp")
```
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