deberta v3 base zeroshot v2.0
简介
核心亮点
- 无需训练数据，直接通过标签名称进行分类
- 语义理解精准，适合处理灵活变动的分类任务
- 部署轻量，可替代部分简单的关键词匹配逻辑
- 理想的冷启动方案，快速验证文本分类可行性
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("MoritzLaurer/deberta-v3-base-zeroshot-v2.0")
tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/deberta-v3-base-zeroshot-v2.0")Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download MoritzLaurer/deberta-v3-base-zeroshot-v2.0下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download MoritzLaurer/deberta-v3-base-zeroshot-v2.0 config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('MoritzLaurer/deberta-v3-base-zeroshot-v2.0')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('MoritzLaurer/deberta-v3-base-zeroshot-v2.0')
tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/deberta-v3-base-zeroshot-v2.0')完整文档
---
language:
- en
tags:
- text-classification
- zero-shot-classification
base_model: microsoft/deberta-v3-base
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---
Model description: deberta-v3-base-zeroshot-v2.0
zeroshot-v2.0 series of models
Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection.The main update of this zeroshot-v2.0 series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements.
These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
(entailment vs. not_entailment).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into this task by the Hugging Face pipeline.
Training data
Models with a "-c" in the name are trained on two types of fully commercially-friendly data:
1. Synthetic data generated with Mixtral-8x7B-Instruct-v0.1.
I first created a list of 500+ diverse text classification tasks for 25 professions in conversations with Mistral-large. The data was manually curated.
I then used this as seed data to generate several hundred thousand texts for these tasks with Mixtral-8x7B-Instruct-v0.1.
The final dataset used is available in the synthetic_zeroshot_mixtral_v0.1 dataset
in the subset mixtral_written_text_for_tasks_v4. Data curation was done in multiple iterations and will be improved in future iterations.
2. Two commercially-friendly NLI datasets: (MNLI, FEVER-NLI).
These datasets were added to increase generalization.
3. Models without a "-c" in the name also included a broader mix of training data with a broader mix of licenses: ANLI, WANLI, LingNLI,
and all datasets in this list
where used_in_v1.1==True.
How to use the models
#!pip install transformers[sentencepiece]
from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "This text is about {}"
classes_verbalized = ["politics", "economy", "entertainment", "environment"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0") # change the model identifier here
output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False)
print(output)multi_label=False forces the model to decide on only one class. multi_label=True enables the model to choose multiple classes.
Metrics
The models were evaluated on 28 different text classification tasks with the f1_macro metric.
The main reference point is facebook/bart-large-mnli which is, at the time of writing (03.04.24), the most used commercially-friendly 0-shot classifier.
| | facebook/bart-large-mnli | roberta-base-zeroshot-v2.0-c | roberta-large-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0 (fewshot) | deberta-v3-large-zeroshot-v2.0-c | deberta-v3-large-zeroshot-v2.0 (fewshot) | bge-m3-zeroshot-v2.0-c | bge-m3-zeroshot-v2.0 (fewshot) |
|:---------------------------|---------------------------:|-----------------------------:|------------------------------:|--------------------------------:|-----------------------------------:|---------------------------------:|------------------------------------:|-----------------------:|--------------------------:|
| all datasets mean | 0.497 | 0.587 | 0.622 | 0.619 | 0.643 (0.834) | 0.676 | 0.673 (0.846) | 0.59 | (0.803) |
| amazonpolarity (2) | 0.937 | 0.924 | 0.951 | 0.937 | 0.943 (0.961) | 0.952 | 0.956 (0.968) | 0.942 | (0.951) |
| imdb (2) | 0.892 | 0.871 | 0.904 | 0.893 | 0.899 (0.936) | 0.923 | 0.918 (0.958) | 0.873 | (0.917) |
| appreviews (2) | 0.934 | 0.913 | 0.937 | 0.938 | 0.945 (0.948) | 0.943 | 0.949 (0.962) | 0.932 | (0.954) |
| yelpreviews (2) | 0.948 | 0.953 | 0.977 | 0.979 | 0.975 (0.989) | 0.988 | 0.985 (0.994) | 0.973 | (0.978) |
| rottentomatoes (2) | 0.83 | 0.802 | 0.841 | 0.84 | 0.86 (0.902) | 0.869 | 0.868 (0.908) | 0.813 | (0.866) |
| emotiondair (6) | 0.455 | 0.482 | 0.486 | 0.459 | 0.495 (0.748) | 0.499 | 0.484 (0.688) | 0.453 | (0.697) |
| emocontext (4) | 0.497 | 0.555 | 0.63 | 0.59 | 0.592 (0.799) | 0.699 | 0.676 (0.81) | 0.61 | (0.798) |
| empathetic (32) | 0.371 | 0.374 | 0.404 | 0.378 | 0.405 (0.53) | 0.447 | 0.478 (0.555) | 0.387 | (0.455) |
| financialphrasebank (3) | 0.465 | 0.562 | 0.455 | 0.714 | 0.669 (0.906) | 0.691 | 0.582 (0.913) | 0.504 | (0.895) |
| banking7
