---
title: ofa_text-classification_mnli_large_en
canonical_url: "https://www.modelscope.cn/models/damo/ofa_text-classification_mnli_large_en"
md_url: "https://www.modelscope.cn/models/damo/ofa_text-classification_mnli_large_en.md"
repository: damo/ofa_text-classification_mnli_large_en
chinese_name: "OFA自然语言推理模型-英文-数据集MNLI-large"
last_updated: 2025-06-11
pipeline_tag: text-classification
tasks:
  - text-classification
model_type:
  - ofa
architectures:
  - OFAModel
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 1192
stars: 3
---

# ofa_text-classification_mnli_large_en

> ofa_text-classification_mnli_large_en - damo 在 ModelScope 开源的模型。给定一个前提句和一个假设句，任务是预测前提是否包含假设（蕴含），与假设相矛盾（矛盾），或者两者都不包含（中性）。

- **Repository**: damo/ofa_text-classification_mnli_large_en
- **Tasks**: text-classification
- **Downloads**: 1192
- **Stars**: 3
- **Last updated**: 2025-06-11

Source: https://www.modelscope.cn/models/damo/ofa_text-classification_mnli_large_en

---

# OFA-自然语言推理 (MNLI)
## 自然语言推理是什么？
给定一个前提句和一个假设句，任务是预测前提是否包含假设（蕴含），与假设相矛盾（矛盾），或者两者都不包含（中性）。

## 快速玩起来
玩转OFA只需区区以下数行代码，就是如此轻松！如果你觉得还不够方便，请点击右上角`Notebook`按钮，我们为你提供了配备好的环境（可选CPU/GPU），你只需要在notebook里输入提供的代码，就可以把OFA玩起来了！

```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

ofa_pipe = pipeline(
    Tasks.text_classification,
    model='damo/ofa_text-classification_mnli_large_en',
    model_revision='master')
text = 'One of our number will carry out your instructions minutely.'
text2 = 'A member of my team will execute your orders with immense precision.'
input = {'text': text, 'text2': text2}
result = ofa_pipe(input)
print(result)
```
<br>

## OFA是什么？
OFA(One-For-All)是通用多模态预训练模型，使用简单的序列到序列的学习框架统一模态（跨模态、视觉、语言等模态）和任务（如图片生成、视觉定位、图片描述、图片分类、文本生成等），详见我们发表于ICML 2022的论文：[OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://arxiv.org/abs/2202.03052)，以及我们的官方Github仓库[https://github.com/OFA-Sys/OFA](https://github.com/OFA-Sys/OFA)。
<br>

<p align="center">
    <br>
    <img src="resources/OFA_logo_tp_path.svg" width="150" />
    <br>
<p>
<br>

<p align="center">
        <a href="https://github.com/OFA-Sys/OFA">Github</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2202.03052">Paper </a>&nbsp ｜ &nbspBlog
</p>

<p align="center">
    <br>
        <video src="https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/maas/resources/modelscope_web/demo.mp4" loop="loop" autoplay="autoplay" muted width="80%"></video>
    <br>
</p>

### OFA模型规模：

<table border="1" width="100%">
    <tr align="center">
        <th>Model</th><th>Params-en</th><th>Params-zh</th><th>Backbone</th><th>Hidden size</th><th>Intermediate size</th><th>Num. of heads</th><th>Enc layers</th><th>Dec layers</th>
    </tr>
    <tr align="center">
        <td>OFA<sub>Tiny</sub></td><td>33M</td><td>-</td><td>ResNet50</td><td>256</td><td>1024</td><td>4</td><td>4</td><td>4</td>
    </tr>
    <tr align="center">
        <td>OFA<sub>Medium</sub></td><td>93M</td><td>-</td><td>ResNet101</td><td>512</td></td><td>2048</td><td>8</td><td>4</td><td>4</td>
    </tr>
    <tr align="center">
        <td>OFA<sub>Base</sub></td><td>180M</td><td>160M</td><td>ResNet101</td><td>768</td></td><td>3072</td><td>12</td><td>6</td><td>6</td>
    </tr>
    <tr align="center">
        <td>OFA<sub>Large</sub></td><td>470M</td><td>440M</td><td>ResNet152</td><td>1024</td></td><td>4096</td><td>16</td><td>12</td><td>12</td>
    </tr>
    <tr align="center">
        <td>OFA<sub>Huge</sub></td><td>930M</td><td>-</td><td>ResNet152</td><td>1280</td></td><td>5120</td><td>16</td><td>24</td><td>12</td>
    </tr>
</table>
<br>


## 效果展示
OFA在GLUE多个任务上取得了和RoBERTa、DEBERTa等经典模型匹敌的结果，具体如下所示：

<img src="resources/ofa_mnli_score.png" alt="score" />

## 模型训练流程

### 训练数据介绍
本模型训练数据集是glue/mnli数据集。

### 训练流程
模型及finetune细节请参考[OFA Tutorial](https://modelscope.cn/docs/OFA_Tutorial#1.4%20%E5%A6%82%E4%BD%95%E8%AE%AD%E7%BB%83) 1.4节。


### Finetune示例
```python
from modelscope.msdatasets import MsDataset
import tempfile
from modelscope.msdatasets import MsDataset
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from modelscope.utils.constant import DownloadMode

train_dataset = MsDataset(
    MsDataset.load('clue', subset_name='afqmc', split='train').remap_columns({
        'sentence1': 'text',
        'sentence2': 'text2'
    }))
test_dataset = MsDataset(
    MsDataset.load('clue', subset_name='afqmc', split='validation').remap_columns({
        'sentence1': 'text',
        'sentence2': 'text2'
    }))


def cfg_modify_fn(cfg):
    cfg.train.hooks = [{
        'type': 'CheckpointHook',
        'interval': 2
    }, {
        'type': 'TextLoggerHook',
        'interval': 1
    }, {
        'type': 'IterTimerHook'
    }]
    cfg.train.max_epochs=2
    return cfg

args = dict(
    model='damo/ofa_text-classification_mnli_large_en',
    model_revision='v1.0.1',
    train_dataset=train_dataset,
    eval_dataset=test_dataset,
    cfg_modify_fn=cfg_modify_fn,
    work_dir = tempfile.TemporaryDirectory().name)
trainer = build_trainer(name=Trainers.ofa, default_args=args)
trainer.train()
```

## 模型局限性以及可能的偏差
训练数据集自身有局限，有可能产生一些偏差，请用户自行评测后决定如何使用。

## 相关论文以及引用信息
如果你觉得OFA好用，喜欢我们的工作，欢迎引用：

```
@article{wang2022ofa,
  author    = {Peng Wang and
               An Yang and
               Rui Men and
               Junyang Lin and
               Shuai Bai and
               Zhikang Li and
               Jianxin Ma and
               Chang Zhou and
               Jingren Zhou and
               Hongxia Yang},
  title     = {OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence
               Learning Framework},
  journal   = {CoRR},
  volume    = {abs/2202.03052},
  year      = {2022}
}
```