echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic

🤗 Hugging Face 来源text-classificationapache-2.0139 MBother✓ 1 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic ./model-folder
需要做种者 →

Dynamically quantized DistilBERT base uncased finetuned SST-2

Table of Contents

Model Details

Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.

  • Model Type: Text Classification
  • Language(s): English
  • License: Apache-2.0
  • Parent Model: For more details on the original model, we encourage users to check out this model card.

How to Get Started With the Model

This requires to install Optimum : pip install optimum[neural-compressor]

To load the quantized model and run inference using the Transformers pipelines, you can do as follows:

from transformers import AutoTokenizer, pipeline
from optimum.intel import INCModelForSequenceClassification

model_id = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic"
model = INCModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "He's a dreadful magician."
outputs = cls_pipe(text)