distilbert-base-uncased-finetuned-sst-2-english EU AI Act Compliance Profile

DistilBERT base uncased finetuned SST-2

Table of Contents

• Model Details
• How to Get Started With the Model
• Uses
• Risks, Limitations and Biases
• Training

Model Details

Model Description: This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).

• Developed by: Hugging Face
• Model Type: Text Classification
• Language(s): English
• License: Apache-2.0
• Parent Model: For more details about DistilBERT, we encourage users to check out this model card.
• Resources for more information:
  • Model Documentation
  • DistilBERT paper

How to Get Started With the Model

Example of single-label classification: ​​

import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

Uses

Direct Use

This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned vers

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