Text Classification
Transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
English
distilbert
Eval Results (legacy)
Instructions to use distilbert/distilbert-base-uncased-finetuned-sst-2-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilbert-base-uncased-finetuned-sst-2-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english") model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english") - Inference
- Notebooks
- Google Colab
- Kaggle
Educational sentences classififcation ?
#24
by RavirajDarisi - opened
Hi,
I am thinking of using this model for this text classification task ...
for Example: " What is Addition in Math " and the Second sentence " What is Addition in Physics?" So when I feed these 2 sentences to the model, the model should give a positive response( high score) to the Math sentence and a negative response(low score) to the physics sentence so this is my requirement ...
All my questions will be regarding the topics been covered in different subjects from Grade 1 to Grade 10 .
could you help will this model be a right fit?
Thank you , peace✌