Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
aspect-based-sentiment-analysis
education
student-feedback
teacher-evaluation
pedagogy
text-embeddings-inference
Instructions to use chiraghariprasad/Pedagogy-Analysis-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chiraghariprasad/Pedagogy-Analysis-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chiraghariprasad/Pedagogy-Analysis-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chiraghariprasad/Pedagogy-Analysis-V1") model = AutoModelForSequenceClassification.from_pretrained("chiraghariprasad/Pedagogy-Analysis-V1") - Notebooks
- Google Colab
- Kaggle
Pedagogy-Analysis-V1
A fine-tuned BERT model for pedagogical analysis of educational text.
Model Details
- Architecture: BERT
- Framework: Hugging Face Transformers
- License: MIT
- Task: Text Classification
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "chiraghariprasad/Pedagogy-Analysis-V1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = "The instructor explained the concept clearly."
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)
print(outputs.logits)
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