Text Classification
Transformers
Safetensors
English
distilbert
movie-certification
content-rating
movie-script
nlp
multilingual
knowledge-distillation
long-document
explainable-ai
text-embeddings-inference
Instructions to use pratikkalamkar/moviecert-teacher-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratikkalamkar/moviecert-teacher-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pratikkalamkar/moviecert-teacher-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pratikkalamkar/moviecert-teacher-en") model = AutoModelForSequenceClassification.from_pretrained("pratikkalamkar/moviecert-teacher-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "label_map": { | |
| "G": 0, | |
| "PG": 1, | |
| "PG-13": 2, | |
| "R": 3, | |
| "NC-17": 4 | |
| }, | |
| "inv_label_map": { | |
| "0": "G", | |
| "1": "PG", | |
| "2": "PG-13", | |
| "3": "R", | |
| "4": "NC-17" | |
| }, | |
| "metadata": { | |
| "chunk_max_len": 256, | |
| "chunk_stride": 64 | |
| } | |
| } |