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
File size: 266 Bytes
e3eb635 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"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
}
} |