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
Update README.md
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README.md
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# MovieCert English Teacher Model
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## Model Details
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---
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license: apache-2.0
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datasets:
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- pratikkalamkar/Movie_Scripts_with_Age_Ratings_by_Pratik_Kalamkar-EN
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language:
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- en
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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base_model:
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- distilbert/distilbert-base-multilingual-cased
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pipeline_tag: text-classification
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library_name: transformers
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tags:
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- movie-certification
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- content-rating
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- movie-script
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- nlp
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- text-classification
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- multilingual
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- distilbert
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- knowledge-distillation
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- long-document
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- explainable-ai
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---
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# MovieCert English Teacher Model
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## Model Details
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