Instructions to use SajilAwale/FunnyModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SajilAwale/FunnyModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SajilAwale/FunnyModel", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SajilAwale/FunnyModel") model = AutoModelForSequenceClassification.from_pretrained("SajilAwale/FunnyModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| language: | |
| - en | |
| base_model: | |
| - FacebookAI/roberta-base | |
| library_name: transformers | |
| tags: | |
| - joke_analyst | |
| # Model Card for Funny Model (fun-model-v0.1) | |
| This model was fine tuned to classify if a joke is humorous, offensive and what sentiment it carries (multi-label classification). | |
| ## Model Details | |
| - **Base Model**: FacebookAI/roberta-base | |
| - **Tokenizer**: FacebookAI/roberta-base | |
| - **Parameters**: 125M | |
| ## Training Data | |
| - 10% sample of r/Jokes dataset from https://github.com/orionw/rJokesData (500k) | |
| ## Dataset | |
| - Can be found at https://huggingface.co/datasets/SajilAwale/FunnyData/ | |
| - Total Data Size: 573,410 | |
| - Train Data Size: 90% of 10% of total size | |
| - Validation Data Size: 10% of 10% of total size | |
| - Test Data Size: 90% of total size | |
|  | |
| ## Evaluation | |
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