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")# 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
File size: 1,028 Bytes
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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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