Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use gorovuha/distilbert-base-uncased-humor-classifier-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gorovuha/distilbert-base-uncased-humor-classifier-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gorovuha/distilbert-base-uncased-humor-classifier-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gorovuha/distilbert-base-uncased-humor-classifier-en") model = AutoModelForSequenceClassification.from_pretrained("gorovuha/distilbert-base-uncased-humor-classifier-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-humor-classifier-en
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.19.1
- Downloads last month
- 10
Model tree for gorovuha/distilbert-base-uncased-humor-classifier-en
Base model
distilbert/distilbert-base-uncased