Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use kanishka/aann-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanishka/aann-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanishka/aann-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanishka/aann-detector") model = AutoModelForSequenceClassification.from_pretrained("kanishka/aann-detector") - Notebooks
- Google Colab
- Kaggle
AANN-Detector
This model is a fine-tuned version of bert-base-uncased on a custom dataset that detects if a sentence contains the interesting "Indefinite Article + Adjective + Numeral + Noun" construction.
For instance: A beautiful five days counts but "A five beautiful days" does not, since the numeral precedes the adjective.
This idea was inspired by Chris Potts' "obscure" classifier to detect the PiPP construction.
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.2.1
- Tokenizers 0.14.1
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Model tree for kanishka/aann-detector
Base model
google-bert/bert-base-uncased