# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thomasavare/distilbert-ft-test3")
model = AutoModelForSequenceClassification.from_pretrained("thomasavare/distilbert-ft-test3")Quick Links
distilbert-ft-test3
This model is a fine-tuned version of distilbert-base-uncased on thomasavare/waste-classification-v2. It is part of my master thesis at Politecnico di Torino in partenership with ReLearn.
It achieves the following results on the test set:
| accuracy | precision | recall | f1 |
|---|---|---|---|
| 0.974 | 0.9805 | 0.9732 | 0.9725 |
Model description
DistilBERT finetuned for waste classification on 50 different classes as part of my master thesis at Politecnico di Torino.
Intended uses & limitations
Use for waste classification on 50 different waste classes (see dataset)
Training and evaluation data
waste-classification-v2 dataset
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thomasavare/distilbert-ft-test3")