Instructions to use jonraza15/learb_hf_food_text_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonraza15/learb_hf_food_text_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonraza15/learb_hf_food_text_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonraza15/learb_hf_food_text_classifier") model = AutoModelForSequenceClassification.from_pretrained("jonraza15/learb_hf_food_text_classifier") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learb_hf_food_text_classifier
results: []
learb_hf_food_text_classifier
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0005
- Accuracy: 1.0
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4454 | 1.0 | 7 | 0.1451 | 1.0 |
| 0.1091 | 2.0 | 14 | 0.0119 | 1.0 |
| 0.0087 | 3.0 | 21 | 0.0029 | 1.0 |
| 0.0025 | 4.0 | 28 | 0.0014 | 1.0 |
| 0.0013 | 5.0 | 35 | 0.0009 | 1.0 |
| 0.0009 | 6.0 | 42 | 0.0007 | 1.0 |
| 0.0007 | 7.0 | 49 | 0.0006 | 1.0 |
| 0.0007 | 8.0 | 56 | 0.0005 | 1.0 |
| 0.0006 | 9.0 | 63 | 0.0005 | 1.0 |
| 0.0006 | 10.0 | 70 | 0.0005 | 1.0 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2