Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use bernardlkb/AI_MODEL_20241002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bernardlkb/AI_MODEL_20241002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bernardlkb/AI_MODEL_20241002")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bernardlkb/AI_MODEL_20241002") model = AutoModelForSequenceClassification.from_pretrained("bernardlkb/AI_MODEL_20241002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
AI_MODEL_20241002
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6105
- Matthews Correlation: 0.5206
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: 2.073873291463896e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5282 | 1.0 | 535 | 0.4923 | 0.4440 |
| 0.3385 | 2.0 | 1070 | 0.5094 | 0.5101 |
| 0.2344 | 3.0 | 1605 | 0.6105 | 0.5206 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
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Model tree for bernardlkb/AI_MODEL_20241002
Base model
distilbert/distilbert-base-uncased