Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use AIA-tclin/hw-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIA-tclin/hw-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIA-tclin/hw-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIA-tclin/hw-1") model = AutoModelForSequenceClassification.from_pretrained("AIA-tclin/hw-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hw-1
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: 0.8330
- Matthews Correlation: 0.5321
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5177 | 1.0 | 535 | 0.4552 | 0.4537 |
| 0.3422 | 2.0 | 1070 | 0.4733 | 0.5083 |
| 0.2322 | 3.0 | 1605 | 0.6302 | 0.5100 |
| 0.1723 | 4.0 | 2140 | 0.7842 | 0.5318 |
| 0.1294 | 5.0 | 2675 | 0.8330 | 0.5321 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for AIA-tclin/hw-1
Base model
distilbert/distilbert-base-uncased