Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use orcalewang/samwang_hw001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use orcalewang/samwang_hw001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orcalewang/samwang_hw001")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("orcalewang/samwang_hw001") model = AutoModelForSequenceClassification.from_pretrained("orcalewang/samwang_hw001") - Notebooks
- Google Colab
- Kaggle
samwang_hw001
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: 0.4649
- Matthews Correlation: 0.5435
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 |
|---|---|---|---|---|
| 0.5196 | 1.0 | 535 | 0.4556 | 0.4550 |
| 0.3454 | 2.0 | 1070 | 0.4649 | 0.5435 |
| 0.2375 | 3.0 | 1605 | 0.6452 | 0.4915 |
| 0.1665 | 4.0 | 2140 | 0.7487 | 0.5422 |
| 0.1211 | 5.0 | 2675 | 0.8620 | 0.5390 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Model tree for orcalewang/samwang_hw001
Base model
distilbert/distilbert-base-uncased