Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use charlemagne/distilbert-base-uncased-new2-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use charlemagne/distilbert-base-uncased-new2-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="charlemagne/distilbert-base-uncased-new2-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("charlemagne/distilbert-base-uncased-new2-cola") model = AutoModelForSequenceClassification.from_pretrained("charlemagne/distilbert-base-uncased-new2-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-new2-cola
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2107
- Matthews Correlation: 0.9155
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 164 | 0.4352 | 0.8059 |
| No log | 2.0 | 328 | 0.2626 | 0.8950 |
| No log | 3.0 | 492 | 0.2422 | 0.9063 |
| 0.4552 | 4.0 | 656 | 0.2107 | 0.9155 |
| 0.4552 | 5.0 | 820 | 0.2160 | 0.9134 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.8.0+cu111
- Datasets 2.1.0
- Tokenizers 0.11.6
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