Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tianyisun/distilbert-base-uncased-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("tianyisun/distilbert-base-uncased-finetuned-cola")Quick Links
distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.8231
- Matthews Correlation: 0.5640
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
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 |
|---|---|---|---|---|
| 0.5249 | 1.0 | 535 | 0.4499 | 0.5117 |
| 0.3431 | 2.0 | 1070 | 0.4726 | 0.5515 |
| 0.2262 | 3.0 | 1605 | 0.6070 | 0.5330 |
| 0.1632 | 4.0 | 2140 | 0.7148 | 0.5556 |
| 0.1318 | 5.0 | 2675 | 0.8231 | 0.5640 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.11.0
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Model tree for tianyisun/distilbert-base-uncased-finetuned-cola
Base model
distilbert/distilbert-base-uncasedDataset used to train tianyisun/distilbert-base-uncased-finetuned-cola
Evaluation results
- Matthews Correlation on glueself-reported0.564
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tianyisun/distilbert-base-uncased-finetuned-cola")