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("sciarrilli/distilbert-base-uncased-cola")
model = AutoModelForSequenceClassification.from_pretrained("sciarrilli/distilbert-base-uncased-cola")Quick Links
distilbert-base-uncased-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: 1.2715
- Matthews Correlation: 0.5301
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5216 | 1.0 | 535 | 0.5124 | 0.4104 |
| 0.3456 | 2.0 | 1070 | 0.5700 | 0.4692 |
| 0.2362 | 3.0 | 1605 | 0.7277 | 0.4844 |
| 0.1818 | 4.0 | 2140 | 0.7553 | 0.5007 |
| 0.1509 | 5.0 | 2675 | 0.9406 | 0.4987 |
| 0.1017 | 6.0 | 3210 | 0.9475 | 0.5387 |
| 0.0854 | 7.0 | 3745 | 1.0933 | 0.5317 |
| 0.051 | 8.0 | 4280 | 1.1719 | 0.5358 |
| 0.0512 | 9.0 | 4815 | 1.2296 | 0.5321 |
| 0.0308 | 10.0 | 5350 | 1.2715 | 0.5301 |
Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
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Dataset used to train sciarrilli/distilbert-base-uncased-cola
Evaluation results
- Matthews Correlation on glueself-reported0.530
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sciarrilli/distilbert-base-uncased-cola")