Text Classification
Transformers
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bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Apucs/bert-fine-tuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("Apucs/bert-fine-tuned-cola")Quick Links
bert-fine-tuned-cola
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.8483
- Matthews Correlation: 0.5731
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.4485 | 1.0 | 1069 | 0.4392 | 0.5550 |
| 0.3059 | 2.0 | 2138 | 0.6730 | 0.5576 |
| 0.1866 | 3.0 | 3207 | 0.8483 | 0.5731 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for Apucs/bert-fine-tuned-cola
Base model
google-bert/bert-base-casedDataset used to train Apucs/bert-fine-tuned-cola
Evaluation results
- Matthews Correlation on gluevalidation set self-reported0.573
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apucs/bert-fine-tuned-cola")