Text Classification
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distilbert
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Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_10_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_10_v1_cola")Quick Links
bert_base_rand_10_v1_cola
This model is a fine-tuned version of Hartunka/bert_base_rand_10_v1 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6168
- Matthews Correlation: 0.0
- Accuracy: 0.6913
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6138 | 1.0 | 34 | 0.6168 | 0.0 | 0.6913 |
| 0.5908 | 2.0 | 68 | 0.6231 | 0.0687 | 0.6874 |
| 0.5407 | 3.0 | 102 | 0.6428 | 0.1314 | 0.6779 |
| 0.4904 | 4.0 | 136 | 0.7110 | 0.1287 | 0.6366 |
| 0.4418 | 5.0 | 170 | 0.6772 | 0.1075 | 0.6692 |
| 0.3958 | 6.0 | 204 | 0.8237 | 0.0752 | 0.6472 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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Model tree for Hartunka/bert_base_rand_10_v1_cola
Base model
Hartunka/bert_base_rand_10_v1Dataset used to train Hartunka/bert_base_rand_10_v1_cola
Evaluation results
- Matthews Correlation on GLUE COLAself-reported0.000
- Accuracy on GLUE COLAself-reported0.691
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_10_v1_cola")