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_20_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_20_v2_cola")Quick Links
bert_base_rand_20_v2_cola
This model is a fine-tuned version of Hartunka/bert_base_rand_20_v2 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6169
- 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 |
|---|---|---|---|---|---|
| 0.6147 | 1.0 | 34 | 0.6169 | 0.0 | 0.6913 |
| 0.5912 | 2.0 | 68 | 0.6169 | 0.0302 | 0.6855 |
| 0.5445 | 3.0 | 102 | 0.6372 | 0.0965 | 0.6616 |
| 0.4913 | 4.0 | 136 | 0.7028 | 0.0746 | 0.6577 |
| 0.434 | 5.0 | 170 | 0.7017 | 0.0952 | 0.6453 |
| 0.3888 | 6.0 | 204 | 0.7899 | 0.0734 | 0.6337 |
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_20_v2_cola
Base model
Hartunka/bert_base_rand_20_v2Dataset used to train Hartunka/bert_base_rand_20_v2_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_20_v2_cola")