nyu-mll/glue
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How to use Hartunka/bert_base_km_100_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/bert_base_km_100_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_100_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_km_100_v1_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_100_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_km_100_v1_cola")This model is a fine-tuned version of Hartunka/bert_base_km_100_v1 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6167 | 1.0 | 34 | 0.6237 | 0.0 | 0.6913 |
| 0.5951 | 2.0 | 68 | 0.6253 | 0.0126 | 0.6894 |
| 0.5382 | 3.0 | 102 | 0.6292 | 0.1000 | 0.6711 |
| 0.456 | 4.0 | 136 | 0.6964 | 0.1330 | 0.6491 |
| 0.3717 | 5.0 | 170 | 0.7490 | 0.0652 | 0.6299 |
| 0.298 | 6.0 | 204 | 0.9227 | 0.0768 | 0.6424 |
Base model
Hartunka/bert_base_km_100_v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_km_100_v1_cola")