nyu-mll/glue
Viewer • Updated • 1.49M • 388k • 516
How to use Hartunka/tiny_bert_rand_100_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_100_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_100_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_100_v2_cola")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_100_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_100_v2_cola")This model is a fine-tuned version of Hartunka/tiny_bert_rand_100_v2 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6142 | 1.0 | 34 | 0.6166 | 0.0 | 0.6913 |
| 0.6003 | 2.0 | 68 | 0.6185 | 0.0 | 0.6913 |
| 0.5794 | 3.0 | 102 | 0.6215 | 0.0179 | 0.6874 |
| 0.5396 | 4.0 | 136 | 0.6724 | 0.0597 | 0.6711 |
| 0.4923 | 5.0 | 170 | 0.6689 | 0.0786 | 0.6453 |
| 0.4541 | 6.0 | 204 | 0.7376 | 0.0804 | 0.6261 |
Base model
Hartunka/tiny_bert_rand_100_v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_100_v2_cola")