nyu-mll/glue
Viewer • Updated • 1.49M • 425k • 509
How to use dinhlnd1610/distilbert-base-uncased-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dinhlnd1610/distilbert-base-uncased-finetuned-sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dinhlnd1610/distilbert-base-uncased-finetuned-sst2")
model = AutoModelForSequenceClassification.from_pretrained("dinhlnd1610/distilbert-base-uncased-finetuned-sst2")This model is a fine-tuned version of distilbert-base-uncased on the glue 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 | Accuracy |
|---|---|---|---|---|
| 0.0367 | 1.0 | 4210 | 0.5104 | 0.8968 |
| 0.0287 | 2.0 | 8420 | 0.6466 | 0.9002 |
| 0.0372 | 3.0 | 12630 | 0.5988 | 0.9014 |
| 0.028 | 4.0 | 16840 | 0.6600 | 0.9037 |
| 0.0111 | 5.0 | 21050 | 0.8030 | 0.9014 |