Transformers course
Collection
Collection for the UU transformers course. โข 9 items โข Updated
How to use kbberendsen/deberta-v3-base-hyper-finetuned-sst2-midterm with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="kbberendsen/deberta-v3-base-hyper-finetuned-sst2-midterm") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kbberendsen/deberta-v3-base-hyper-finetuned-sst2-midterm")
model = AutoModelForSequenceClassification.from_pretrained("kbberendsen/deberta-v3-base-hyper-finetuned-sst2-midterm", device_map="auto")This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown 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 | Accuracy |
|---|---|---|---|---|
| 0.2085 | 1.0 | 8419 | 0.2503 | 0.9392 |
| 0.1458 | 2.0 | 16838 | 0.2837 | 0.9381 |
| 0.1121 | 3.0 | 25257 | 0.2857 | 0.9415 |
| 0.0881 | 4.0 | 33676 | 0.2589 | 0.9381 |
| 0.0423 | 5.0 | 42095 | 0.3403 | 0.9415 |
Base model
microsoft/deberta-v3-base