nyu-mll/glue
Viewer • Updated • 1.49M • 458k • 495
How to use JeremiahZ/roberta-base-sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-sst2")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-sst2")This model is a fine-tuned version of roberta-base on the GLUE SST2 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 |
|---|---|---|---|---|
| 0.2287 | 1.0 | 4210 | 0.2314 | 0.9358 |
| 0.1959 | 2.0 | 8420 | 0.3027 | 0.9266 |
| 0.1635 | 3.0 | 12630 | 0.3022 | 0.9300 |
| 0.1148 | 4.0 | 16840 | 0.3162 | 0.9289 |