nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_train_wnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_train_wnli") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_wnli")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_train_wnli", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_train on the GLUE WNLI 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 |
|---|---|---|---|---|
| 0.71 | 1.0 | 3 | 0.7326 | 0.2676 |
| 0.6971 | 2.0 | 6 | 0.7371 | 0.4366 |
| 0.6901 | 3.0 | 9 | 0.7586 | 0.2394 |
| 0.6811 | 4.0 | 12 | 0.7801 | 0.1549 |
| 0.6864 | 5.0 | 15 | 0.7982 | 0.1690 |
| 0.6792 | 6.0 | 18 | 0.8183 | 0.1690 |
Base model
distilbert/distilbert-base-uncased