nyu-mll/glue
Viewer โข Updated โข 1.49M โข 662k โข 783
How to use gokulsrinivasagan/tinybert_train_book_ent_15p_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_train_book_ent_15p_cola") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_train_book_ent_15p_cola", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_train_book_ent_15p 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 |
|---|---|---|---|---|---|
| 0.614 | 1.0 | 34 | 0.6174 | 0.0 | 0.6913 |
| 0.6059 | 2.0 | 68 | 0.6166 | 0.0 | 0.6913 |
| 0.5899 | 3.0 | 102 | 0.6150 | 0.0584 | 0.6874 |
| 0.5635 | 4.0 | 136 | 0.6511 | 0.0376 | 0.6855 |
| 0.5165 | 5.0 | 170 | 0.6492 | 0.0928 | 0.6520 |
| 0.4734 | 6.0 | 204 | 0.6968 | 0.0858 | 0.6500 |
| 0.4402 | 7.0 | 238 | 0.7428 | 0.0720 | 0.6510 |
| 0.4064 | 8.0 | 272 | 0.7450 | 0.0845 | 0.6146 |
Base model
distilbert/distilbert-base-uncased