nyu-mll/glue
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How to use gokulsrinivasagan/distilbert_base_train_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/distilbert_base_train_sst2") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/distilbert_base_train_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/distilbert_base_train_sst2", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/distilbert_base_train on the GLUE SST2 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 |
|---|---|---|---|---|
| 0.355 | 1.0 | 264 | 0.4462 | 0.8154 |
| 0.1983 | 2.0 | 528 | 0.4407 | 0.8222 |
| 0.1397 | 3.0 | 792 | 0.5094 | 0.8154 |
| 0.1059 | 4.0 | 1056 | 0.5862 | 0.8200 |
| 0.0835 | 5.0 | 1320 | 0.5946 | 0.8108 |
| 0.0668 | 6.0 | 1584 | 0.7749 | 0.8050 |
| 0.0539 | 7.0 | 1848 | 0.6839 | 0.8177 |
Base model
distilbert/distilbert-base-uncased