nyu-mll/glue
Viewer โข Updated โข 1.49M โข 656k โข 785
How to use gokulsrinivasagan/distilbert_base_train_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/distilbert_base_train_stsb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/distilbert_base_train_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/distilbert_base_train_stsb", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/distilbert_base_train on the GLUE STSB 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 | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 2.611 | 1.0 | 23 | 2.2757 | 0.2247 | 0.2160 | 0.2204 |
| 1.6387 | 2.0 | 46 | 2.3884 | 0.3814 | 0.3601 | 0.3707 |
| 1.2188 | 3.0 | 69 | 1.8166 | 0.4864 | 0.4818 | 0.4841 |
| 0.9041 | 4.0 | 92 | 2.0495 | 0.4651 | 0.4567 | 0.4609 |
| 0.7104 | 5.0 | 115 | 2.3667 | 0.4607 | 0.4534 | 0.4570 |
| 0.5228 | 6.0 | 138 | 2.1243 | 0.4707 | 0.4605 | 0.4656 |
| 0.4702 | 7.0 | 161 | 1.9230 | 0.5023 | 0.4981 | 0.5002 |
| 0.3472 | 8.0 | 184 | 1.9116 | 0.4898 | 0.4795 | 0.4846 |
Base model
distilbert/distilbert-base-uncased