nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_train_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_train_stsb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_train_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_train_stsb", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_train on the GLUE STSB 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 | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 3.1767 | 1.0 | 23 | 2.6928 | 0.0748 | 0.0664 | 0.0706 |
| 2.0145 | 2.0 | 46 | 2.7238 | 0.1395 | 0.1108 | 0.1252 |
| 1.8126 | 3.0 | 69 | 2.2746 | 0.2493 | 0.2392 | 0.2443 |
| 1.5711 | 4.0 | 92 | 2.3210 | 0.3326 | 0.3285 | 0.3305 |
| 1.2158 | 5.0 | 115 | 2.1816 | 0.3827 | 0.3756 | 0.3792 |
| 0.9702 | 6.0 | 138 | 2.2613 | 0.3981 | 0.3928 | 0.3954 |
| 0.7377 | 7.0 | 161 | 2.2930 | 0.4208 | 0.4190 | 0.4199 |
| 0.5823 | 8.0 | 184 | 2.3649 | 0.4391 | 0.4378 | 0.4384 |
| 0.518 | 9.0 | 207 | 2.2171 | 0.4238 | 0.4212 | 0.4225 |
| 0.4424 | 10.0 | 230 | 2.2883 | 0.4315 | 0.4289 | 0.4302 |
Base model
distilbert/distilbert-base-uncased