nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_stsb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_stsb", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda 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 |
|---|---|---|---|---|---|---|
| 2.9116 | 1.0 | 23 | 2.5819 | 0.0325 | 0.0447 | 0.0386 |
| 2.0708 | 2.0 | 46 | 2.8471 | 0.0523 | 0.0570 | 0.0547 |
| 1.9775 | 3.0 | 69 | 2.7257 | 0.0836 | 0.0864 | 0.0850 |
| 1.8462 | 4.0 | 92 | 2.6186 | 0.1403 | 0.1391 | 0.1397 |
| 1.6662 | 5.0 | 115 | 3.0109 | 0.1573 | 0.1506 | 0.1539 |
| 1.4643 | 6.0 | 138 | 3.1074 | 0.1873 | 0.1981 | 0.1927 |
Base model
gokulsrinivasagan/bert_tiny_lda