nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_book_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_book_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_book_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_book_rte")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda_book on the GLUE RTE 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 |
|---|---|---|---|---|
| 0.7037 | 1.0 | 10 | 0.6966 | 0.4693 |
| 0.6896 | 2.0 | 20 | 0.6909 | 0.5090 |
| 0.6785 | 3.0 | 30 | 0.6869 | 0.5487 |
| 0.6576 | 4.0 | 40 | 0.6988 | 0.5487 |
| 0.6326 | 5.0 | 50 | 0.7494 | 0.5523 |
| 0.5793 | 6.0 | 60 | 0.7458 | 0.5343 |
| 0.4963 | 7.0 | 70 | 0.8371 | 0.5307 |
| 0.3952 | 8.0 | 80 | 0.9946 | 0.5126 |
Base model
gokulsrinivasagan/bert_tiny_lda_book