nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_50_v1_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_50_v1_rte") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_50_v1_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_50_v1_rte", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda_50_v1 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 |
|---|---|---|---|---|
| 0.7102 | 1.0 | 10 | 0.7030 | 0.4513 |
| 0.6931 | 2.0 | 20 | 0.6947 | 0.5162 |
| 0.684 | 3.0 | 30 | 0.6952 | 0.5596 |
| 0.6672 | 4.0 | 40 | 0.6998 | 0.5415 |
| 0.6337 | 5.0 | 50 | 0.7336 | 0.5162 |
| 0.5785 | 6.0 | 60 | 0.8523 | 0.4693 |
| 0.5141 | 7.0 | 70 | 0.8520 | 0.5415 |
Base model
gokulsrinivasagan/bert_tiny_lda_50_v1