nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_2_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_base_train_book_ent_2_rte") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_rte", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_2 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 |
|---|---|---|---|---|
| 0.7063 | 1.0 | 10 | 0.6947 | 0.5054 |
| 0.6906 | 2.0 | 20 | 0.7085 | 0.4801 |
| 0.6768 | 3.0 | 30 | 0.7321 | 0.4801 |
| 0.6506 | 4.0 | 40 | 0.7425 | 0.5090 |
| 0.6027 | 5.0 | 50 | 0.8085 | 0.4513 |
| 0.5494 | 6.0 | 60 | 0.8785 | 0.4946 |
Base model
distilbert/distilbert-base-uncased