nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_15p_s_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_15p_s_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_s_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_15p_s_rte", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_15p_s 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 |
|---|---|---|---|---|
| 0.7164 | 1.0 | 10 | 0.6922 | 0.5271 |
| 0.7045 | 2.0 | 20 | 0.6981 | 0.5271 |
| 0.7048 | 3.0 | 30 | 0.6972 | 0.4729 |
| 0.6976 | 4.0 | 40 | 0.7002 | 0.4729 |
| 0.6957 | 5.0 | 50 | 0.6935 | 0.5271 |
| 0.6978 | 6.0 | 60 | 0.6960 | 0.4729 |
Base model
google-bert/bert-base-uncased