nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_1_mnli 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_1_mnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_mnli")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_mnli")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_1 on the GLUE MNLI 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 |
|---|---|---|---|---|
| 1.0644 | 1.0 | 1534 | 1.0033 | 0.4955 |
| 0.9624 | 2.0 | 3068 | 0.9277 | 0.5507 |
| 0.8975 | 3.0 | 4602 | 0.8896 | 0.5864 |
| 0.8483 | 4.0 | 6136 | 0.8781 | 0.5998 |
| 0.8033 | 5.0 | 7670 | 0.8751 | 0.6031 |
| 0.7583 | 6.0 | 9204 | 0.8797 | 0.6115 |
| 0.7114 | 7.0 | 10738 | 0.8902 | 0.6109 |
| 0.6627 | 8.0 | 12272 | 0.9312 | 0.6084 |
| 0.6116 | 9.0 | 13806 | 0.9755 | 0.6116 |
| 0.5605 | 10.0 | 15340 | 1.0309 | 0.6028 |
Base model
distilbert/distilbert-base-uncased