nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_1_inv_mrpc 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_inv_mrpc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_inv_mrpc")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_inv_mrpc")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_1_inv on the GLUE MRPC 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 | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.6393 | 1.0 | 15 | 0.6167 | 0.6912 | 0.8131 | 0.7521 |
| 0.6068 | 2.0 | 30 | 0.6069 | 0.6912 | 0.8091 | 0.7501 |
| 0.5588 | 3.0 | 45 | 0.6247 | 0.6961 | 0.8127 | 0.7544 |
| 0.5126 | 4.0 | 60 | 0.6573 | 0.6471 | 0.7517 | 0.6994 |
| 0.4325 | 5.0 | 75 | 0.6867 | 0.6667 | 0.7710 | 0.7189 |
| 0.3219 | 6.0 | 90 | 0.8301 | 0.6348 | 0.7246 | 0.6797 |
| 0.2381 | 7.0 | 105 | 0.9458 | 0.6593 | 0.7608 | 0.7100 |
Base model
distilbert/distilbert-base-uncased