nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_2_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_2_inv_mrpc") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_inv_mrpc")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_2_inv_mrpc", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_2_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 |
|---|---|---|---|---|---|---|
| 0.652 | 1.0 | 15 | 0.6135 | 0.6838 | 0.8083 | 0.7461 |
| 0.6082 | 2.0 | 30 | 0.6168 | 0.6691 | 0.7854 | 0.7272 |
| 0.5719 | 3.0 | 45 | 0.6200 | 0.6887 | 0.8049 | 0.7468 |
| 0.5222 | 4.0 | 60 | 0.6556 | 0.6422 | 0.7402 | 0.6912 |
| 0.4375 | 5.0 | 75 | 0.7900 | 0.5980 | 0.6858 | 0.6419 |
| 0.3398 | 6.0 | 90 | 0.7928 | 0.6618 | 0.7596 | 0.7107 |
Base model
distilbert/distilbert-base-uncased