nyu-mll/glue
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How to use gokulsrinivasagan/bert_base_train_book_ent_1_inv_sst2 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_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_inv_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_base_train_book_ent_1_inv_sst2")This model is a fine-tuned version of gokulsrinivasagan/bert_base_train_book_ent_1_inv on the GLUE SST2 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 |
|---|---|---|---|---|
| 0.5659 | 1.0 | 264 | 0.4935 | 0.7718 |
| 0.2908 | 2.0 | 528 | 0.4909 | 0.7982 |
| 0.2089 | 3.0 | 792 | 0.5067 | 0.7936 |
| 0.167 | 4.0 | 1056 | 0.5166 | 0.7993 |
| 0.1351 | 5.0 | 1320 | 0.5875 | 0.7936 |
| 0.1114 | 6.0 | 1584 | 0.7650 | 0.7798 |
| 0.093 | 7.0 | 1848 | 0.7186 | 0.7878 |
Base model
distilbert/distilbert-base-uncased