nyu-mll/glue
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How to use gokulsrinivasagan/bert_tiny_lda_book_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/bert_tiny_lda_book_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_lda_book_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/bert_tiny_lda_book_sst2")This model is a fine-tuned version of gokulsrinivasagan/bert_tiny_lda_book 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 |
|---|---|---|---|---|
| 0.3534 | 1.0 | 264 | 0.3482 | 0.8532 |
| 0.2055 | 2.0 | 528 | 0.3371 | 0.8635 |
| 0.1453 | 3.0 | 792 | 0.3587 | 0.8796 |
| 0.1112 | 4.0 | 1056 | 0.3804 | 0.8830 |
| 0.0871 | 5.0 | 1320 | 0.3870 | 0.8670 |
| 0.07 | 6.0 | 1584 | 0.4491 | 0.8716 |
| 0.0558 | 7.0 | 1848 | 0.4381 | 0.8739 |
Base model
gokulsrinivasagan/bert_tiny_lda_book