nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_qnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_qnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_qnli")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_qnli", device_map="auto")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete on the GLUE QNLI 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 |
|---|---|---|---|---|
| 0.5045 | 1.0 | 410 | 0.4113 | 0.8191 |
| 0.4154 | 2.0 | 820 | 0.3766 | 0.8387 |
| 0.3644 | 3.0 | 1230 | 0.3786 | 0.8332 |
| 0.3184 | 4.0 | 1640 | 0.3905 | 0.8378 |
| 0.2723 | 5.0 | 2050 | 0.4453 | 0.8223 |
| 0.2342 | 6.0 | 2460 | 0.4462 | 0.8351 |
| 0.2021 | 7.0 | 2870 | 0.4562 | 0.8334 |
Base model
google/bert_uncased_L-4_H-512_A-8