nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_rte 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_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_rte")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete on the GLUE RTE 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 |
|---|---|---|---|---|
| 0.729 | 1.0 | 10 | 0.7027 | 0.4801 |
| 0.6883 | 2.0 | 20 | 0.6912 | 0.5018 |
| 0.6703 | 3.0 | 30 | 0.6878 | 0.5126 |
| 0.6464 | 4.0 | 40 | 0.6963 | 0.5235 |
| 0.6075 | 5.0 | 50 | 0.7279 | 0.5379 |
| 0.5549 | 6.0 | 60 | 0.7688 | 0.5162 |
| 0.4855 | 7.0 | 70 | 0.8285 | 0.5162 |
| 0.4121 | 8.0 | 80 | 0.9034 | 0.5307 |
Base model
google/bert_uncased_L-4_H-512_A-8