nyu-mll/glue
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How to use gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_mrpc 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_book_mrpc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_mrpc")
model = AutoModelForSequenceClassification.from_pretrained("gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book_mrpc")This model is a fine-tuned version of gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_book 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 |
|---|---|---|---|---|---|---|
| 0.6257 | 1.0 | 15 | 0.6032 | 0.6961 | 0.8086 | 0.7524 |
| 0.584 | 2.0 | 30 | 0.5825 | 0.7010 | 0.8111 | 0.7561 |
| 0.5483 | 3.0 | 45 | 0.6029 | 0.7059 | 0.8171 | 0.7615 |
| 0.5131 | 4.0 | 60 | 0.5927 | 0.6863 | 0.7808 | 0.7335 |
| 0.4597 | 5.0 | 75 | 0.6270 | 0.6985 | 0.7897 | 0.7441 |
| 0.3832 | 6.0 | 90 | 0.6773 | 0.7034 | 0.7987 | 0.7511 |
| 0.3111 | 7.0 | 105 | 0.7539 | 0.7083 | 0.8096 | 0.7590 |
Base model
google/bert_uncased_L-4_H-512_A-8